Journal articles: 'Control System Simulation'

1

Dexter, A. L. "Control system simulation — computer control." Energy and Buildings 10, no. 3 (January 1988): 203–11. http://dx.doi.org/10.1016/0378-7788(88)90006-0.

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Popova, O. V., Ya Yu Grigoriev, E. P. Zharikova, and A. L. Grigorieva. "LIGHTING CONTROL SYSTEM SIMULATION." Scholarly Notes of Komsomolsk-na-Amure State Technical University, no. 1 (2021): 10–18. http://dx.doi.org/10.17084/20764359_2021_49_10.

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Sovetov, B. Y., and S. A. Yakovlev. "ISDN Control System Simulation." IFAC Proceedings Volumes 21, no. 19 (June 1988): 241–44. http://dx.doi.org/10.1016/s1474-6670(17)54499-7.

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Costa Drumond Sousa, Gilberto, Bimal K. Bose, and Marcelo Godoy Simões. "A simulation-implementation methodology of a fuzzy logic based control system." Eletrônica de Potência 2, no. 1 (June 1, 1997): 61–68. http://dx.doi.org/10.18618/rep.1997.1.061068.

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Atherton, D. P. "Simulation in Control System Design." IFAC Proceedings Volumes 20, no. 12 (September 1987): 43–49. http://dx.doi.org/10.1016/s1474-6670(17)55604-9.

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Huang, Da Zhi. "Motorcycle's Road Simulation System Based on PID Control." Advanced Materials Research 749 (August 2013): 576–81. http://dx.doi.org/10.4028/www.scientific.net/amr.749.576.

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The application of motorcycles electro-hydraulic servo road simulation system is based on the modeling and the simulating. The model with the systems feature and parameter is finished,and analyzed the closed loop frequency characteristic of the system ,and proved that the simulation met the request of the fact. In the paper,the PID controller is designed based on the Matlab/Simulink. The system is controlled used in PID controller.The precision meet the request of the road status,and the system can reappear the stochastic waveform.

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Wang, Lei, and Xue Ling. "Coordination Control Model of Manufacturing System." Applied Mechanics and Materials 101-102 (September 2011): 800–803. http://dx.doi.org/10.4028/www.scientific.net/amm.101-102.800.

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This paper established a coordination control model for manufacturing system with three manufacturing cells. The step responses of the system under randomly selected control parameters and under empirically optimized control parameters were studied via simulations. Simulation results show that the proposed model coincides with the real-world system.

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Okolnishnikov, Victor, Sergey Rudometov, and Sergey Zhuravlev. "Simulation Environment for Development of Automated Process Control System in Coal Mining." International Journal of Energy 15 (November 21, 2021): 98–101. http://dx.doi.org/10.46300/91010.2021.15.15.

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This paper describes a visual interactive simulation system of technological processes intended for the development and execution of simulation and emulation models for automated process control systems in coal mining. A set of simulation models of various subsystems of a mine was developed with the help of this simulation system. These models united to create simulation environment. Simulation environment is visually interactive, include emulation models of technological equipment and allow simulating complex situations in mines and working faces. Simulation environment was used for testing of control programs executed in programmable logic controllers.

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Liang, Chen, and Jun Lin. "The Simulation Platform of Network Control System." Advanced Materials Research 562-564 (August 2012): 1434–37. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1434.

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With the development of electronic technology, computer and networked communication technology, control system is speeding up from close and centralized architecture to being open and distributed, to satisfy the continuous developing requirement of control quality and management. Networked control system is gradually formed in the process, and soon become one of the most important domains of control technique. One of the research of networked control systems is the effect of network upon control systems .A simulation platform of networked control systems is designed for this purpose. Matlab is used to build models of real systems. The simulation platform gives good environment for the analysis and design of networked control systems.

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Grigas, Vytautas, Alexandra Legha, Anatolijus Sulginas, and Rymantas Tadas Toločka. "Rowing Force Simulation and Control System." Solid State Phenomena 164 (June 2010): 161–64. http://dx.doi.org/10.4028/www.scientific.net/ssp.164.161.

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The rowing force simulation and control system is to be developed to generate the loading on the oar during the rowing stroke most adequate to the one acting during waterborne rowing. The paper presents the methodology of defining the law of change of loading on the oar by changing the cross-sectional area of flow channel of hydraulic loading unit at indoor rowing training facility. An example of the rowing force variation during one cycle obtained by using such a system is provided.

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Yau, Her-Terng, Maciej J. Ogorzalek, Frode Eika Sandnes, Cheng Siong Lee (Vincent), and Yunhua Li. "System Simulation and Control in Engineering." Mathematical Problems in Engineering 2014 (2014): 1. http://dx.doi.org/10.1155/2014/608237.

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Huang, Yue Hua, Guang Xu Li, and Huan Huan Li. "Wind Power System Optimization Control Simulation." Applied Mechanics and Materials 313-314 (March 2013): 817–20. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.817.

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This paper establishes the wind power system simulation model in Simulink/ Matlab environment. By adjusting the speed of variable speed wind turbine, the simulation model can keep running at the best operation condition, and then achieve maximum power transfer. In this process, this paper use PI controller to track and control the speed of wind turbine. Simulation results show that selecting the appropriate PI parameters can effectively track the speed and increase the efficiency of wind power generation system.

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Abramov, Ivan, Yuri Turygin, Ella Sosnovich, and Branislav Bako. "Control System Simulation of Technical Equipment." Procedia Engineering 149 (2016): 566–70. http://dx.doi.org/10.1016/j.proeng.2016.06.706.

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Kelly, George E. "Control system simulation in North America." Energy and Buildings 10, no. 3 (January 1988): 193–202. http://dx.doi.org/10.1016/0378-7788(88)90005-9.

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Kumar, M. Arun, S. Ashwin Kannan, A. Sathish Kumar, and S. Kumaravel. "Simulation of Corner Skidding Control System." International Journal of Advanced Engineering Research and Science 4, no. 3 (2017): 120–25. http://dx.doi.org/10.22161/ijaers.4.3.18.

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Jin, Guo Dong, Li Bin Lu, Juan Liang, and Xiao Fei Zhu. "Flight Control System for UAV Based on Simulink." Advanced Materials Research 709 (June 2013): 662–66. http://dx.doi.org/10.4028/www.scientific.net/amr.709.662.

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Most UAV flight control system simulation was used for evaluating the quality of UAV and designing it, however, the results failed to conform to actual flight laws. According to the need of UAV simulator in actually, a new way which was convenient for engineering computing and appropriated for simulation training was built for simulating flight control system. With the actual flight figures, flight parameters were modified by Least squares method curve fitting. The system offers a way for developing UAV simulation training system, thus of great value in both theory and practice.

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Pavlović, Tomislav, Ivan Župan, Viktor Šunde, and Željko Ban. "HIL Simulation of a Tram Regenerative Braking System." Electronics 10, no. 12 (June 9, 2021): 1379. http://dx.doi.org/10.3390/electronics10121379.

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Regenerative braking systems are an efficient way to increase the energy efficiency of electric rail vehicles. During the development phase, testing of a regenerative braking system in an electric vehicle is costly and potentially dangerous. For this reason, Hardware-In-the-Loop (HIL) simulation is a useful technique to conduct the system’s testing in real time where the physical parts of the system are replaced by simulation models. This paper presents a HIL simulation of a tram regenerative braking system performed on a scaled model. First, offline simulations are performed using a measured speed profile in order to validate the tram, supercapacitor, and power grid model, as well as the energy control algorithm. The results are then verified in the real-time HIL simulation in which the tram and power grid are emulated using a three-phase converter and LiFePO4 batteries. The energy flow control algorithm controls a three-phase converter which enables the control of energy flow within the regenerative braking system. The results validate the simulated regenerative braking system, making it applicable for implementation in a tram vehicle.

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Zhao, Si Jun, Jia Yua Shan, and Lu Yan Bi. "6-Axis Serial Robot Simulation Based on SimulationX." Applied Mechanics and Materials 152-154 (January 2012): 1010–17. http://dx.doi.org/10.4028/www.scientific.net/amm.152-154.1010.

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This paper presents research and simulation analysis on kinematics and dynamics problem based on the 6-axis serial robot. By means of Denavit-Hartenberg method, the robot kinematics model is established as well as and the derivation process of kinematic and inverse kinematic resolution is described in detail. Furthermore, in software simulationX, robot system model including mechanical sub-system and control sub-system are founded. Additionally, through simulation, different performances of robot are illustrated based on different trajectory planning and control. In this way a theoretical reference is provided for the further study on trajectory planning and controls of 6-axis serial robot.

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Chen, Feng, and Limin Chen. "Analysis and Simulation of Automatic Control System." Advanced Materials Research 143-144 (October 2010): 177–80. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.177.

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With the rapid development of modern science and technology, the importance that control theory will be applied to mechanical engineering is increasingly evident. Matlab is a high-performance language for technical computing and Simulink provides a graphical user interface for building models as block diagrams. This article analyzes the application of the simulation software in mechanical engineering by taking position control servo system of CNC machine tool for example. Matlab also provides a function for frequency domain analysis, which has some functions of directly computing frequency response of systems, also drawing of some typical frequency response curves of the control system, and greatly simplifies the process of frequency-domain analysis. System stability can be judged from system frequency characteristic curve. According to judgment of stability of the system, characteristics of the system is easy to be observed by the simulation results, it can greatly improve quality and reliability of the programming with a friendly graphical user interface.

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Dörr, Matthias, Felix Leitenberger, Kai Wolter, Sven Matthiesen, and Thomas Gwosch. "Model-Based Control Design of an EHA Position Control Based on Multicriteria Optimization." Machines 10, no. 12 (December 8, 2022): 1190. http://dx.doi.org/10.3390/machines10121190.

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For the control of dynamic systems such as an Electro-Hydraulic Actuator (EHA), there is a need to optimize the control based on simulations, since a prototype or a physical system is usually not available during system design. In consequence, no system identification can be performed. Therefore, it is unclear how well a simulation model of an EHA can be used for multicriteria optimization of the position control due to the uncertain model quality. To evaluate the suitability for control optimization, the EHA is modeled and parameterized as a grey-box model using existing parameters independent of test bench experiments. A method for multi-objective optimization of a controller is used to optimize the position control of the EHA. Finally, the step responses are compared with the test bench. The evaluation of the step responses for different loads and control parameters shows similar behavior between the simulation model and the physical system on the test bench, although the essential phenomena could not be reproduced. This means that the model quality achieved by modeling is suitable as an indication for the optimization of the control by simulation without a physical system.

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Sapaty, P. S. "Symbiosis of Distributed Simulation and Control under Spatial Grasp Technology." Mathematical machines and systems 3 (2020): 23–48. http://dx.doi.org/10.34121/1028-9763-2020-3-23-48.

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We are witnessing rapidly growing world dynamics caused by climate change, military, religious and ethnic conflicts, terrorism, refugee flows and weapons proliferation, political and industrial restructuring too. Dealing with frequently emerging crises may need rapid integration of scattered heterogeneous resources into capable operational forces pursuing goals which may not be known in advance. Proper understanding and managing of unpredictable and crisis situations may need their detailed simulation at runtime and even ahead of it. The current paper aims at deep integration, actually symbiosis, of advanced simulation with live system control and management, which can be effectively organized in nationwide and world scale. It will be presenting the latest version of Spatial Grasp Technology (SGT) which is not based on traditional communicating parts or agents, as usual, but rather using self-spreading, self-replicating, and self-modifying higher-level code covering and matching distributed systems at runtime while providing global integrity, goal-orientation, and finding effective solutions. These spatial solutions are often hundreds of times shorter and simpler than with other approaches due to special recursive scenario language hiding traditional system management routines inside its parallel and distributed interpretation. The paper provides basics for deep integration, actually symbiosis, of different worlds allowing us to unite advanced distributed simulation with spatial parallel and fully distributed control, while doing all this within the same high-level and very simple Spatial Grasp formalism and its basic Spatial Grasp Language (SGL). It will also mention various SGT applications including economy, ecology, space research & conquest and security, where effective symbiosis of distributed interactive simulation with live control and management may provide a real breakthrough. SGL can be quickly implemented even within standard university environments by a group of system programmers, similar to its previous versions in different countries under the author’s supervision. The technology can be installed in numerous copies worldwide and deeply integrated with any other systems, actually acquiring unlimited power throughout the world.

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Yi, Long Fang, Zhi Li, Jing Feng Mao, Ju Pin Gu, and Yu Jian Qiang. "Simulation of Hybrid Excited Doubly Salient Motor Driven Control System." Advanced Materials Research 317-319 (August 2011): 354–60. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.354.

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To research the performance of hybrid excited doubly salient motor (HEDS) driven system, a HEDS mathematical model is derived and the simulation driven system is built by Simulink/PSB environment. According to the principal of HEDS driven and its control strategies, several simulations under different operating modes are modeled. The results show that, HEDS driven system is of higher torque at low speed, and is of wide speed range in constant power operation. The simulation results are consistent with theoretical analysis; it shows that the simulation method is validity and effectiveness.

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Cheung, W. N. "Virtual Analogue Simulation of Control Systems." International Journal of Electrical Engineering & Education 34, no. 3 (July 1997): 223–34. http://dx.doi.org/10.1177/002072099703400304.

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This article describes the method of virtual analogue simulation (VAS) using PSpice for learning.control systems. Examples are included to demonstrate the application of VAS in studying system transient responses. The effects of controller compensation, component nonlinearity, and transport delays on system performance can be observed readily with VAS.

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Hoyos Velasco, Fredy Edimer, Camilo Younes Velosa, Eduardo Antonio Cano Plata, and Sebastián Sánchez Aristizábal. "Developing speed control for a permanent magnet DC motor using rapid control of prototyping techniques." Ingeniería e Investigación 30, no. 3 (September 1, 2010): 140–48. http://dx.doi.org/10.15446/ing.investig.v30n3.18186.

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Virtually every engineering development for control systems is tested by simulation to predict performance. However, the final use of an algorithm is in its application in a real time system. Development tools using a DSP and Simulink RTW can be performed with real-time simulations (i.e. simulation interacting with physical plant). Testing the speed control loop of a DC motor with permanent magnets has thus been developed to appreciate the considerable advantages offered by these tools.

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Sun, Ping, Shou Wei Hu, and Wei Wang. "Simulation on Networked Munitions System Control with Time Delay." Applied Mechanics and Materials 347-350 (August 2013): 11–14. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.11.

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Networked munitions system is becoming more and more important with the development of modern electronic technology. This new generation of ground-based munitions are much more intelligent for the capability they have to trace targets, communicate with each other and order a killing node for vehicles caught in the kill-zone . It is useful to study and attempt to model through simulations the interaction between enemy targets and the whole munitions system . This paper is to propose a simulation-based model to study the performances of the weapon system. Especially, the sensors which sense the environment and the weapons which attack the targets are connected with wireless networks that will bring time delay to the system. We focus our efforts to evaluate how time delays from wireless communication affect the control performance of the weapon. This study introduces models for sensors and communication routing mechanisms , as well as the servo system and vehicle targets. A series of simulation trials to analyze and compare the effects of time delays have on the killing efficiency. The results of the study indicate in general that communication delay have the most effect on the systems.

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Abu Khadra, Fayiz. "Tracking Control of Chaotic Systems via Optimized Active Disturbance Rejection Control." Mathematical Problems in Engineering 2018 (September 19, 2018): 1–10. http://dx.doi.org/10.1155/2018/4698953.

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For tracking control of chaotic systems, we develop an active disturbance rejection (ADR) control method. Using the first state of the system as the only available state, a time-varying bandwidth extended state observer reconstructs the remaining states and the total disturbance. A time-varying bandwidth feedback controller forces all the states of the system to follow exactly the reference signal and its derivative. The parameters of the ADR controller are optimized using a genetic algorithm. As the objective function, we chose the weighted sum of the integral of the absolute error and the integral of the absolute control signal. Two chaotic systems—the Duffing system and the Genesio–Tesi system—are considered in computer simulation tests. Results of these simulations are presented to demonstrate the effectiveness of the ADRC method in controlling chaotic systems.

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Yuan, Wei Guo, and Ya Bin Wang. "Verification of Control System by Physical Simulation." Applied Mechanics and Materials 472 (January 2014): 389–93. http://dx.doi.org/10.4028/www.scientific.net/amm.472.389.

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Control system is generally verified by experiment, some of which are too difficult or high-cost to implement, while physical simulation can be substituted for experiment to verify the effectiveness of control system. The study take the heat exchanger as an example to describe the process of verification by physical simulation: Choose FLOEFD and SIMULINK as the software of physical simulation and control system design, establish the mathematical model of heat exchanger, design control module, whose output is set as an input of FLOEFD, compare the result of physical simulation and SIMULINK, if they are similar, the control module is effective.

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Sun, Fang, and Peng Li. "Enterprise Logistics System and Simulation Control Technology." Applied Mechanics and Materials 548-549 (April 2014): 1994–97. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1994.

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By analyzing the domestic and foreign enterprise logistics system , a logistics simulation control system suitable for those general enterprises has been designed and developed. Simulation control system is in the form of adding plug-in units to the main program, speeding up processing tasks and improving flexibility of the system. Combining the characteristics and complex adaptability of the enterprises' logistics system, this article builds a simulation control model.

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Sato, Tadanobu, and Makoto Sato. "Hybrid Simulation System for Structural Control Experiments." Doboku Gakkai Ronbunshu, no. 577 (1997): 257–67. http://dx.doi.org/10.2208/jscej.1997.577_257.

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Klimenko, V., M. Michalevich, D. Leontiev, A. Yarita, and U. Ryabukha. "SIMULATION OF THE AUTOMATIC CLUTCH CONTROL SYSTEM." Automobile Transport, no. 41 (December 4, 2017): 49. http://dx.doi.org/10.30977/at.2219-8342.2017.41.0.49.

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Thompson, H. Paul. "Increasing Data Communications Control Through System Simulation." Journal of Information Systems Management 4, no. 1 (January 1987): 29–38. http://dx.doi.org/10.1080/07399018708962824.

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Morgan, C. "The Bilinear Transform in Control System Simulation." International Journal of Mechanical Engineering Education 22, no. 4 (October 1994): 283–93. http://dx.doi.org/10.1177/030641909402200406.

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Digital computer simulation of a continuous control system furnishes the designer with a convenient means of assessing the effects of control strategies and can also provide an independent check on the transient and frequency responses obtained from a theoretical analysis. Although commercial packages are widely available, it is instructive to build an initial simulation using a discrete transfer function representation of the dynamic elements. The bilinear transform can be used to derive a suitable transfer function, with the necessary algebraic manipulations being performed by the computer. The method produces an inherently flexible simulation and is equally applicable to linear and nonlinear systems.

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Gerdes, J. Christian, and J. Karl Hedrick. "Brake System Modeling for Simulation and Control." Journal of Dynamic Systems, Measurement, and Control 121, no. 3 (September 1, 1999): 496–503. http://dx.doi.org/10.1115/1.2802501.

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This paper presents reduced-order models of brake system dynamics derived from a physical modeling perspective. The vacuum booster model combines a static control valve with dynamic air flows, resulting in the ability to easily reproduce both static hysteresis effects and rapid transients. Following the assumption of incompressible flow, a four-state model of the brake hydraulics is presented and, subsequently, reduced to one or two states for certain applications. Experimental results support the simplifying assumptions made during the modeling process by demonstrating better agreement with the response from pedal force to brake pressure than previously displayed in the literature. These models are intended for use in the design and analysis of vehicle control systems and the evaluation of driver/vehicle interactions through dynamic simulation.

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Kom, Granino A. "Simulation of a Fuzzy-Logic Control System." SIMULATION 61, no. 4 (October 1993): 244–49. http://dx.doi.org/10.1177/003754979306100405.

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Wang, Zhong Chu, Yuan Ding, Hong Yang Zhao, and Xiao Dong Hu. "Coiling Tension Control System of PID Simulation." Advanced Materials Research 989-994 (July 2014): 3019–24. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3019.

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Coiling tension control system is the main control means to ensure the quality of the rolling process. According to the characteristics of coiling tension control characteristics and the requirements of actual engineering, establish the mathematical model of the double closed loop control system and use the traditional PID to tuning the model parameters. It is proved by Matlab simulation experiments that traditional PID can effectively improve the response process of coiling tension control system and greatly enhance the dynamic and steady-state performance.

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Dulau, Mircea, and Dorin Bica. "Simulation of Speed Steam Turbine Control System." Procedia Technology 12 (2014): 716–22. http://dx.doi.org/10.1016/j.protcy.2013.12.554.

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Jennions, Ian, Fakhre Ali, Manuel Esperon Miguez, and Ignacio Camacho Escobar. "Simulation of an aircraft environmental control system." Applied Thermal Engineering 172 (May 2020): 114925. http://dx.doi.org/10.1016/j.applthermaleng.2020.114925.

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Björkbom, Mikael, Shekar Nethi, Lasse M. Eriksson, and Riku Jäntti. "Wireless control system design and co-simulation." Control Engineering Practice 19, no. 9 (September 2011): 1075–86. http://dx.doi.org/10.1016/j.conengprac.2011.05.012.

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Takeuchi, Akihiro, Minoru Hirose, Atsushi Hamada, and Noriaki Ikeda. "Simulation system of arrhythmia using ActiveX control." Computer Methods and Programs in Biomedicine 79, no. 1 (July 2005): 49–57. http://dx.doi.org/10.1016/j.cmpb.2005.03.011.

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Zhukov, Nikolay. "SIMULATION MODELING OF ACTIVE FILTERS CONTROL SYSTEM." Electrotechnical Systems and Complexes, no. 4(37) (December 26, 2017): 27–31. http://dx.doi.org/10.18503/2311-8318-2017-4(37)-27-31.

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Zhang, Lei, and Shi Tian Yan. "Simulation and Control System of Camera Robot." Applied Mechanics and Materials 128-129 (October 2011): 1230–33. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1230.

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This paper presents simulation method for a new camera robot system. A special mechanism of 7 DOF which allow the robot to work flexibly in variable workspace is designed. The performance of the proposed mechanism is verified reasonable by simulation in Matlab. Additional, control system for the camera robot is presented in this paper.

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Souza Lima, E. E., and L. F. De Jesus Fernandes. "Assessing eigenvalue sensitivities [power system control simulation]." IEEE Transactions on Power Systems 15, no. 1 (2000): 299–306. http://dx.doi.org/10.1109/59.852136.

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Walker, M. L., G. Ambrosino, G. De Tommasi, D. A. Humphreys, M. Mattei, G. Neu, C. J. Rapson, et al. "The ITER Plasma Control System Simulation Platform." Fusion Engineering and Design 96-97 (October 2015): 716–19. http://dx.doi.org/10.1016/j.fusengdes.2015.01.009.

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Girard, Antoine, and George J. Pappas. "Hierarchical control system design using approximate simulation." Automatica 45, no. 2 (February 2009): 566–71. http://dx.doi.org/10.1016/j.automatica.2008.09.016.

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Shahmaliyev, Mammad. "Simulation Model of Infinite Perishable Queueing Inventory System with Feedback." Upravlâûŝie sistemy i mašiny, no. 3 (275) (November 20, 2018): 60–68. http://dx.doi.org/10.15407/usim.2018.03.060.

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Liang, Yan-Jun, and Shi-Liang Wu. "Optimal Vibration Control for Tracked Vehicle Suspension Systems." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/178354.

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Technique of optimal vibration control with exponential decay rate and simulation for vehicle active suspension systems is developed. Mechanical model and dynamic system for a class of tracked vehicle suspension vibration control is established and the corresponding system of state space form is described. In order to prolong the working life of suspension system and improve ride comfort, based on the active suspension vibration control devices and using optimal control approach, an optimal vibration controller with exponential decay rate is designed. Numerical simulations are carried out, and the control effects of the ordinary optimal controller and the proposed controller are compared. Numerical simulation results illustrate the effectiveness of the proposed technique.

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Lucas, David E., Pitu B. Mirchandani, and K. Larry Head. "Remote Simulation to Evaluate Real-Time Traffic Control Strategies." Transportation Research Record: Journal of the Transportation Research Board 1727, no. 1 (January 2000): 95–100. http://dx.doi.org/10.3141/1727-12.

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Simulation is a valuable tool for evaluating the effects of various changes in a transportation system. This is especially true in the case of real-time traffic-adaptive control systems, which must undergo extensive testing in a laboratory setting before being implemented in a field environment. Various types of simulation environments are available, from software-only to hardware-in-the-loop simulations, each of which has a role to play in the implementation of a traffic control system. The RHODES (real-time hierarchical optimized distributed effective system) real-time traffic-adaptive control system was followed as it progressed from a laboratory project toward actual field implementation. The traditional software-only simulation environment and extensions to a hardware-in-the-loop simulation are presented in describing the migration of RHODES onto the traffic controller hardware itself. In addition, a new enhancement to the standard software-only simulation that allows remote access is described. The enhancement removes the requirement that both the simulation and the traffic control scheme reside locally. This architecture is capable of supporting any traffic simulation package that satisfies specific input-output data requirements. This remote simulation environment was tested with several different types of networks and was found to perform in the same manner as its local counterpart. Remote simulation has all of the advantages of its local counterpart, such as control and flexibility, with the added benefit of distribution. This remote environment could be used in many different ways and by different groups or individuals, including state or local transportation agencies interested in performing their own evaluations of alternative traffic control systems.

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Tóthová, Mária, Milan Balara, and Ján Dubják. "Simulation Model of Cascade Control of the Heating System." International Journal of Engineering Research in Africa 18 (October 2015): 20–27. http://dx.doi.org/10.4028/www.scientific.net/jera.18.20.

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The automatic heating control systems seem to be the nonlinear systems with thermal inertias and time delay. The controller is also nonlinear because its information and power signals are limited. Application of methods, which are available for nonlinear systems together with computer simulation and mathematical modeling, create possibility to reach serious information about researched system. The paper contains the heating system model with the cascade control, simulation model of this system and some simulation results created in Matlab/Simulink environment.

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Cichon, Torben, Marc Priggemeyer, and Jürgen Rossmann. "Simulation-Based Control and Simulation-Based Support in eRobotics Applications." Applied Mechanics and Materials 840 (June 2016): 74–81. http://dx.doi.org/10.4028/www.scientific.net/amm.840.74.

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The utilization of simulation capabilities in the development process of robotic systems is already known as one standard procedure for predicting complex system behavior in a time- and cost efficient manner. eRobotics join multiple process simulation components to build "Virtual Testbeds" to provide a comprehensive tool chain and thus a holistic development. VTB may represent "mental models" of robotic systems and their environment. Therefore, they allow the development of control schemes and directly transfer simulation results for Simulation-based Control for implementing intelligent robot controls. Using Simulation-based Support, the VTBs support the ease of use of robotic systems and also the operators in their decisions. Offering an additional abstraction layer for the user, virtual representations of the robot and its environment are used to intuitively control and maneuver intelligent robotic systems. Thus, Simulation-based Control and Simulation-based Support complement each other and are promising development tools for robotic systems, individual parts thereof as well as systems in their entirety. In our contribution, we present the concepts of SbC and SbS in more detail, by examples of several complex robotic systems such as a Motion Simulator, lightweight robots and a mobile Centaur-like teleoperated robot.

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MIN, BYUNG-KWON, ZHENGDONG HUANG, ZBIGNIEW J. PASEK, DEREK YIP-HOI, FORBES HUSTED, and STEPHEN MARKER. "INTEGRATION OF REAL-TIME CONTROL SIMULATION TO A VIRTUAL MANUFACTURING ENVIRONMENT." Journal of Advanced Manufacturing Systems 01, no. 01 (June 2002): 67–87. http://dx.doi.org/10.1142/s0219686702000076.

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This paper presents a new integrated approach for simulation developed to improve the accuracy of virtual manufacturing environments. While machine tool simulation and virtual manufacturing for factory simulation have been frequently used in early stage plant development, each of these technique has been researched and implemented separately. This paper focuses on the utilization of real-time simulation of machine tools or active axes in manufacturing systems and integration of this simulation capability with virtual manufacturing environments. Machine-level simulation results are generated in real-time with a real machine tool controller and are fed to a virtual manufacturing environment. To integrate these two simulation techniques, system-level software is utilized as a communication platform. This system-level software was originally developed to control and configure whole manufacturing systems. The method has been successfully implemented within a testbed with full-scale machine tools. The results demonstrate that the proposed method advances the virtual manufacturing environments toward improved accuracy of factory level simulation, reduced effort for modeling and expanded functionality of machine-level simulations.

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Journal articles on the topic 'Control System Simulation'

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