To see the other types of publications on this topic, follow the link: Process control Dynamics Mathematical models.
Journal articles on the topic 'Process control Dynamics Mathematical models'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Process control Dynamics Mathematical models.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Vinayak Kishan Nirmale. "Mathematical Models for Infectious Disease Dynamics and Control Strategies." Communications on Applied Nonlinear Analysis 32, no. 1s (2024): 54–62. http://dx.doi.org/10.52783/cana.v32.2101.
Full textAbstract:
The study explores mathematical models related to the dynamics of infectious diseases and approaches to manage them. These models are essential resources for comprehending how illnesses spread throughout people and assessing how well control strategies work. The study of infectious disease epidemiology heavily relies on mathematical modeling and analysis. Here, we give a clear overview of the spread of disease, explain how to mathematically model this stochastic process, and show how to utilize this mathematical representation to analyze the emergent dynamics of real-world epidemics. The advancement of mathematical analysis and modeling is crucial to our expanding comprehension of the evolution and ecology of pathogens. This study emphasizes how important mathematical modeling is to improving our knowledge of the dynamics of infectious diseases and how crucial it is to creating efficient management plans to lessen the burden of infectious diseases on public health.
2
N. Sridhar, Lakshmi. "Dynamics of Leukemia Models." International Journal of Clinical Case Reports and Reviews 27, no. 03 (2025): 01–11. https://doi.org/10.31579/2690-4861/860.
Full textAbstract:
Millions of people are affected by leukemia. It is important to understand the progression dynamics of this disease to be able to minimize the damage that is caused by it. This article provides a mathematical framework to develop strategies to control leukemia. Bifurcation analysis is a powerful mathematical tool used to deal with the nonlinear dynamics of any process. Several factors must be considered, and multiple objectives must be met simultaneously. Bifurcation analysis and multiobjective nonlinear model predictive control (MNLMPC) calculations are performed on three leukemia models. The MATLAB program MATCONT was used to perform the bifurcation analysis. The MNLMPC calculations were performed using the optimization language PYOMO in conjunction with the state-of-the-art global optimization solvers IPOPT and BARON. The bifurcation analysis revealed the existence of limit points and branch in the models. The limit and branch points were beneficial because they enabled the multiobjective nonlinear model predictive control calculations to converge to the Utopia point in both problems, which is the most beneficial solution. A combination of bifurcation analysis and multiobjective nonlinear model predictive control for leukemia models is the main contribution of this paper.
3
Park, Joseph, George Sugihara, and Gerald Pao. "Control of complex systems with generalized embedding and empirical dynamic modeling." PLOS ONE 19, no. 8 (2024): e0305408. http://dx.doi.org/10.1371/journal.pone.0305408.
Full textAbstract:
Effective control requires knowledge of the process dynamics to guide the system toward desired states. In many control applications this knowledge is expressed mathematically or through data–driven models, however, as complexity grows obtaining a satisfactory mathematical representation is increasingly difficult. Further, many data–driven approaches consist of abstract internal representations that may have no obvious connection to the underlying dynamics and control, or, require extensive model design and training. Here, we remove these constraints by demonstrating model predictive control from generalized state space embedding of the process dynamics providing a data–driven, explainable method for control of nonlinear, complex systems. Generalized embedding and model predictive control are demonstrated on nonlinear dynamics generated by an agent based model of 1200 interacting agents. The method is generally applicable to any type of controller and dynamic system representable in a state space.
4
Prof.Divya, K.Pai. "Mathematical Model of Chemical Reactor Plant and Heat Exchanger Dynamics with Control Design Techniques." Journal of Instrumentation and Innovation Sciences 3, no. 3 (2018): 21–29. https://doi.org/10.5281/zenodo.1480812.
Full textAbstract:
<em>Mathematical Models are sets of equations that describe a process or System. All engineering programs need Mathematical model before real time implementation of any system, to determine how the process reacts to numerous inputs. Although the bodily gadget is available for experimentation the process usually may be very high priced. Mathematical modeling of a chemical reactor is acquired via the material balance equations and using differential equations, Laplace transforms, Linearizing the Non-Liner equations via Taylor series, obtainingthe transfer function models and finally the design of controller by Internal model principle.Robustness of the control design is verified through model mismatch. Modelling of heat exchanger dynamics is acquired by using the measured statistics and manipulate layout by using Feedforward+remarks control.</em>
5
ZAVADSKY, SERGEY V., DMITRI A. OVSYANNIKOV, and SHENG-LUEN CHUNG. "PARAMETRIC OPTIMIZATION METHODS FOR THE TOKAMAK PLASMA CONTROL PROBLEM." International Journal of Modern Physics A 24, no. 05 (2009): 1040–47. http://dx.doi.org/10.1142/s0217751x09044486.
Full textAbstract:
Mathematical models of the structural parametric optimization of plasma dynamics are discussed. Optimization approach to plasma dynamic is based on the consideration of trajectory ensemble. This ensemble describes transient process in tokamak subject to the initial data and external disturbances. In the framework of this approach the optimization of dynamics of the trajectory ensemble in ITER tokamak is given. The trajectories of this ensemble are perturbed at the initial point set and the set of external disturbances.
6
Jannatun, Irana Ira, Shahidul Islam Md., C. Misra J, and Kamrujjaman Md. "Mathematical Modelling of the Dynamics of Tumor Growth and its Optimal Control." International Journal of Ground Sediment & Water 2020, no. 11 (2020): 659–79. https://doi.org/10.5281/zenodo.4275629.
Full textAbstract:
<strong>Abstract: </strong>In the last few decades, the dynamics of tumor cells and their growths are presented via clinical, experimental, and theoretical approaches, which leads to the development of the new idea of multiple cancer therapies to control and reduce the death rate for earlier detection. In this paper, we discussed the dynamics of tumor cell growth and its treatment process. We analyzed some simple mathematical models and generalized the study to understand the growth of tumor cells. The main proposed model is a system of ordinary differential equations which combines interactions among natural killer cells, dendritic cells and cytotoxic CD8+T cells. The model is solved numerically to explain how the tumor cells spread and become more dangerous as well as the treatment process of cancer. It is also studied that how the cell behaves in the presence of different therapy and drugs. The optimal control of chemotherapy has been discussed. It has also been explained how much the model is effective in reducing tumor cells over time. Finally, a couple of spatially distributed models are discussed for tumor cell growth. <strong>Keywords: </strong>Mathematical models; tumor growth; chemotherapy; di usion; optimal control © International Journal of Ground Sediment & Water, ©Sun Jichao. The website is http://ijgsw.comze.com/
7
N Sridhar, Lakshmi. "Dynamics of Chemoimmunotherapy Models." Journal of Innovative Clinical Trials and Case Reports 1, no. 1 (2025): 1–10. https://doi.org/10.63721/25jctc0106.
Full textAbstract:
Chemoimmunotherapy is chemotherapy combined with immunotherapy. Chemotherapy uses different drugs to kill or slow the growth of cancer cells; immunotherapy uses treatments to stimulate or restore the ability of the immune system to fight cancer. Both chemotherapy and immunotherapy are highly nonlinear processes that several factors affect. The two treatments together would be very highly nonlinear. It is necessary to understand and control this combined treatment. Bifurcation analysis is a powerful mathematical tool used to deal with the nonlinear dynamics of any process. Several factors must be considered, and multiple objectives must be met simultaneously. Bifurcation analysis and multiobjective nonlinear model predictive control (MNLMPC) calculations are performed on two chemoimmunotherapy models. The MATLAB program MATCONT was used to perform the bifurcation analysis. The MNLMPC calculations were performed using the optimization language PYOMO in conjunction with the state-of-the-art global optimization solvers IPOPT and BARON.. The bifurcation analysis revealed branch and limit points in the two models. The branch and limit points were beneficial because they enabled the multiobjective nonlinear model predictive control calculations to converge to the Utopia point in both the problems, which is the best solution
8
Barrios Sánchez, Jorge Manuel, Roberto Baeza Serrato, and Marco Bianchetti. "Design and Development of a Mathematical Model for an Industrial Process, in a System Dynamics Environment." Applied Sciences 12, no. 19 (2022): 9855. http://dx.doi.org/10.3390/app12199855.
Full textAbstract:
This research proposes a methodology based on control engineering, transforming the simulation model of system dynamics into a mathematical model expressed as a system transfer function. The differential equations of a time domain present in the Forrester diagram are transformed into a frequency domain based on the Laplace transform. The conventional control engineering technique is used to present and reduce the dynamic system in a block diagram as a mechanism for determining the structure of the system. The direct path equation and the feedback equation are determined to obtain mathematical models that explain the trajectory of the behavior of each state variable through a transfer function in response to the different inputs of the system. The research proposal is based on presenting an alternative of analytical validation for more robust decision-making to systems dynamics models, based on the explanation of the system structure through a transfer function and its analysis of stability and external controllability for the system dynamics model under study. The results are visually analyzed in a root diagram.
9
Kochuk, Serhii, Dinh Dong Nguyen, Artem Nikitin, and Rafael Trujillo Torres. "Identification of UAV model parameters from flight and computer experiment data." Aerospace technic and technology, no. 6 (November 29, 2021): 12–22. http://dx.doi.org/10.32620/aktt.2021.6.02.
Full textAbstract:
The object of research in the article is various well-known approaches and methods of structural and parametric identification of dynamic controlled objects - unmanned aerial vehicles (UAVs). The subject of the research is the parameters of linear and nonlinear mathematical models of spatial and isolated movements, describing the dynamics and aerodynamic properties of the UAV and obtained both from the results of flight experiments and using computer object-oriented programs for 3-D UAV models. The goal is to obtain mathematical models of UAV flight dynamics in the form of differential equations or transfer functions, check them for reliability and the possibility of using them in problems of synthesis of algorithms for automatic control systems of UAVs. Tasks to be solved: evaluation of the analytical (parametric), direct (transient), as well as the identification method using the 3-D model of the control object. Methods used structural and parametric identification of dynamic objects; the determination of static and dynamic characteristics of mathematical models by the type of their transient process; the System Identification Toolbox package of the MatLab environment, the Flow Simulation subsystem of the SolidWorks software and the X-Plane software environment. The experimental parameters of UAV flights, as well as the results of modeling in three-dimensional environments, are the initial data for the identification of mathematical models. The following results were obtained: the possibility of analytical and computer identification of mathematical models by highly noisy parameters of the UAV flight was shown; the mathematical models of UAVs obtained after identification is reliable and adequately reproduce the dynamics of a real object. A comparative analysis of the considered UAV identification methods is conducted, their performance and efficiency are confirmed. Conclusions. The scientific novelty of the result obtained is as follows: good convergence, reliability and the possibility of using the considered identification methods for obtaining mathematical models of dynamic objects to synthesize algorithms for automatic control systems of UAVs is shown.
10
Jia, Xiao Yi, Yu Tian Lin, Hui Bin Lin, Ling Gao, Jian Qun Lin, and Jian Qiang Lin. "Mathematical Modeling of CSTR Bioreactor Control for Production of Recombinant Protein." Advanced Materials Research 894 (February 2014): 311–15. http://dx.doi.org/10.4028/www.scientific.net/amr.894.311.
Full textAbstract:
Fermentation process using recombinant strain for production of recombinant protein is widely used in commercialization of the biotechnologies. The continuous stirred tank reactor (CSTR) is a typical microbial cultivation method, has the major advantage of high productivity. Mathematical modeling and simulation is useful for analysis and optimization of the CSTR fermentation process. Most of the mathematical models developed for CSTR are black box models without information of the intracellular dynamics and regulations. In this research, a mathematical model is built based on gene regulation for recombinant protein production using CSTR, and simulation is made using this model.
11
Al Salaimeh, Safwan. "MATHEMATICAL MODELS FOR COMPUTERIZED CONTROL SYSTEM." Gulustan-Black Sea Scientific Journal of Academic Research 48, no. 05 (2019): 119–23. http://dx.doi.org/10.36962/gbssjar119.
Full textAbstract:
The software is a set of mathematical methods, and algorithms of information processing, which used in creating the control system. When designing control systems, Initial data for the design of control system. The tasks of the computerized control system are understood as a part of the computerized functions of the computerized control system characterized by the outcomes and outputs in specific form. control function is: commutative action for computerized control system, aimed to achieve a criterion goal. Depending on the properties of the process and their mathematical description can be combined into different classes; This paper shows the designing the mathematical models which need to computerized control systems (models (3) – (8)). In the same time this paper shows the main methods which were used to formulate the mathematical models as: • Stochastic and deterministic; • One dimensional and multidimensional; • Linear and nonlinear; • Static and dynamic; • Stationary and non – stationary; • With distributed and lumped parameters.
12
Sridhar, Lakshmi N. "Dynamics of Chemotherapy Models." Journal of Innovative Clinical Trials and Case Reports 1, no. 1 (2025): 1–9. https://doi.org/10.63721/25jctc0105.
Full textAbstract:
Chemotherapy is a drug treatment that uses powerful chemicals to kill fast-growing cancer cells. Cancer cells grow and multiply much more quickly than most cells in the body, and it is necessary to destroy the cancerous cells to prevent the loss of life. Many different chemotherapy drugs are available. Since cancer cells multiply rapidly, the interaction dynamics between the drugs and the cancer cells need to be understood and controlled. Bifurcation analysis is a powerful mathematical tool used to describe the dynamics of any process. Several factors must be considered, and multiple objectives need to be met simultaneously. Bifurcation analysis and multiobjective nonlinear model predictive control (MNLMPC) calculations were performed on two chemotherapy models. The MATLAB program MATCONT was used to perform the bifurcation analysis. The MNLMPC calculations were performed using the optimization language PYOMO in conjunction with the state-of-the-art global optimization solvers IPOPT and BARON.. The bifurcation analysis revealed branch points in both models. The branch points were beneficial because they enabled the multiobjective nonlinear model predictive control calculations to converge to the Utopia point, which is the best solution.
13
Barrios Sánchez, Jorge Manuel, and Roberto Baeza Serrato. "Design and Development of an Optimal Control Model in System Dynamics through State-Space Representation." Applied Sciences 13, no. 12 (2023): 7154. http://dx.doi.org/10.3390/app13127154.
Full textAbstract:
Control engineering and state-space representation are valuable tools in the analysis and design of dynamic systems. In this research, a methodology is proposed that uses these approaches to transform a system-dynamics simulation model into a mathematical model. This is achieved by expressing input, output and state variables as input, output and state vectors, respectively, allowing the representation of the model in matrix form. The resulting model is linear and time-invariant, facilitating its analysis and design. Through the use of this methodology, the system transfer matrix is obtained, which allows the analysis and design of the optimal control of the simulation model. The Ackermann gain-control technique is used to determine the optimal control of the system, which results in a shorter settlement time. This research proposal seeks to mathematically strengthen simulation models and provide an analytical alternative through modern control engineering in SD simulation models. This would allow more informed and effective decisions in the implementation of dynamic systems.
14
Vukobratović, M. K., V. F. Filaretov, and A. I. Korzun. "A unified approach to mathematical modelling of robotic manipulator dynamics." Robotica 12, no. 5 (1994): 411–20. http://dx.doi.org/10.1017/s0263574700017963.
Full textAbstract:
SUMMARYA new method for computer forming of dynamic equations of open-chain mechanical robot configurations is presented. The algorithm used is of a numeric-iterative type, based on mathematical apparatus of screw theory, which has enabled elimination of the unnecessary computations in the process of dynamic model derivation. In addition to conventional kinematic schemes of robotic manipulators, the branched kinematic chains which have recently found their application in the locomotion of robotic mechanisms were also treated. Both the inverse and direct problems of dynamics were addressed. A comparative analysis was carried out of the numerical complexity of various existing algorithms of numeric-iterative type dealing with the problems of spatial active mechanisms dynamics. It has been shown that the proposed method regardless of its generality, approaches by its models complexity symbolic models, which are valid for particular robotic mechanisms only where they achieve a high degree of efficiency.
15
Kotov, Borys, Valentyn Mironenko, Serhii Stepanenko, Volodymyr Gryshchenko, Yurii Pantsyr, and Ihor Gerasimchuk. "Mathematical Modeling of the Material Drying Process in a Drum Dryer as an Object of Automatic Control." National Interagency Scientific and Technical Collection of Works. Design, Production and Exploitation of Agricultural Machines, no. 54 (2024): 202–14. https://doi.org/10.32515/2414-3820.2024.54.202-214.
Full textAbstract:
The aim of this research is to develop a mathematical description of the material drying process in drum dryers, create simplified models for analyzing the dynamics of this process, and formulate a conceptual model for automatic control of drying equipment to improve its efficiency. The study proposes a mathematical description of the drying process in the form of a system of nonlinear partial differential equations, modeling the unsteady temperature-moisture regime of the material. Simplified models were developed for dynamic process analysis, implemented in MathCad and Matlab Simulink environments, taking into account the parameter distribution along the drum's length. These models describe the interrelation of key process parameters, such as Θ(y,τ) and u(y,τ).which characterize cross-link effects. Additionally, a conceptual model for automatic control was developed, based on methods for compensating for parameter interdependencies. The developed mathematical description of the drying process in drum dryers allows for an accurate assessment of the temperature-humidity regime of the material, which is important for optimizing the drying technology. The use of simplified models in the MathCad and Matlab Simulink software environments makes it possible to implement numerical simulation of the process dynamics, which includes the distribution of parameters along the length of the drum. This allows determining the optimal operating conditions of the dryer to ensure high quality of the processed material and reduce energy costs. The proposed automatic control concept, which is based on cross-coupling compensation methods, provides increased efficiency of the drying process regulation, stability of the dryer operation and a significant reduction in energy costs. The implementation of this concept will significantly improve the quality of material processing and increase the energy efficiency of drying processes, while reducing operating costs.
16
Sridhar, Lakshmi N. "Analysis and Control of Brain Dynamic Models." Journal of Innovative Clinical Trials and Case Reports 1, no. 1 (2025): 1–10. https://doi.org/10.63721/25jctc0104.
Full textAbstract:
The nonlinear behavior of the brain's information processing represents one of the key tasks in modern neuroscience, and a lot of research has been conducted in trying to rhythmicity in brain networks. Bifurcation analysis is a powerful mathematical tool used to deal with the nonlinear dynamics of any process. Several factors must be considered, and multiple objectives must be met simultaneously. Bifurcation analysis and multiobjective nonlinear model predictive control (MNLMPC) calculations are performed on two brain dynamic models. The MATLAB program MATCONT was used to perform the bifurcation analysis. The MNLMPC calculations were performed using the optimization language PYOMO in conjunction with the state-of-the-art global optimization solvers IPOPT and BARON.. The bifurcation analysis Hopf bifurcation points that lead to limit cycles in the two models. These Hopf points were eliminated using an activation factor that involves the tanh function. The multiobjective nonlinear model predictive control calculations converge to the Utopia point in both the problems, which is the best solution.
17
L., V. URSULYAK, and O. SHVETS A. "IMPROVEMENT OF MATHEMATICAL MODELS FOR ESTIMATION OF TRAIN DYNAMICS." Science and Transport Progress, no. 6(72) (December 8, 2017): 70–82. https://doi.org/10.15802/stp2017/118002.
Full textAbstract:
<strong>Purpose</strong>. Using scientific publications the paper analyzes the mathematical models developed in Ukraine, CIS countries and abroad for theoretical studies of train dynamics and also shows the urgency of their further improvement. <strong>Methodology</strong>. Information base of the research was official full-text and abstract databases, scientific works of domestic and foreign scientists, professional periodicals, materials of scientific and practical conferences, methodological materials of ministries and departments. Analysis of publications on existing mathematical models used to solve a wide range of problems associated with the train dynamics study shows the expediency of their application. <strong>Findings</strong>. The results of these studies were used in: 1) design of new types of draft gears and air distributors; 2) development of methods for controlling the movement of conventional and connected trains; 3) creation of appropriate process flow diagrams; 4) development of energy-saving methods of train driving; 5) revision of the Construction Codes and Regulations (SNiP ΙΙ-39.76); 6) when selecting the parameters of the autonomous automatic control system, created in DNURT, for an auxiliary locomotive that is part of a connected train; 7) when creating computer simulators for the training of locomotive drivers; 8) assessment of the vehicle dynamic indices characterizing traffic safety. Scientists around the world conduct numerical experiments related to estimation of train dynamics using mathematical models that need to be constantly improved. <strong>Originality</strong>. The authors presented the main theoretical postulates that allowed them to develop the existing mathematical models for solving problems related to the train dynamics. The analysis of scientific articles published in Ukraine, CIS countries and abroad allows us to determine the most relevant areas of application of mathematical models. <strong>Practical</strong><strong>value</strong>. The practical value of the results obtained lies in the scientific validity and applied orientation of theoretical studies using mathematical models, the improvement of which will expand the range of problems to be solved, and increase the level of reliability of the results obtained.
18
Korniyenko, Bogdan, and Andrii Nesteruk. "Mathematical modelling of granulation process in fluidised bed (overview of models)." Proceedings of the NTUU “Igor Sikorsky KPI”. Series: Chemical engineering, ecology and resource saving, no. 2 (June 30, 2022): 51–59. http://dx.doi.org/10.20535/2617-9741.2.2022.260349.
Full textAbstract:
One of the most common methods of making mineral fertilizers is granulation. Fertilizers in the form of granules have a number of advantages over conventional fertilizers in the form of powder or liquid, namely, ease of transportation, well absorbed and less susceptible to weathering from the soil, convenient to use. To obtain solid particles from liquid starting material such as solutions, emulsions or suspensions, the following processes are used: crystallization, granulation, spray drying. Depending on the focus of the study, the fluidized bed granulation process can be modeled at different levels of abstraction. The dynamics of individual particles is modeled on a microscopic scale. The interaction of a particle with a liquid, equipment or other particles is considered. The next rougher level of abstraction is the mesoscale. Here the particles are divided into classes according to their characteristics. It is assumed that the particles of the class have the same properties and dynamics. On a macroscopic scale, the roughest level of approximation, attention is focused on the integral behavior of the whole set of particles. As a result, the selected characteristic values describe the state of the particle layer. There are different approaches to modeling for each scale. It is proposed to describe the microscopic scale using the hydrodynamics model, the mesoscale using the balance model, and the macroscopic scale using the moments method or the Lagrange-Euler model. A combined balance-hydrodynamics model and a multi-chamber balance model that can be used for the tasks of building information technology for fluidized bed granulation process control technology are also considered.
19
Ritonja, Jožef, Andreja Goršek, Darja Pečar, Tatjana Petek, and Boštjan Polajžer. "Dynamic Modeling of the Impact of Temperature Changes on CO2 Production during Milk Fermentation in Batch Bioreactors." Foods 10, no. 8 (2021): 1809. http://dx.doi.org/10.3390/foods10081809.
Full textAbstract:
Knowledge of the mathematical models of the fermentation processes is indispensable for their simulation and optimization and for the design and synthesis of the applicable control systems. The paper focuses on determining a dynamic mathematical model of the milk fermentation process taking place in a batch bioreactor. Models in the literature describe milk fermentation in batch bioreactors as an autonomous system. They do not enable the analysis of the effect of temperature changes on the metabolism during fermentation. In the presented extensive multidisciplinary study, we have developed a new mathematical model that considers the impact of temperature changes on the dynamics of the CO2 produced during fermentation in the batch bioreactor. Based on laboratory tests and theoretical analysis, the appropriate structure of the temperature-considered dynamic model was first determined. Next, the model parameters of the fermentation process in the laboratory bioreactor were identified by means of particle swarm optimization. Finally, the experiments with the laboratory batch bioreactor were compared with the simulations to verify the derived mathematical model. The developed model proved to be very suitable for simulations, and, above all, it enables the design and synthesis of a control system for batch bioreactors.
20
Kosyanov, Nikita, Elena Gubar, and Vladislav Taynitskiy. "MPC Controllers in SIIR Epidemic Models." Computation 11, no. 9 (2023): 173. http://dx.doi.org/10.3390/computation11090173.
Full textAbstract:
Infectious diseases are one of the most important problems of the modern world, for example, the periodic outbreaks of coronavirus infections caused by COVID-19, influenza, and many other respiratory diseases have significantly affected the economics of many countries. Hence, it is therefore important to minimize the economic damage, which includes both loss of work and treatment costs, quarantine costs, etc. Recent studies have presented many different models describing the dynamics of virus spread, which help to analyze the epidemic outbreaks. In the current work we focus on finding solutions that are robust to noise and take into account the dynamics of future changes in the process. We extend previous results by using a nonlinear model-predictive-control (MPC) controller to find effective controls. MPC is a computational mathematical method used in dynamically controlled systems with observations to find effective controls.
21
Aponte-Rengifo, Oscar, Mario Francisco, Ramón Vilanova, Pastora Vega, and Silvana Revollar. "Intelligent Control of Wastewater Treatment Plants Based on Model-Free Deep Reinforcement Learning." Processes 11, no. 8 (2023): 2269. http://dx.doi.org/10.3390/pr11082269.
Full textAbstract:
In this work, deep reinforcement learning methodology takes advantage of transfer learning methodology to achieve a reasonable trade-off between environmental impact and operating costs in the activated sludge process of Wastewater treatment plants (WWTPs). WWTPs include complex nonlinear biological processes, high uncertainty, and climatic disturbances, among others. The dynamics of complex real processes are difficult to accurately approximate by mathematical models due to the complexity of the process itself. Consequently, model-based control can fail in practical application due to the mismatch between the mathematical model and the real process. Control based on the model-free reinforcement deep learning (RL) methodology emerges as an advantageous method to arrive at suboptimal solutions without the need for mathematical models of the real process. However, convergence of the RL method to a reasonable control for complex processes is data-intensive and time-consuming. For this reason, the RL method can use the transfer learning approach to cope with this inefficient and slow data-driven learning. In fact, the transfer learning method takes advantage of what has been learned so far so that the learning process to solve a new objective does not require so much data and time. The results demonstrate that cumulatively achieving conflicting objectives can efficiently be used to approach the control of complex real processes without relying on mathematical models.
22
Zamula, Alina, and Sergii Kavun. "Complex systems modeling with intelligent control elements." International Journal of Modeling, Simulation, and Scientific Computing 08, no. 01 (2017): 1750009. http://dx.doi.org/10.1142/s179396231750009x.
Full textAbstract:
The approach to managing complex systems through the usage of fuzzy technology in combination with the system-dynamic modeling is improved. The methods of system dynamics and artificial intelligence represented the research subject. Object of study includes process operation of complex systems. For the experiment selected banking system (BS) and commercial bank (CB) as its subsystem. The mathematical models of the BS and CBs are developed; elements of intelligent control are formalized. The knowledge base in the form of production rules is designed in the paper. Functions of fuzzy variables and their parameters are selected for building fuzzy models using Mamdani and Sugeno algorithms. The control impact and the functioning of intellectual decision support system for the prediction task are studied. The efficiency of scientific research on the example of the Ukrainian BS is evaluated.
23
M. SALMAN, AMER, and MOHD HAFIZ MOHD. "SUSTAINABLE COVID-19 CONTROL MEASURES: A MATHEMATICAL MODELLING STUDY." JOURNAL OF SUSTAINABILITY SCIENCE AND MANAGEMENT 19, no. 6 (2024): 25–35. http://dx.doi.org/10.46754/jssm.2024.06.003.
Full textAbstract:
Mathematical modelling techniques have become essential in predicting the consequences of viral disease outbreaks and in planning sustainable preventative measures to curb their transmission dynamics. To better understand the sustainable evolution of the control measures’ impacts on COVID-19 transmissions, a reaction-diffusion system is employed to describe the epidemiological phenomenon through two processes: (i) A reaction process of SEIRS-type (Susceptible, Exposed, Infected, Recovered, and Susceptible) kinetics; and (ii) a diffusion process that models local dispersal of individuals through the incorporation of spatial dimension. Optimal control theory and numerical simulation studies are performed to examine the combined effects of reinfection, spatial dispersal process, and distinct control measures on the severity of the outbreaks. The results indicate the effectiveness of implementing adequate control measures vaccination and treatments to prevent further disease outbreaks and minimise the number of infected cases. The insights from these modelling studies benefit different stakeholders, e.g., public health practitioners and policymakers in devising sustainable COVID-19 management plans to prepare for endemicity and deal with large-scale disease outbreaks in the future.
24
Nguyen, Nhu Tuong An, Vinh Quang Do, The Thinh Pham, and Tuan Tran Nguyen. "Application of different control algorithms on a ‘home-made’ temperature control lab kit." Can Tho University Journal of Science 14, no. 1 (2022): 62–73. http://dx.doi.org/10.22144/ctu.jen.2022.007.
Full textAbstract:
Providing enough facilities for students to do laboratory activities is important. An existing useful kit was proposed for students learning a variety of control engineering topics. A temperature control lab kit is made from scratch using common electronics components as a replacement for the original TCLab introduced by Hedengren (Hedengren et al., 2019). Mathematical models of the system derived from theoretical and experimental methods are simulated in Matlab/Simulink to verify their accuracy to the physical kit. Different control algorithms such as: On/Off, PID, Fuzzy are then applied on the Kit as well as its mathematical models to illustrate their control feasibility. Human machine interface (HMI) is also designed using Matlab GUI allowing an operator to select a control algorithm, tune control parameters and monitor parameters of the process. Experimental results show that the derived models can reflect quite well dynamics of the physical kit with temperature deviation among them in the range of ±3°C. This confirms that the kit is well-suited for teaching different control topics such as system modelling, system identification, classical control and advanced control algorithms.
25
Khvostov, A. A., A. A. Zhuravlev, E. A. Shipilova, R. S. Sumina, G. O. Magomedov, and I. A. Khaustov. "Simulink models of technological systems with perfect mixing and plug-flow hydrodynamics." Proceedings of the Voronezh State University of Engineering Technologies 81, no. 3 (2019): 28–38. http://dx.doi.org/10.20914/2310-1202-2019-3-28-38.
Full textAbstract:
The dynamic models of elements of technological systems with perfect mixing and plug-flow hydrodynamics are based on the systems of algebraic and differential equations that describe a change in the basic technological parameters. The main difficulty in using such models in MathWorks Simulink™ computer simulation systems is the representation of ordinary differential equations (ODE) and partial differential equations (PDE) that describe the dynamics of a process as a MathWorks Simulink™ block set. The study was aimed at developing an approach to the synthesis of matrix dynamic models of elements of technological systems with perfect mixing and plug-flow hydrodynamics that allows for transition from PDE to an ODE system on the basis of matrix representation of discretization of coordinate derivatives. The process of synthesis of the dynamic matrix mathematical model was considered by the example of a sugar syrup cooler, the quality indicator of the finished product are selected as sucrose crystals and their portion in the total volume of caramel mass. Taking into account the dependence of syrup viscosity on temperature, thermal effects as a result of the process of crystallization of sucrose from syrup, design features of a typical caramel machine made it possible to clarify the dynamics of the process of syrup cooling. The model developed with this approach allows to obtain real-time estimates of temperatures at the outlet of the cooler, which makes it possible to study the dynamics of the technological process and synthesize the control system. The presented approach allows to implement mathematical models of ideal reactors in Simulink system and to move to matrix ordinary differential equations, which makes it possible to convert them into Simulink blocks. The approach is also applicable to other models of ideal reactors, which allows to form libraries of typical ideal reactors of Simulink system for synthesis of heat and mass exchange equipment. The proposed approach significantly simplifies the study and modernization of the current and the development of new technological equipment, as well as the synthesis of algorithms for controlling the processes therein.
26
Alzbutas, R., and V. Janilionis. "THE SIMULATION OF DYNAMIC SYSTEMS USING COMBINED MODELLING." Mathematical Modelling and Analysis 5, no. 1 (2000): 7–17. http://dx.doi.org/10.3846/13926292.2000.9637123.
Full textAbstract:
The new approach to the problems of dynamic systems simulation is proposed. The analytical and imitation modelling of non‐linear complex dynamic systems which comprise simulation of continuous and discrete processes with constant and variable parameters, are investigated. The aggregate mathematical modelling scheme [1] and the method of control sequences for discrete systems specification and simulation are used as well as the dynamic mathematical modelling scheme for continuous process formalization and modelling. According to them the investigated systems are presented as the set of interacting piecewise linear aggregates, which can include processes described with differential equations. The above mentioned approach is used in developing software for the construction and research of the models. The modelling of the dynamic systems’ control is also analyzed and developed software for the dynamic systems’ simulation is presented. It is related to the proposed combined modelling methodology. The developed dynamical simulation system ADPRO (Automatic Differentiation PROgram) extends applicability of the system SIMAS (SIMulation of the Aggregate Systems) [2] with dynamical simulation means realized with APL2 (A Programming Language 2) and based on automatic differentiation [3]. The created model of service process and its control can be used as a base for other models of wide class complex dynamics’ systems [4], the parts of which are described with differential equations.
27
Wang, Zixiong, Xiaoyi Wang, Guotao Zhang, and Fangling Liang. "Numerical study of the seepage behavior of droplets in porous materials." Journal of Physics: Conference Series 2760, no. 1 (2024): 012069. http://dx.doi.org/10.1088/1742-6596/2760/1/012069.
Full textAbstract:
Abstract This article analyzes the spread and infiltration dynamic process of liquid droplets on the surface of porous materials and establishes a mathematical model of dynamic changes in droplets. To accurately describe the dynamic effects of droplet flow, a mathematical model of droplet dynamics was developed by applying the level-set method, and the seepage process of the droplets was also analyzed numerically. The effects of a series of control parameters on the droplet percolation process are analyzed, and the pore degree of porous materials performed a numerical study of the droplet deformation process. We observe the spread and infiltration process of liquid droplets in porous materials models. It was found that there is a competitive relationship between diffusion and penetration of droplets. The depth of penetration of droplets decreases with increasing viscosity and increases with increasing porosity. The results of the study help to understand the seepage behavior of the droplet on the surface of the porous material.
28
ДИВАК, МИКОЛА, та ВАДИМ ЗАБЧУК. "МОДЕЛЮВАННЯ ХАРАКТЕРИСТИК ПРОЦЕСІВ У БІОГАЗОВИХ УСТАНОВКАХ НА ОСНОВІ АНАЛІЗУ ІНТЕРВАЛЬНИХ ДАНИХ". Herald of Khmelnytskyi National University. Technical sciences 331, № 1 (2024): 180–90. http://dx.doi.org/10.31891/2307-5732-2024-331-28.
Full textAbstract:
The problems of the research presented in the work relate to mathematical modeling in order to reflect the relationship between the main characteristic of the process and the factors that affect it, as well as the dynamics of the main characteristic of the process, which is determined by the acidity of the substrate in the bioreactor. To build mathematical models of both types, it is proposed to use the methods of parametric and structural identification of models of static objects and discrete models of object dynamics based on the analysis of interval data. An universal approach based on metaheuristic optimization algorithms is proposed and substantiated for the identification of both types of models. These methods, in turn, use mechanisms of self-organization and self-adaptation in the process of finding an optimal or quasi-optimal solution. In particular, the work uses computational algorithms built on the basis of artificial bee colony algorithms. The method is implemented using data presented in interval form. The proposed universal method was tested on the construction of a mathematical model that reflects the dependence between the pH of the fermentation medium and the volume of the loaded bio-raw material in the form of its dry and liquid parts, the temperature in the bioreactor and the humidity of the dry part of the bio-raw material. Another mathematical model built in the work reflects the dynamics of the acidity indicator pH of the fermentation medium depending on the ratio of the mass of the loaded dry bio-raw material to the volume of the loaded liquid bio-raw material Both obtained interval mathematical models can be applied to control processes in biogas plants.
29
Shmalko, Elizaveta, Yuri Rumyantsev, Ruslan Baynazarov, and Konstantin Yamshanov. "Identification of Neural Network Model of Robot to Solve the Optimal Control Problem." Informatics and Automation 20, no. 6 (2021): 1254–78. http://dx.doi.org/10.15622/ia.20.6.3.
Full textAbstract:
To calculate the optimal control, a satisfactory mathematical model of the control object is required. Further, when implementing the calculated controls on a real object, the same model can be used in robot navigation to predict its position and correct sensor data, therefore, it is important that the model adequately reflects the dynamics of the object. Model derivation is often time-consuming and sometimes even impossible using traditional methods. In view of the increasing diversity and extremely complex nature of control objects, including the variety of modern robotic systems, the identification problem is becoming increasingly important, which allows you to build a mathematical model of the control object, having input and output data about the system. The identification of a nonlinear system is of particular interest, since most real systems have nonlinear dynamics. And if earlier the identification of the system model consisted in the selection of the optimal parameters for the selected structure, then the emergence of modern machine learning methods opens up broader prospects and allows you to automate the identification process itself. In this paper, a wheeled robot with a differential drive in the Gazebo simulation environment, which is currently the most popular software package for the development and simulation of robotic systems, is considered as a control object. The mathematical model of the robot is unknown in advance. The main problem is that the existing mathematical models do not correspond to the real dynamics of the robot in the simulator. The paper considers the solution to the problem of identifying a mathematical model of a control object using machine learning technique of the neural networks. A new mixed approach is proposed. It is based on the use of well-known simple models of the object and identification of unaccounted dynamic properties of the object using a neural network based on a training sample. To generate training data, a software package was written that automates the collection process using two ROS nodes. To train the neural network, the PyTorch framework was used and an open source software package was created. Further, the identified object model is used to calculate the optimal control. The results of the computational experiment demonstrate the adequacy and performance of the resulting model. The presented approach based on a combination of a well-known mathematical model and an additional identified neural network model allows using the advantages of the accumulated physical apparatus and increasing its efficiency and accuracy through the use of modern machine learning tools.
30
Beglov, Konstantin V., Victoria I. Kryvda, Oleksandr А. Klymchuk, Vladyslav R. Zhukovskyi, Oleksandr V. Yavorskyi, and Gennady Io Galanter. "Comparison of mathematical models of power generation equipment in transient process simulation in energy systems." ELECTRICAL AND COMPUTER SYSTEMS, no. 42(118) (2025): 32–42. https://doi.org/10.15276/eltecs.42.118.2025.4.
Full textAbstract:
Abstract. The aim of this study is to develop approaches for improving the efficiency of transient characteristic modeling of a nuclear power plant (NPP) unit by approximating dynamic processes using first- and second-order functions. This will reduce the simulation time for combined energy systems, which include power generation sources with different physical operating principles (NPPs, thermal power plants, hydroelectric power plants, wind farms, etc.). This work is aimed at ensuring the selection of a simulation model of equipment that best meets the goals of future research, including the development of new approaches to controlling the structure and parameters of power generating sources, which will contribute to the efficient operation of power systems. Keywords: automatic control system, identification, classification, efficiency criteria, mathematical model.
31
Gallego, Antonio J., Manuel Macías, Fernando de de Castilla, and Eduardo F. Camacho. "Mathematical modeling of the Mojave Solar Plants." Energies 12, no. 21 (2019): 4197. http://dx.doi.org/10.3390/en12214197.
Full textAbstract:
Competitiveness of solar energy is one of current main research topics. Overall efficiency of solar plants can be improved by using advanced control strategies. To design and tuning properly advanced control strategies, a mathematical model of the plant is needed. The model has to fulfill two important points: (1) It has to reproduce accurately the dynamics of the real system; and (2) since the model is used to test advanced control strategies, its computational burden has to be as low as possible. This trade-off is essential to optimize the tuning process of the controller and minimize the commissioning time. In this paper, the modeling of the large-scale commercial solar trough plants Mojave Beta and Mojave Alpha is presented. These two models were used to test advanced control strategies to operate the plants.
32
Dyvak, Mykola, Andriy Melnyk, Andriy Pukas, and Libor Dostalek. "Control of mathematical modeling process of dynamics of harmful substances concentrations on the basis of ontological approach." Computational Problems of Electrical Engineering 12, no. 1 (2022): 7–16. http://dx.doi.org/10.23939/jcpee2022.01.007.
Full textAbstract:
The problem of building a mathematical model of the dynamics of nitrogen dioxide concentrations at different parts of the city is considered in the paper. The peculiarities of the construction of such models on the basis of periodic measurement of concentrations of harmful substances and identification on the basis of the measurements obtained are considered. This paper also proposes an ontological approach as a control tool that greatly simplifies the systematic standardized methods of the models storage, the process of their construction and appropriate usage in practice. The use of the ontological model allows formalizing the process of obtaining, storing and using relevant knowledge and is suitable for more intelligent systems, such as identification of obviously erroneous solutions based on the model, predictive control of the model, optimization of the decision-making process based on knowledge and modeling of an appropriate technological flow chart. This paper also describes the features of the construction of the corresponding ontological model, the pattern of choice of a nonlinear model with "switching" to different conditions. Relevant experimental studies have also been conducted to confirm the effectiveness of the proposed approach.
33
Ryzhova, I. M., Vladimir A. Romanenkov, and Viktor M. Stepanenko. "Modern development of soil organic matter dynamics models (review)." Lomonosov Soil Science Journal 79, no. 4, 2024 (2024): 122–29. https://doi.org/10.55959/msu0137-0944-17-2024-79-4-122-129.
Full textAbstract:
Soils are the largest terrestrial reservoir of organic carbon, so even small changes in soil carbon stocks can have significant effects on the atmosphere and climate. To select effective strategies to mitigate climate change, predictions of how soils will respond to future changes in climate and land use are needed. Achieving meaningful predictions requires a deep understanding of the highly complex, open, multicomponent soil organic matter system. One of the most effective methods for predicting the dynamics of soil organic matter is mathematical modeling. Process-oriented (physically based) models make it possible to present the basic concepts about the mechanisms that determine the behavior of this system in a mathematically formalized form and conduct a quantitative analysis. The uncertainty of the forecasts depends on the level of development of the theory explaining the dynamics of soil organic matter, the models representing it and their experimental support. This review examines the achievements of the last decade in modeling the role of microorganisms in the stabilization of soil organic matter, the concept of soil saturation with organic carbon, temperature control, as well as the development of reactive transport models describing the dynamics of organic carbon in the soil profile, and the representation of the dynamics of soil organic matter in global climate models. Unsolved problems associated with the high variability in the structure of new generation soil organic matter dynamics models are discussed.
34
Slivinskas, Kastytis, Vladimir Gichan, Vytautas Striška, and Algimantas Juozas Poška. "Optimization of Transport Movement Parameters of the Transfer Manipulator for the Quenching Bath According to the Technological Process Requirements." Solid State Phenomena 164 (June 2010): 411–18. http://dx.doi.org/10.4028/www.scientific.net/ssp.164.411.
Full textAbstract:
The paper analyzes problems related to optimal selection and possible control of parameters of transport movements of the manipulator serving galvanizing and quenching baths. Evaluation of the acceleration and braking processes as well as minimization of the deflection during oscillations of the suspended loading are considered. Mathematical models of the transfer manipulator together with the loading unit were developed, which enable dynamic evaluation of transporting movements. Calculations of the dynamics of the model and simulations of the transfer process were performed. The obtained research results allow to improve the quality of the processing and reduce the emission of pollutions.
35
Wolkenhauer, O., S. N. Sreenath, P. Wellstead, M. Ullah, and K. H. Cho. "A systems- and signal-oriented approach to intracellular dynamics." Biochemical Society Transactions 33, no. 3 (2005): 507–15. http://dx.doi.org/10.1042/bst0330507.
Full textAbstract:
A mathematical understanding of regulation, and, in particular, the role of feedback, has been central to the advance of the physical sciences and technology. In this article, the framework provided by systems biology is used to argue that the same can be true for molecular biology. In particular, and using basic modular methods of mathematical modelling which are standard in control theory, a set of dynamic models is developed for some illustrative cell signalling processes. These models, supported by recent experimental evidence, are used to argue that a control theoretical approach to the mechanisms of feedback in intracellular signalling is central to furthering our understanding of molecular communication. As a specific example, a MAPK (mitogen-activated protein kinase) signalling pathway is used to show how potential feedback mechanisms in the signalling process can be investigated in a simulated environment. Such ‘what if’ modelling/simulation studies have been an integral part of physical science research for many years. Using tools of control systems analysis, as embodied in the disciplines of systems biology, similar predictive modelling/simulation studies are now bearing fruit in cell signalling research.
36
Feldblium, I. V., V. G. Akimkin, A. V. Alimov, et al. "NEW APPROACHES TO ASSESSING AND FORECASTING MORBIDITY WITH ENTEROVIRUS (NON-POLIO) INFECTION IN THE RUSSIAN FEDERATION USING MATHEMATICAL MODELS." Health Risk Analysis, no. 3 (September 2021): 108–17. http://dx.doi.org/10.21668/health.risk/2021.3.10.
Full textAbstract:
At present it is impossible to develop epidemiologic surveillance and control over any infection regarding studies on dynamics of morbidity, seasonality and periodicity without using mathematical modeling techniques. Our research goal was to study regularities in manifestations of epidemic process for enterovirus (non-polio) infection (EVnI) in the Russian Federation over 14 years (2006–2019) using mathematical models (linear, logarithmic, power, and exponential approximation).An optimal mathematical model was selected using three statistical parameters, namely determination coefficient, Fischer’s exact test, and standard error. Periodicity of rises and falls in morbidity was calculated with Fourier one- dimensional spectral analysis. Intra-year dynamics of morbidity with EVnI was estimated basing on monthly spread of the disease cases on the RF territory. Classic seasonal decomposition, Census I technique, was applied to analyze time series of monthly morbidity. It was determined that EVnI epidemic process was unevenly spread over years in the RF in the examined period of time (2006–2019) and there were two opposite trends in it; the first one lasted from 2006 to 2010 when morbidity was declining and the second was from 2010 to 2019 when it was growing. Having analyzed manifestations of EVnI epidemi- ologic process in long-term dynamics given its uneven spread as per years, we established that it was advisable to use mathematical models approximated as per separate time periods. Average long-term morbidity with EVnI amounted to 8.09 0/0000 in the RF in 2010–2019 with growth rate being equal to 17.7 %. Maximum value was registered in 2017 (16.32 0/0000). An unfavorable prediction for further epidemic situation development was revealed for the examined pe- riod. The epidemic process was characterized with 4-year periodicity and summer-autumn seasonality with peaks usually occurring in August and September. Rates that characterized intensity of the trends in long-term morbidity dynamics and were calculated with mathematical models differed authentically from those obtained via conventional calculations of average values (χ=11.08; d.f.=1; p=0.0009).
37
Feldblium, I. V., V. G. Akimkin, A. V. Alimov, et al. "NEW APPROACHES TO ASSESSING AND FORECASTING MORBIDITY WITH ENTEROVIRUS (NON-POLIO) INFECTION IN THE RUSSIAN FEDERATION USING MATHEMATICAL MODELS." Health Risk Analysis, no. 3 (September 2021): 108–17. http://dx.doi.org/10.21668/health.risk/2021.3.10.eng.
Full textAbstract:
At present it is impossible to develop epidemiologic surveillance and control over any infection regarding studies on dynamics of morbidity, seasonality and periodicity without using mathematical modeling techniques. Our research goal was to study regularities in manifestations of epidemic process for enterovirus (non-polio) infection (EVnI) in the Russian Federation over 14 years (2006–2019) using mathematical models (linear, logarithmic, power, and exponential approximation).An optimal mathematical model was selected using three statistical parameters, namely determination coefficient, Fischer’s exact test, and standard error. Periodicity of rises and falls in morbidity was calculated with Fourier one- dimensional spectral analysis. Intra-year dynamics of morbidity with EVnI was estimated basing on monthly spread of the disease cases on the RF territory. Classic seasonal decomposition, Census I technique, was applied to analyze time series of monthly morbidity. It was determined that EVnI epidemic process was unevenly spread over years in the RF in the examined period of time (2006–2019) and there were two opposite trends in it; the first one lasted from 2006 to 2010 when morbidity was declining and the second was from 2010 to 2019 when it was growing. Having analyzed manifestations of EVnI epidemi- ologic process in long-term dynamics given its uneven spread as per years, we established that it was advisable to use mathematical models approximated as per separate time periods. Average long-term morbidity with EVnI amounted to 8.09 0/0000 in the RF in 2010–2019 with growth rate being equal to 17.7 %. Maximum value was registered in 2017 (16.32 0/0000). An unfavorable prediction for further epidemic situation development was revealed for the examined pe- riod. The epidemic process was characterized with 4-year periodicity and summer-autumn seasonality with peaks usually occurring in August and September. Rates that characterized intensity of the trends in long-term morbidity dynamics and were calculated with mathematical models differed authentically from those obtained via conventional calculations of average values (χ=11.08; d.f.=1; p=0.0009).
38
Kovalenko, O. Ye, and V. L. Kosolapov. "Stability model of agent-based situational control system." Mathematical machines and systems 3 (2020): 93–104. http://dx.doi.org/10.34121/1028-9763-2020-3-93-104.
Full textAbstract:
While managing complex systems, it is advisable to use mathematical models adequate to real systems, which can be used for generalized model analysis of different solutions in systems of situational man-agement (SSM). Such mathematical models are an important component of the SSM in the process of supporting the adoption of important strategic and operational decisions at various levels of government. Implementation of SSM in the form of a multi-agent system, due to its characteristics, is an adequate approach to solving the problems of situational management (SM). According to the context of SM, the behavior of the SSM is described as the dynamics of movement from a certain point in the phase space, that corresponds to some state of the managed system under the influence of the ensemble of SSM agents. During the operation of the SSM, its agents use knowledge that corresponds to the context of the situation. The agent's knowledge is a fragment of the field of knowledge on the target problem of SM. Knowledge of the problem area of the SM is a key element of the SM model. The convergent agents’ ensemble of SSM is characterized by a certain level of intelligence, which is represented as an entropic force that uses the free energy of the dissipative system to maintain its stability. An agent-oriented approach to the study of the stability of a dynamic stochastic system in the process of situa-tional management as a target project activity is proposed. Within the scope of the proposed approach, the stability model of agent SSM as a dynamic stochastic system is considered using the Lyapunov sta-bility criterion in the form of a system of ordinary differential equations. Support of modeling functions by agents of agent-oriented system allows to form adequate behavior in the process of situational man-agement in the conditions of changes in the environment. Developed models for integrating behavioral and coordination aspects of knowledge-based agents can be used in the development of situational man-agement systems and technologies.
39
Kotov, B. I., V. O. Hryshchenko, S. P. Stepanenko, Y. I. Pantsir, and I. D. Gerasimchuk. "Mathematical model of system dynamics for heat utilization from ventilation emissions with intermediate heat carrier as an object of automation." Mehanization and electrification of agricultural, no. 14(113) (2021): 88–97. http://dx.doi.org/10.37204/0131-2189-2021-14-9.
Full textAbstract:
Annotation Purpose. Formulation of a mathematical description of the non-stationary thermal regime of the heat utilization system of ventilation emissions on the basis of a heat exchanger with an intermediate heat carrier for industrial premises. Methods. The specificity of the object under study determines the analytical method of research, which is based on the analysis of the thermal balance of the elements of the studied system and the heat and energy connections between them. Results. A mathematical description of the dynamics of the thermal process in a recuperative heat recovery unit with an intermediate heat carrier is formulated taking into account the variable parameters of ventilation air, both exhaust and supply air and the presence of condensation on the surface of the heat exchanger. Given the possibility of automatic control of the operation of the disposal system by changing the flow of intermediate coolant (which makes the system of equations nonlinear), a linearized mathematical model of the dynamics of the studied system is proposed. Conclusions 1. The obtained mathematical models allow to determine the dynamic characteristics of the system of waste heat utilization in transient modes, as well as to evaluate the efficiency of the system itself and to optimize the parameters of heat exchangers. 2. The linearized model of the thermal utilizer allows to synthesize the system of automatic control of the operating mode and to investigate its parameters. Keywords: waste heat utilization, heat exchanger recuperator, intermediate heat carrier, dynamic mode, mathematical model, production room.
40
Ryzhova, I. M., V. A. Romanenkov, and V. M. Stepanenko. "Modern Development of Soil Organic Matter Dynamics Models (Review)." Moscow University Soil Science Bulletin 79, no. 4 (2024): 493–99. https://doi.org/10.3103/s0147687424700467.
Full textAbstract:
Abstract Soils are the largest terrestrial reservoir of organic carbon, and so even small changes in soil carbon stocks can have significant effects on the atmosphere and climate. To select effective strategies to mitigate climate change, predictions of how soils will respond to future changes in climate and land use are needed. Achieving meaningful predictions requires a deep understanding of the highly complex, open, multicomponent soil organic matter system. One of the most effective methods for predicting the dynamics of soil organic matter is mathematical modeling. Process-oriented (physically based) models make it possible to present the basic concepts about the mechanisms that determine the behavior of this system in a mathematically formalized form and conduct a quantitative analysis. The uncertainty of the forecasts depends on the level of development of the theory explaining the dynamics of soil organic matter, the models representing it and their experimental support. This review examines the achievements of the last decade in modeling the role of microorganisms in the stabilization of soil organic matter, the concept of soil saturation with organic carbon, and temperature control, as well as the development of reactive transport models describing the dynamics of organic carbon in the soil profile and the representation of the dynamics of soil organic matter in global climate models. Unsolved problems associated with the high variability in the structure of new generation soil organic matter dynamics models are discussed.
41
Suhika, Dewi, Roberd Saragih, Dewi Handayani, and Mochamad Apri. "Optimal control strategies based on extended Kalman filter in mathematical models of COVID-19." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 6 (2024): 6300. http://dx.doi.org/10.11591/ijece.v14i6.pp6300-6312.
Full textAbstract:
The Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is an extremely contagious variant that has garnered global attention due to its potential for rapid spread and its impact on the effectiveness of vaccines and non-pharmacological measures. In this paper, we investigate mathematical models involving vaccinated individuals and control functions to analyze how the spread of coronavirus disease 2019 (COVID-19) infection evolves over time. In the process of constructing a mathematical model for COVID-19, there are many parameters whose values are not yet known with certainty. Therefore, the extended Kalman filter method is used as a tool to estimate these parameters in an effort to better understand the dynamics of the spread and evolution of this disease. This method helps align the mathematical model with existing empirical data, allowing us to make more accurate predictions about the course of the COVID-19 pandemic and plan more precise actions to address the situation. Furthermore, an optimal control design is applied to reduce the number of infected individuals by implementing seven strategies involving a combination of health education, vaccination, and isolation controls. The simulation results we conducted indicate that the use of optimal control strategies can lead to a significant decrease in the number of individuals infected with COVID-19.
42
Zhuchenko, Anatolii, Ruslan Osipa, Liudmyla Osipa, and Lesia Ladieva. "Algorithm for controlling the process of buffer wastewater neutralization." Proceedings of the NTUU “Igor Sikorsky KPI”. Series: Chemical engineering, ecology and resource saving, no. 3 (September 29, 2021): 27–35. http://dx.doi.org/10.20535/2617-9741.3.2021.241028.
Full textAbstract:
In Ukraine, the condition of surface water near industrial enterprises is extremely critical. The operation of enterprises leads to intensive water pollution with industrial and domestic wastewater. Therefore, improving the quality of treatment facilities through the introduction of automated control systems is an urgent problem.
 For the operation of automated control systems for typical cleaning processes, a software package is required, which is developed on the basis of appropriate algorithmic software and mathematical models of processes. To obtain them, methods of mathematical and simulation modeling and block diagram method of algorithmization were used.
 In order to assess the quality of the developed algorithm for controlling the process of buffer wastewater neutralization during operation, a comparison of control system operation based on this algorithm with the most successful foreign variants of neutralization control systems was made. Simulation for the average values of operating parameters Q = 75 m3 / h, CP = 75 g / l, and Ck = 2 g / l at minimum b = 0.02 g / l pH and maximum buffer value b = 0.47 g / l pH, and also with unidirectional extreme combination of parameters Q = 50 m3 / h, CP = 100 g / l, b = 0,02 g / l pH and Q = 100 m3 / h, CP= 50 g / l, for minimum b = 0.02 g / l pH and maximum buffering value b = 0.47 g / l pH shows that the best quality of transient processes is for the control system operating on the basis of the developed algorithm. For any combination of parameters, the transients for this control system provide better quality transients. Studies have shown that the control system based on the developed algorithm in comparison with the previously proposed systems provides better process control by reducing the time of transients and reducing the dynamic deviation of the output parameters, which improves the quality of wastewater treatment.
 Given the non-stationary process and high requirements for the cleaning parameters, manual control of this process is beyond the power of even an experienced operator. The developed mathematical model describing the dynamics of the wastewater neutralization reactor with buffer properties and the process control algorithm made it possible to proceed to the development of the control system software, which is necessary for the automated control of this process.
43
Madear, Gelu, and Camelia Madear. "Environmental modelling - a modern tool towards sustainability." MATEC Web of Conferences 342 (2021): 03013. http://dx.doi.org/10.1051/matecconf/202134203013.
Full textAbstract:
One way to solve environmental problems is through modelling. Humankind developed a series of models, from mental models, physical models to computer simulation models. Building a model assumes abstraction, simplifying the natural system by considering only the essential details and discarding irrelevant ones. Mapping the real worlds to the world of models is done by choosing an abstraction level and the corresponding modelling tool. The right abstraction level is paramount for any modelling project, depending on the real problem being analysed. In modern simulation modelling, there are three methods, each having a particular range of abstraction levels: system dynamics, discrete event (process-centric modelling) and agent-based models. Ecosystems and generally any environmental problems (real world) are complex dynamics that challenge our comprehension. Understanding the significant environmental challenges is vital to adopt adequate policies for a sustainable environment through modelling and simulation. Since our cognitive abilities are limited, we need a simulation of the environmental systems to see the dynamic patterns and how humans interact with the environment. Environmental modelling helps us understand complex systems by building mathematical models and running simulations using a high abstraction level. The system dynamics method of modelling and simulation is used to clarify the representation of the stocks and flows and the feedback process that control the flows and describe the dynamic behaviour (growth, decay, or oscillations) of complex systems over time. Modelling for prediction, understanding across time and spatial scales, and environmental systems disciplines is key for a sustainable future.
44
Liashenko, Serhii, Victor Kis, Oleksandr Kis, and Yevhenii Leshchenko. "Determination the parameters of model the technological process of diffusion in sugar production on the basis of neuro network identification." Ukrainian Journal of Applied Economics and Technology 8, no. 1 (2023): 241–47. http://dx.doi.org/10.36887/2415-8453-2023-1-35.
Full textAbstract:
The article considers the sugar industry's importance in the country's food supply. The complexity of technological processes characterizes sugar factories. A system analysis of the technical operation of diffusion and an analysis of indicators of the diffusion process in the diffusion equipment were carried out. An analysis of the mathematical support of automated control systems of complex technological processes is given. The assessment of material, quality, and energy indicators was carried out, as well as the formation of the necessary information variables, which makes it possible to determine the structure of the technological process control system. To effectively manage the complex dynamic technological process of diffusion, it is proposed to use neurocontrol. The use of linear regression models was considered to determine the type of mathematical models in the system of automated control of technological processes in the diffusion department of the sugar factory. When building regression models, a significant number of input and output components of the technology diffusion process were considered. Two variants of regression models of the diffusion process were considered. The obtained regression models were analyzed, and the significance of the indicators of the diffusion process was determined. For an adequate description of the complex diffusion process in an automated control system, it is proposed to use models based on neural network identification. Significant indicators of regression models were taken as the main indicators of the process. The construction of mathematical models that make it possible to respond to changes in the technological process adequately boils down to the issue of building neural network models based on a multilayer perceptron and a radial base network. The most effective model structures were determined from various types of neural network models. The control activation time when using the proposed model was 3 minutes. The error of identification of the received models was 5-7%. Keywords: diffusion equipment, mathematical model, neural network, regression equation, control, identification.
45
Petrakov, Yuri, Oleksandr Okhrimenko, and Maksim Sikailo. "Identification of dynamics for machining systems." Mechanics and Advanced Technologies 8, no. 4(103) (2024): 337–45. https://doi.org/10.20535/2521-1943.2024.8.4(103).305837.
Full textAbstract:
Cutting processes are carried out in an elastic machining system, which is multi-mass with negative and positive loop control with a delay in construed mathematical models. Its behavior during the cutting process is entirely determined by dynamic properties and an adequate parameters of mathematical model is necessary to control the process. The paper proposes a method for identifying such dynamic parameters of the machining system, which include natural vibration frequencies, vibration damping coefficients, and stiffness of the replacement model of single-mass system in the direction of the machine-CNC coordinate axes. It is proposed to identify such parameters as a result of experimental modal analysis by impacting the elements of the tool and workpiece with an impact hammer and processing the impulse signal with a fast Fourier transform. It is proposed to adapt the results obtained to the adopted mathematical model of the machining system, presented in the form of two masses, each with two degrees of freedom, according to the equivalence of the spectrum signal power or its spectral density. The cutting force model in the form of a linearized dependence on the area of undeformed chips needs to be clarified by the coefficient using experimental oscillograms obtained during milling of a workpiece mounted on a dynamometer table. Based on the identified parameters of the machining system, a stability diagram was constructed in the “spindle speed – feed” coordinates and experiments were carried out under conditions in the zone of stable and unstable cutting. Evaluation of the roughness of the machined surface confirmed the correspondence to the location of the stability lobes diagram constructed using the identified parameters, which indicates the effectiveness of the proposed identification method.
46
Rizk, Hanan, Ahmed Chaibet, and Ali Kribèche. "Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey." Applied Sciences 13, no. 11 (2023): 6700. http://dx.doi.org/10.3390/app13116700.
Full textAbstract:
Autonomous driving has the potential to revolutionize mobility and transportation by reducing road accidents, alleviating traffic congestion, and mitigating air pollution. This transformation can result in energy efficiency, enhanced convenience, and increased productivity, as valuable driving time can be repurposed for other activities. The main objective of this paper is to provide a comprehensive technical survey of the latest research in the field of lateral, longitudinal, and integrated control techniques for autonomous vehicles. The survey aims to explore a wide range of techniques and methodologies employed to achieve precise steering control while also considering longitudinal aspects. Model-based control techniques form the foundation for control, utilizing mathematical models of vehicle dynamics to design controllers that effectively track desired speeds and/or steering behavior. Unlike model-free control techniques such as reinforcement learning and deep learning algorithms facilitate the integration of longitudinal and lateral control by learning control policies directly from data and without explicit knowledge of the underlying dynamics. Through this survey, the paper delves into the strengths, limitations, and advancements in both model-based and model-free control approaches for autonomous vehicles. It investigates their performance in real-world scenarios and addresses the technical challenges associated with their implementation. These challenges may include uncertainties in the environment, adaptability to dynamic conditions, robustness, safety considerations, and computational complexity.
47
Grządziela, Andrzej, and Stanisław Hożyń. "Simulation Tests of a Drive Shaft and Propeller Control Subsystem for a Fast Boat." Polish Maritime Research 31, no. 2 (2024): 20–28. http://dx.doi.org/10.2478/pomr-2024-0018.
Full textAbstract:
Abstract This paper presents an analysis of the acceleration of a fast boat using a simulation model. Mathematical equations of ship motion dynamics with two types of propeller capabilities are developed using MATLAB and Simulink as simulation tools. The equations are extended to include the acting thrust, resistance, propeller’s performance curves, and the PID governor curve for the acceleration manoeuvre. The application models the dynamic differential equations representing the vessel dynamics in one degree of freedom. MATLAB code was used to develop the ship acceleration as a multibody system. Modules of hydrodynamic forces, resistance, moments, and propeller performances were implemented to simulate the ship manoeuvring process. A comparison of the results for the boat’s propulsion performance with two different propellers and the characteristics of the PID governor, which controls the fuel dose in the gas turbines, was carried out. We present a summary including a comparative analysis of the results for the boat dynamics with and without the PID governor. The results obtained here confirm significant discrepancies between the results of numerical simulations with and without the PID governor.
48
Khusainov, Denis, Andrey Shatyrko, Alexey Bychkov, Bedrick Puza, and Veronika Novotna. "INVESTIGATION OF THE IMPACT OF DELAY IN ONE MATHEMATICAL MODEL OF WORLD DEVELOPMENT DYNAMICS." Journal of Automation and Information sciences 6 (November 1, 2021): 47–54. http://dx.doi.org/10.34229/1028-0979-2021-6-5.
Full textAbstract:
There is a large number of works devoted to the dynamics of world development. But very few of them have clear abstract mathematical models of the corresponding processes. This work is devoted to further deepening and mathematical abstraction of the study of world development process. The qualitative analysis of linear and modified nonlinear model in the form of systems of inhomogeneous differential equations is carried out. Their steady states are calculated, explicit analytical solutions are presented. For the first time, a model taking into account the time delay factor is proposed, which is written in the form of functional-differential equations with argument deviation. It is shown that with such an introduction to the model of a delayed argument, the system can be reduced to a system of linear inhomogeneous differential equations with constant coefficients without delay, and the stability of the steady state of the system equilibrium under study will be affected only by linear terms of equations without argument deviation. This fact well correlates with the socio-economic interpretation of this problem. In the future, the work will focus on studying the influence of not one but several factors of time lag, when the model is presented as a system of functional-differential equations with several different deviating arguments in equations responsible for the dynamics of a particular process dynamics of world development.
49
Staroverov, B. A., and S. K. Ulybyshev. "Mathematical model of room heating as object of dynamic temperature control." Vestnik IGEU, no. 3 (June 30, 2023): 62–67. http://dx.doi.org/10.17588/2072-2672.2023.3.062-067.
Full textAbstract:
Energy saving issue in the process of administrative buildings heating is an important one. Dynamic control of room temperature depending on the schedule of its usage gives great opportunities. Dynamic control should meet the criteria of energy efficiency and ensure air temperature in the rooms at time intervals of their usage at the required level in terms of external impact on the building. Obviously, such control can only be implemented by an automatic system. To synthesize such a system, we need mathematical models of room heating as control objects. Consequently, the problem under consideration is relevant. Experimental data and numerical simulation methods are used to obtain mathematical models. Mathematical models are obtained in the state space and in the form of transfer functions for controlling and disturbing influences based on the equations of the dynamic heat balance of the room. Two types of mathematical model of the room are defined. They make it possible to synthesize the laws of qualitative and quantitative control of the heat supply of the building with a given accuracy and to determine schedules of temperature changes depending on the schedule of its use, optimal in terms of minimum energy consumption.
50
Arif Mammadov, Arif Mammadov, Nizami Ismayilov Nizami Ismayilov, Mukhtar Huseynov Mukhtar Huseynov, and Faiq Guliyev Faiq Guliyev. "SOME ASPECTS OF MATHEMATICAL MODELING OF ELECTRIC STEEL MELTING PROCESS." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 14, no. 03 (2022): 04–12. http://dx.doi.org/10.36962/pahtei14032022-04.
Full textAbstract:
The article discusses some aspects of mathematical modeling of the process of melting electric steel on the basis of innovative metallurgical technologies. It was noted that the production of electric steel mainly consists of three stages - preparation of the charge, melting and casting of liquid steel. The most important of these stages is the mathematical modeling of the melting process, especially the physicochemical processes that take place during melting. All physicochemical processes controlled in order to obtain the required chemical composition of electric steel for modeling are combined into two main groups, such as metal refining and alloying. Possibilities of mathematical modeling of electric steel melting processes have been identified: to successfully solve different types of problems without conducting pro¬duc¬tion experiments; to ensure optimal modes of melting in specific production conditions. The problems to be solved by mathematical modeling have been identified: con¬struc¬tion of a model for specific conditions that allow to achieve the required value of any parameter of the solution; possibility to purchase electric steel in specific conditions; minimum cost of material, time, labor and energy and required chemical composition, temperature and mass melting of steel; automatic control of all parameters of the solution. To solve these problems, the characteristics of static, dynamic and mixed mathematical models have been identified. It has been shown that a deterministic mathematical model can be applied to a system of equations expressing the functional relationships between the parameters of the solution and the factors affecting them. The mixed mathematical model includes equations expressing functional correlations. This model is actually a deterministic static model. Depending on the problem, the expediency of using appropriate models in the melting of electric steel is justified. In general, the creation of a mathematical model of melting processes of electric steel includes: dividing the melting into elementary physicochemical processes within the limits of each period, ie decomposing the melting process; to give a quantitative description of each elementary process, ie to describe the process mathematically; write a mathematical model of each cycle of melting by combining the quantitative characteristics of the parameters and elementary processes controlled on the basis of the equations of material and heat balances; to obtain a mathematical model of the solution as a whole by combining mathematical models of different periods. As an example of the application of mathematical modeling in electroplating processes, the amount of pores formed during the steel melting process in the main braided electric arc furnace was calculated using scrap metal. Keywords: electroplating steel, mathematical modeling, static model, dynamic model, deterministic model, mixed model, functional relationships.