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Journal articles on the topic 'Model predictive controller (MPC)'

Author: Grafiati

Published: 25 May 2024

Last updated: 25 July 2025

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1

Rezaee, Alireza. "Model predictive Controller for Mobile Robot." Transactions on Environment and Electrical Engineering 2, no. 2 (2017): 18. http://dx.doi.org/10.22149/teee.v2i2.96.

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This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is implemented on a real robot. The comparison between a PID controller, adaptive controller, and the MPC

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Tesfaye, Alamirew, Balaji V., and Gabbeye Nigus. "Comparison of PID Controller with Model Predictive Controller for Milk Pasteurization Process." Bulletin of Electrical Engineering and Informatics 6, no. 1 (2017): 24–35. https://doi.org/10.11591/eei.v6i1.575.

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Proportional-Integral-Derivative (PID) controllers are used in many of the Industries for various process control applications. PID controller yields a long settling time and overshoot which is not good for the process control applications. PID is not suitable for many of the complex process control applications. This research paper is about developing a better type of controller, known as MPC (Model Predictive Control). The aim of the paper is to design MPC and PID for a pasteurization process. In this manuscript comparison of PID controller with MPC is made and the responses are presented. M

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Alasali, Feras, Stephen Haben, Husam Foudeh, and William Holderbaum. "A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads." Energies 13, no. 10 (2020): 2596. http://dx.doi.org/10.3390/en13102596.

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This paper aims to present the significance of predicting stochastic loads to improve the performance of a low voltage (LV) network with an energy storage system (ESS) by employing several optimal energy controllers. Considering the highly stochastic behaviour that rubber tyre gantry (RTG) cranes demand, this study develops and compares optimal energy controllers based on a model predictive controller (MPC) with a rolling point forecast model and a stochastic model predictive controller (SMPC) based on a stochastic prediction demand model as potentially suitable approaches to minimise the impa

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Wahid, Abdul, and Richi Adi. "MODELING AND CONTROL OF MULTIVARIABLE DISTILLATION COLUMN USING MODEL PREDICTIVE CONTROL USING UNISIM." SINERGI 20, no. 1 (2016): 14. http://dx.doi.org/10.22441/sinergi.2016.1.003.

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Distillation columns are widely used in chemical industry as unit operation and required advance process control because it has multi input multi output (MIMO) or multi-variable system, which is hard to be controlled. Model predictive control (MPC) is one of alternative controller developed for MIMO system due to loops interaction to be controlled. This study aimed to obtain dynamic model of process control on a distillation column using MPC, and to get the optimum performance of MPC controller. Process control in distillation columns performed by simulating the dynamic models of distillation

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Xu, Ying, Wentao Tang, Biyun Chen, Li Qiu, and Rong Yang. "A Model Predictive Control with Preview-Follower Theory Algorithm for Trajectory Tracking Control in Autonomous Vehicles." Symmetry 13, no. 3 (2021): 381. http://dx.doi.org/10.3390/sym13030381.

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Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the

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Kümpel, Alexander, Phillip Stoffel, and Dirk Müller. "Self-adjusting model predictive control for modular subsystems in HVAC systems." Journal of Physics: Conference Series 2042, no. 1 (2021): 012037. http://dx.doi.org/10.1088/1742-6596/2042/1/012037.

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Abstract In order to reduce the energy consumption and CO2 emissions in the building sector, an efficient control strategy, such as model predictive control (MPC) is required. However, MPC is rarely applied in buildings since the implementation and modeling is complex, time consuming and costly. To bring MPC into practice, controllers and models are needed, that automatically adapt their behavior to the controlled system. In this work, such a self-adjusting MPC applicable to heating, ventilation and air-conditioning (HVAC) systems is developed. The MPC is based on a simple grey-box model that

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Srikshana, Sasidaran, R. Adithya, Raja V. Haris, and M.P.Anbarasi. "Recent Trends in Model Predictive Control." International Journal of Innovative Science and Research Technology 7, no. 2 (2022): 249–54. https://doi.org/10.5281/zenodo.6323081.

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In this paper we are going to present the recent trends of model predictive control (MPC) and its techniques are used in modern world. MPC forecasts plant output behavior using a plant model. The MPC controller solves the optimization problem across the prediction horizon while adhering to the constraints at the current phase. This can be used in non-linear problems and it is more precise when compare to the linear controller such as PID.

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Kumavat, Mayur, and Sushil Thale. "Analysis of CSTR Temperature Control with PID, MPC & Hybrid MPC-PID Controller." ITM Web of Conferences 44 (2022): 01001. http://dx.doi.org/10.1051/itmconf/20224401001.

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This paper presents an analysis of the continuous stirred tank reactor (CSTR) temperature control with the Proportional-Integral-Derivative (PID) Controller, Model Predictive Controller (MPC) and Hybrid-Model Predictive Controller-Proportional Integral Derivative Controller (MPC-PID). It is the main goal of this project to find a suitable improvement strategy for the system’s stability and accuracy to be more stable. By creating a model, the control system is implemented for all the above mentioned control methods and so comparative analysis is carried out to find the best control method for C

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Chrif, Labane, and Zemalache Meguenni Kadda. "Aircraft Control System Using Model Predictive Controller." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (2015): 259. http://dx.doi.org/10.11591/tijee.v15i2.1538.

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This paper concerns the application of model-based predictive control to the longitudinal and lateral mode of an aircraft in a terrain following task. The predictive control approach was based on a quadratic cost function and a linear state space prediction model with input and state constraints. The optimal control was obtained as the solution of a quadratic programming problem defined over a receding horizon. Closed-loop simulations were carried out by using the linear aircraft model. This project thesis provides a brief overview of Model Predictive Control (MPC).A brief history of industria

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Vrečko, D., N. Hvala, and M. Stražar. "The application of model predictive control of ammonia nitrogen in an activated sludge process." Water Science and Technology 64, no. 5 (2011): 1115–21. http://dx.doi.org/10.2166/wst.2011.477.

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In this paper a model predictive controller (MPC) for ammonia nitrogen is presented and evaluated in a real activated sludge process. A reduced nonlinear mathematical model based on mass balances is used to model the ammonia nitrogen in the activated sludge plant. An MPC algorithm that minimises only the control error at the end of the prediction interval is applied. The results of the ammonia MPC were compared with the results of the ammonia feedforward-PI and ammonia PI controllers from our previous study. The ammonia MPC and ammonia feedforward-PI controller give better results in terms of

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Munoz, Samuel Arce, Junho Park, Cristina M. Stewart, Adam M. Martin, and John D. Hedengren. "Deep Transfer Learning for Approximate Model Predictive Control." Processes 11, no. 1 (2023): 197. http://dx.doi.org/10.3390/pr11010197.

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Transfer learning is a machine learning technique that takes a pre-trained model that has already been trained on a related task, and adapts it for use on a new, related task. This is particularly useful in the context of model predictive control (MPC), where deep transfer learning is used to improve the training of the MPC by leveraging the knowledge gained from related controllers. One way in which transfer learning is applied in the context of MPC is by using a pre-trained deep learning model of the MPC, and then fine-tuning the controller training for a new process automation task. This is

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Lio, Wai Hou, John Anthony Rossiter, and Bryn Llywelyn Jones. "Modular Model Predictive Control upon an Existing Controller." Processes 8, no. 7 (2020): 855. http://dx.doi.org/10.3390/pr8070855.

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The availability of predictions of future system inputs has motivated research into preview control to improve set-point tracking and disturbance rejection beyond that achievable via conventional feedback control. The design of preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, is often performed in a monolithic nature, coupling the feedback and feed-forward problems. This can create problems, such as: (i) an additional feedback loop is introduced by MPC, which alters the closed-loop dynamics of the existing feedback compensator, po

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Yu, Xingji, Laurent Georges, and Lars Imsland. "Adaptive Linear Grey-Box Models for Model Predictive Controller of Residential Buildings." E3S Web of Conferences 362 (2022): 12001. http://dx.doi.org/10.1051/e3sconf/202236212001.

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Model predictive control (MPC) is an advanced optimal control technique to minimize a control objective while satisfying a set of constraints and is well suited to activate the building energy flexibility. The MPC controller performance depends on the accuracy of the model prediction. Inaccurate predictions can directly lead to low control performance. Linear time-invariant (LTI) models are often used in MPC in buildings. However, LTI models do not adapt to the weather conditions varying throughout the whole space-heating season, which makes the MPC based on LTI models not perform well over a

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Huynh, Phuc-Hoang, Minh-Hanh Nguyen, Nguyen-Phat Pham, et al. "Model Predictive Control for Rotary Inverted Pendulum: Simulation and Experiment." Journal of Fuzzy Systems and Control 2, no. 3 (2024): 215–22. https://doi.org/10.59247/jfsc.v2i3.263.

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Rotary Inverted Pendulum (RIP) is one of the simplest nonlinear systems commonly used for validating control algorithms. In this study, two controllers, Model Predictive Control (MPC) and Linear Quadratic Regulation (LQR), are simulated and experimentally validated. These controllers are executed in real-time on a PC, while the STM32F407 chip handles control and data acquisition from the pendulum using a high-speed USB interface. Due to the custom-built nature of this model, there are inaccuracies in the model and parameter identification. However, results show that the MPC controller is bette

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Hu, Dawei, Gangyan Li, and Feng Deng. "Gain-Scheduled Model Predictive Control for a Commercial Vehicle Air Brake System." Processes 9, no. 5 (2021): 899. http://dx.doi.org/10.3390/pr9050899.

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This paper presents a control-oriented Linear Parameter-Varying (LPV) model for commercial vehicle air brake systems with the electro-pneumatic proportional valve based on the nonlinear mathematical model, a set of discrete-time linearized models at different target pressures with the q-Markov Cover system identification method. The scheduled parameters for the LPV model were the brake chamber pressure, which was controlled by the electro-pneumatic proportional valve. On the basis of the LPV model, a family of Model Predictive Control (MPC) controllers with a Kalman filter was designed at each

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Nabil, Farah, H. N. Talib M., Ibrahim Z., et al. "Analysis and investigation of different advanced control strategies for high-performance induction motor drives." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 6 (2020): 3303~3314. https://doi.org/10.12928/TELKOMNIKA.v18i6.15342.

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Induction motor (IM) drives have received a strong interest from researchers and industry particularly for high-performance AC drives through vector control method. With the advancement in power electronics and digital signal processing (DSP), high capability processors allow the implementation of advanced control techniques for motor drives such as model predictive control (MPC). In this paper, design, analysis and investigation of two different MPC techniques applied to IM drives; the model predictive torque control (MPTC) and model predictive current control (MPCC) are presented. The two te

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Ilyas, Khelafa, Baghdad Abdenaceur, and El Hachimi Mohamed. "Improving model predictive control's optimization for urban traffic." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1367–74. https://doi.org/10.11591/ijeecs.v25.i3.pp1367-1374.

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When it comes to decreasing traffic congestion and enhancing mobility, traffic forecasting is critical. However, due to the complicated spatiotemporal dynamics of urban transportation networks, which are difficult to describe, this task is tough. Using a model predictive controller (MPC) provides the control of a traffic network's architecture as well as errors in its operations. Based on a real-time simulation, a novel, accurate prediction controller for urban traffic was presented in this study to estimate the number of cars at junctions and their waiting duration. Different optimization

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Hussain, Shafquat, Abualkasim Bakeer, Ihab S. Mohamed, Mario Marchesoni, and Luis Vaccaro. "Comparative Study of Passivity, Model Predictive, and Passivity-Based Model Predictive Controllers in Uninterruptible Power Supply Applications." Energies 16, no. 15 (2023): 5594. http://dx.doi.org/10.3390/en16155594.

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Voltage source converters are widely used in distributed generation (DG) and uninterruptible power supply (UPS) applications. This paper aims to find the controller that performs best when model changes occur in the system, showing insensitivity to parameter variations. A comparison of the finite control set model predictive controller (FCS-MPC), interconnection and damping assignment passivity-based controller (IDA-PBC), and passivity-based model predictive control (PB-MPC) reveals that the PB-MPC provides high resistance to these unexpected LC filter changes in the converter. The second aim

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19

Siti, Fatimah Sulaiman, F. Rahmat M., Athif Faudzi Ahmad, et al. "Pneumatic positioning control system using constrained model predictive controller: Experimental repeatability test." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 3913–23. https://doi.org/10.11591/ijece.v11i5.pp3913-3923.

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Most of the controllers that were proposed to control the pneumatic positioning system did not consider the limitations or constraints of the system in their algorithms. Non-compliance with the prescribed system constraints may damage the pneumatic components and adversely affect its positioning accuracy, especially when the system is controlled in real-time environment. Model predictive controller (MPC) is one of the predictive controllers that is able to consider the constraint of the system in its algorithm. Therefore, constrained MPC (CMPC) was proposed in this study to improve the accurac

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Khalil.Mohamed, Mahdy Ahmed.El, and Mohamed.Refai. "MODEL PREDICTIVE CONTROL USING FPGA." International Journal of Control Theory and Computer Modeling (IJCTCM) 5, no. 2 (2015): 01–14. https://doi.org/10.5121/ijctcm.2015.5201.

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Model predictive control (MPC) is an advanced control algorithm that has been very successful in the control industries due to its capability of handling multi input multi output (MIMO) systems with physical constraints. In MPC, the control action are obtained by solving a constrained optimization problem at every sample interval to minimize the difference between the predicted outputs and the reference value through the using of minimum control energy and satisfying the constraints of the physical system. Quadratic programing (QP) problem is solved using QPKWIK method which improves the activ

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Zhang, Kanghua, Jixin Wang, Xueting Xin, et al. "A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms." Applied Sciences 12, no. 4 (2022): 1995. http://dx.doi.org/10.3390/app12041995.

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The learning-based model predictive control (LB-MPC) is an effective and critical method to solve the path tracking problem in mobile platforms under uncertain disturbances. It is well known that the machine learning (ML) methods use the historical and real-time measurement data to build data-driven prediction models. The model predictive control (MPC) provides an integrated solution for control systems with interactive variables, complex dynamics, and various constraints. The LB-MPC combines the advantages of ML and MPC. In this work, the LB-MPC technique is summarized, and the application of

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Alamirew, Tesfaye, V. Balaji, and Nigus Gabbeye. "Comparison of PID Controller with Model Predictive Controller for Milk Pasteurization Process." Bulletin of Electrical Engineering and Informatics 6, no. 1 (2017): 24–35. http://dx.doi.org/10.11591/eei.v6i1.575.

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Proportional–Integral–Derivative (PID) controllers are used in many of the Industries for various process control applications. PID controller yields a long settling time and overshoot which is not good for the process control applications. PID is not suitable for many of the complex process control applications. This research paper is about developing a better type of controller, known as MPC (Model Predictive Control). The aim of the paper is to design MPC and PID for a pasteurization process. In this manuscript comparison of PID controller with MPC is made and the responses are presented. M

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Vroemen, B. G., H. A. van Essen, A. A. van Steenhoven, and J. J. Kok. "Nonlinear Model Predictive Control of a Laboratory Gas Turbine Installation." Journal of Engineering for Gas Turbines and Power 121, no. 4 (1999): 629–34. http://dx.doi.org/10.1115/1.2818518.

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The feasibility of model predictive control (MPC) applied to a laboratory gas turbine installation is investigated. MPC explicitly incorporates (input and output) constraints in its optimizations, which explains the choice for this computationally demanding control strategy. Strong nonlinearities, displayed by the gas turbine installation, cannot always be handled adequately by standard linear MPC. Therefore, we resort to nonlinear methods, based on successive linearization and nonlinear prediction as well as the combination of these. We implement these methods, using a nonlinear model of the

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Shamraev, A. D., and S. A. Kolyubin. "Bioinspired and Energy-Efficient Convex Model Predictive Control for a Quadruped Robot." Nelineinaya Dinamika 18, no. 5 (2022): 0. http://dx.doi.org/10.20537/nd221214.

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Animal running has been studied for a long time, but until now robots cannot repeat the same movements with energy efficiency close to animals. There are many controllers for controlling the movement of four-legged robots. One of the most popular is the convex MPC. This paper presents a bioinspirational approach to increasing the energy efficiency of the state-of-the-art convex MPC controller. This approach is to set a reference trajectory for the convex MPC in the form of an SLIP model, which describes the movements of animals when running. Adding an SLIP trajectory increases the energy effic

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Sokolov, Vladimir, Oleg Krol, Vladislav Andriichuk, Irina Chernikova, and Tatiana Shevtsova. "Improvement of HVAC systems based on adaptive predictive control." E3S Web of Conferences 420 (2023): 07020. http://dx.doi.org/10.1051/e3sconf/202342007020.

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The paper considers the issue of approbation of adaptive predictive control for heating, ventilation and air conditioning systems, shows the possibility of improving the regulation processes by its application on example of ventilation system. The idea of control using predictive model is presented, the principles of control using MPC controller are noted, the controller structure and the criterion for choosing the optimal values of control signal are considered. The feature of adaptive predictive control is the presence of the mathematical model for control object, which accurately describes

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Y.Swathi*, Dr.R.Kiranmayi K.Venu Gopal Reddy. "ANN BASED MODEL PREDICTIVE CONTROL OF SINGLE PHASE GRID CONNECTED PHOTOVOLTAIC SYSTEM USING MPPT TECHNIQUE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 407–19. https://doi.org/10.5281/zenodo.556321.

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Nowadays solar energy has great importance. Because it is easily available resource for energy generation. But the only problem is efficiency of solar system. And to increase its efficiency many MPPT techniques are available for use. Incremental conductance (INC) method is one of those well known MPPT techniques. Development of INC method using two-step model predictive control by employing artificial neural network (ANN) is the main contribution of this paper. The multilevel inverter controller is based on fixed step current predictive control with small ripples and low total harmonic distort

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Gounder, Yasoda Kailasa, and Sowkarthika Subramanian. "Application of machine learning controller in matrix converter based on model predictive control algorithm." International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no. 3 (2023): 1489. http://dx.doi.org/10.11591/ijpeds.v14.i3.pp1489-1496.

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Finite control set model predictive control (FCS-MPC) algorithms are famous in power converter for its easy implementation of constraints with cost function than classical control algortihms. However computation complexity increases when swicthing state is high for converters such as matrix converter, multilevel converters and this impose a serious drawback to compute multi-step prediction horizon MPC algorithm which further increases the computation. To overcome the above said difficulty, machine learning based artificial neural network (ANN) controller for matrix converter is proposed. The t

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Giraldo, Sergio A. C., Príamo A. Melo, and Argimiro R. Secchi. "Tuning of Model Predictive Controllers Based on Hybrid Optimization." Processes 10, no. 2 (2022): 351. http://dx.doi.org/10.3390/pr10020351.

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A tuning procedure for a model predictive controller (MPC) is presented for multi-input multi-output systems. The approach consists of two steps based on a hybrid method: the goal attainment method and a variable neighborhood search. In the first step, the weights of the MPC objective function are obtained, minimizing the square error between the closed-loop response of the internal controller model and a predefined desired reference trajectory. In the second step, the integer variables of the problem (prediction and control horizons) are obtained, minimizing the square error between the close

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Zhang, Qing, Chi Zhang, Qi Wang, Shiyun Dong, and Aoqi Xiao. "Research on Simplified Design of Model Predictive Control." Actuators 14, no. 4 (2025): 191. https://doi.org/10.3390/act14040191.

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PID controllers have been dominant in the field of process control for a long time, but their control quality is not ideal and the difficulty of parameter tuning has always been a problem. MPCs have good control quality and robustness, but due to the complexity of the algorithm, most are limited to software on PC machines. Although there are examples of implementations on hardware, they are restricted to specific scenarios and are of an experimental nature. The barriers to application and maintenance are high, and therefore, it has not become as popular as PID. The common self-balancing indust

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Rezaee, Alireza. "Controlling of Mobile Robot by Using of Predictive Controller." IAES International Journal of Robotics and Automation (IJRA) 6, no. 3 (2017): 207. http://dx.doi.org/10.11591/ijra.v6i3.pp207-215.

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In this paper implementation of Model Predictive<br />Controller on mobile robot was explained. The conducted<br />experiments show effectiveness of the proposed method on<br />control of the mobile robot. Furthermore the effects of the model<br />parameters such as control horizon, prediction horizon,<br />weighting factor and signal filter band on the controller<br />performance were studied. Finally, a comparison between the<br />designed MPC controller and PID and adaptive controllers was<br />presented demonstrating superior performance of t

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Perez, J. M., D. Odloak, and E. L. Lima. "Robust MPC with Output Feedback of Integrating Systems." Journal of Control Science and Engineering 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/265808.

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In this work, it is presented a new contribution to the design of a robust MPC with output feedback, input constraints, and uncertain model. Multivariable predictive controllers have been used in industry to reduce the variability of the process output and to allow the operation of the system near to the constraints, where it is usually located the optimum operating point. For this reason, new controllers have been developed with the objective of achieving better performance, simpler control structure, and robustness with respect to model uncertainty. In this work, it is proposed a model predi

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Kardos, Tamás, and Dénes Nimród Kutasi. "Model-based Predictive Control of an HVAC System." Műszaki Tudományos Közlemények 11, no. 1 (2019): 101–4. http://dx.doi.org/10.33894/mtk-2019.11.21.

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Abstract This paper presents the application of two model-based predictive control (MPC) algorithms on the cooling system of an office building. The two strategies discussed are a simple MPC, and an adaptive MPC algorithm connected to a model predictor. The cooling method used represents the air-conditioning unit of an HVAC system. The temperature of the building’s three rooms is controlled with fan coil units, based on the reference temperature and with different constraints applied. Furthermore, the building model is affected by dynamically changing interior and exterior heat sources, which

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Domina, Ádám, and Viktor Tihanyi. "Model Predictive Controller Approach for Automated Vehicle’s Path Tracking." Sensors 23, no. 15 (2023): 6862. http://dx.doi.org/10.3390/s23156862.

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In this paper, a model predictive control (MPC) approach for controlling automated vehicle steering during path tracking is presented. A (linear parameter-varying) LPV vehicle plant model including steering dynamics is proposed to determine the system evolution matrices. The steering dynamics are modeled in two different ways by using first-order lag and a second-order lag; the application of the first-order system resulted in a slightly more accurate path-following. Additionally, a cascade MPC structure is applied in which two MPCs are used; the second-order steering dynamics are separated fr

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Kolodin, Aleksey A., and Viktor V. Elshin. "Research and development of the controller based on the model predictive control." Vestnik of Samara State Technical University. Technical Sciences Series 29, no. 1 (2021): 36–45. http://dx.doi.org/10.14498//tech.2021.1.3.

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Modern automated process control systems that use programmable logic controllers use software controllers based on the proportional integral-differential control law, the PID controller. In most cases, this regulator is sufficient for conducting the technological process. It has high performance with configurable and sufficient quality of regulation. However, using a PID controller for non-linear, poorly defined, multi-connected objects with a long delay time can lead to unstable control quality indicators, accumulation of errors, and ultimately to a deterioration in product quality. One of th

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Fop, Davide, Ali Reza Yaghoubi, and Alfonso Capozzoli. "Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator." Energies 17, no. 20 (2024): 5117. http://dx.doi.org/10.3390/en17205117.

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In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a tim

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Xia, Jiyu, and Zhou Zhou. "Model Predictive Control Based on ILQR for Tilt-Propulsion UAV." Aerospace 9, no. 11 (2022): 688. http://dx.doi.org/10.3390/aerospace9110688.

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The transition flight of tilt-propulsion UAV is a complex and time-varying process, which leads to great challenges in the design of a stable and robust controller. This work presents a unified model predictive controller, which can handle the full envelope from vertical take-off and landing to cruise flight, to mean that the UAV can achieve a near-optimal transition flight under uncertainty conditions. Firstly, the nonlinear dynamic model of the tilt-propulsion UAV is developed, in which the aerodynamic/propulsion coupling effect of the ducted propeller is considered. Then, a control framewor

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Vo, Thanh Ha, and Thi Giang Pham. "Control for induction motor drives using predictive model stator currents and speeds control." International Journal of Power Electronics and Drive Systems 13, no. 4 (2022): 2005~2013. https://doi.org/10.11591/ijpeds.v13.i4.pp2005-2013.

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This paper is presented for designing a new controller using the predictive model current and speed control method for the asynchronous motor. This control method is based on traditional predictive controller development to have a cascade structure similar to the rotor flux control (field-oriented control) and direct torque control (DTC). Therefore, this control method will have two control loops. Both inner and outer loop controllers use predictive power. The outer ring is speed control, while the internal circle is stator current control. The inner loop is based on the finite control set &nd

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Sulaiman, Siti Fatimah, M. F. Rahmat, Ahmad Athif Faudzi, et al. "Pneumatic positioning control system using constrained model predictive controller: Experimental repeatability test." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 3913. http://dx.doi.org/10.11591/ijece.v11i5.pp3913-3923.

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Most of the controllers that were proposed to control the pneumatic positioning system did not consider the limitations or constraints of the system in their algorithms. Non-compliance with the prescribed system constraints may damage the pneumatic components and adversely affect its positioning accuracy, especially when the system is controlled in real-time environment. Model predictive controller (MPC) is one of the predictive controllers that is able to consider the constraint of the system in its algorithm. Therefore, constrained MPC (CMPC) was proposed in this study to improve the accurac

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P, Chenchu Saibabu, Sai Hitesh, Yadav Saksham, and R. Srinivasan C. "Synthesis of model predictive controller for an identified model of MIMO process." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 2 (2020): 941–49. https://doi.org/10.11591/ijeecs.v17.i2.pp941-949.

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Model Predictive Controller (MPC) technology has been researched and developed to meet varied demands of need to control industrial power plants and petroleum refineries. This development has paved the way for the MPC technology too many other fields like automotive, aerospace, food processing industries in this paper, primary importance has been paid to the development of a MPC for an identified model of Multiple Input and Multiple Output process. In this paper, a Four Tank System has been considered for generation of input-output data. This data i.e. generated input output data is used for t

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Wang, Xinyu, Xiao Ye, Yipeng Zhou, and Cong Li. "Path-Following Control of Unmanned Vehicles Based on Optimal Preview Time Model Predictive Control." World Electric Vehicle Journal 15, no. 6 (2024): 221. http://dx.doi.org/10.3390/wevj15060221.

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In order to reduce the lateral error of path-following control of unmanned vehicles under variable curvature paths, we propose a path-following control strategy for unmanned vehicles based on optimal preview time model predictive control (OP-MPC). The strategy includes the longitudinal speed limit, the optimal preview time surface, and the model predictive control (MPC)controller. The longitudinal speed limit controls speed to prevent vehicle rollover and sideslip. The optimal preview time surface adjusts the preview time according to the vehicle speed and path curvature. The preview point det

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Yang, Qingfeng, Gang Chen, Mengmeng Guo, Tingting Chen, Lei Luo, and Li Sun. "Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical Unit." Energies 17, no. 24 (2024): 6356. https://doi.org/10.3390/en17246356.

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In response to the escalating challenges of rapid load fluctuations and intricate operating environments, supercritical power units demand enhanced control efficiency and adaptability. To this end, this study introduces a novel model predictive hybrid PID control strategy that integrates PID with model predictive control (MPC), leveraging the operational characteristics of multi-loop systems. The proposed strategy adeptly marries the swift response of PID controllers with the foresight and optimization capabilities of MPC. A dynamic model of a supercritical unit is constructed using the subspa

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Núñez, Alfredo, Carlos Ocampo-Martinez, José María Maestre, and Bart De Schutter. "Time-Varying Scheme for Noncentralized Model Predictive Control of Large-Scale Systems." Mathematical Problems in Engineering 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/560702.

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The noncentralized model predictive control (NC-MPC) framework in this paper refers to any distributed, hierarchical, or decentralized model predictive controller (or a combination of them) the structure of which can change over time and the control actions of which are not obtained based on a centralized computation. Within this framework, we propose suitable online methods to decide which information is shared and how this information is used between the different local predictive controllers operating in a decentralized, distributed, and/or hierarchical way. Evaluating all the possible stru

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Henmi, Tomohiro. "Control Parameters Tuning Method of Nonlinear Model Predictive Controller Based on Quantitatively Analyzing." Journal of Robotics and Mechatronics 28, no. 5 (2016): 695–701. http://dx.doi.org/10.20965/jrm.2016.p0695.

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[abstFig src='/00280005/11.jpg' width='300' text='ANMPC controller' ] The parameter-tuning method we discuss is for an Adaptive Nonlinear Model Predictive Controller (ANMPC). The MPC is optimization-based controller and decides control input to realize system output that tracks a reference trajectory through “optimal computation.” The reference trajectory is ideal trajectory of system output to converge on a desired value, i.e. controlled system performance depends on the reference trajectory. As a MPC controller which applies to the nonlinear systems, our group has already proposed an adaptiv

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Putiatin, Redrikh, and Anatoliy Zhuchenko. "Designing a model predictive controller for graphite baking process." Proceedings of the NTUU “Igor Sikorsky KPI”. Series: Chemical engineering, ecology and resource saving, no. 3 (September 29, 2024): 74–82. http://dx.doi.org/10.20535/2617-9741.3.2024.312422.

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Model predictive controller provides better control quality for graphite products baking process than PID and its variations. The drawback of MPC is its high sensitivity to object modelling errors. If all of object parameters are varied just by 3 % simultaneously then MPC provides satisfactory control no more. In the current paper we design an adaptive MPC for baking process control. In order to identify furnace section “under the fire” on-line, we utilize recursive least squares algorithm with forgetting factor equal to 0,98. We compare conventional and adaptive MPCs in the following cases: a

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Gulzar, Muhammad, Syed Rizvi, Muhammad Javed, Daud Sibtain, and Rubab Salah ud Din. "Mitigating the Load Frequency Fluctuations of Interconnected Power Systems Using Model Predictive Controller." Electronics 8, no. 2 (2019): 156. http://dx.doi.org/10.3390/electronics8020156.

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The penetration of renewable energy sources into the conventional power systems are evolving day by day. Therefore, in this paper, a photovoltaic (PV) connected thermal system is discussed and analyzed by keeping PV to operate at maximum power point (MPP). The main problem in the interconnection of these systems is load frequency fluctuations due to different load changing conditions. The model predictive controller (MPC) has the ability to predict the target value at real-time with fast convergence. Therefore, MPC is proposed to negate this problem by giving minimum oscillation. The compariso

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Krupa, Pablo, Daniel Limon, and Teodoro Alamo. "Harmonic based model predictive control for set-point tracking." IEEE TRANSACTIONS ON AUTOMATIC CONTROL 67, no. 1 (2024): 48–62. https://doi.org/10.1109/TAC.2020.3047579.

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This article presents a novel model predictive control (MPC) formulation for set-point tracking. Stabilizing predictive controllers based on terminal ingredients may exhibit stability and feasibility issues in the event of a reference change for small to moderate prediction horizons. In the MPC for tracking formulation, these issues are solved by the addition of an artificial equilibrium point as a new decision variable, providing a significantly enlarged domain of attraction and guaranteeing recursive feasibility for any reference change. However, it may suffer from performance issues if the

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Xu, Chaobin, Xingyu Zhou, Rupeng Chen, et al. "Trajectory Tracking for 3-Wheeled Independent Drive and Steering Mobile Robot Based on Dynamic Model Predictive Control." Applied Sciences 15, no. 1 (2025): 485. https://doi.org/10.3390/app15010485.

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Compared to four-wheel independent drive and steering (4WID4WIS) mobile robots, three-wheel independent drive and steering (3WID3WIS) mobile robots are more cost-effective due to their lower cost, lighter weight, and better handling performance, even though their acceleration performance is reduced. This paper proposes a dynamic model predictive control (DMPC) controller for trajectory tracking of 3WID3WIS mobile robots to simplify the computational complexity and improve the accuracy of traditional model predictive control (MPC). The A* algorithm with a non-point mass model is used for path p

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Hewing, Lukas, Kim P. Wabersich, Marcel Menner, and Melanie N. Zeilinger. "Learning-Based Model Predictive Control: Toward Safe Learning in Control." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 269–96. http://dx.doi.org/10.1146/annurev-control-090419-075625.

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Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Model predictive control (MPC), as the prime methodology for constrained control, offers a significant opportunity to exploit the abundance of data in a reliable manner, particularly while taking safety constraints into account. This review aims at summarizing and categorizing previous research on learning-based MPC, i.e., the integration or combination of MPC

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Hui, Nanmu, Yunqian Guo, Xiaowei Han, and Baoju Wu. "Robust H-Infinity Dual Cascade MPC-Based Attitude Control Study of a Quadcopter UAV." Actuators 13, no. 10 (2024): 392. http://dx.doi.org/10.3390/act13100392.

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Aimed at the stability problem of quadrotor Unmanned Aerial Vehicle (UAV) flight attitudes under random airflow disturbance conditions, a robust H-infinity-based dual cascade Model Predictive Control (MPC) attitude control method is proposed. Model Predictive Control itself has the capability to minimize the deviation between the prediction error and the control target by optimizing the control algorithm. The robust H-infinity controller can maintain stability in the face of system model uncertainty and external disturbances. The controller designed in this paper divides the attitude control l

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Dubljevic, Stevan. "Model predictive control of diffusion-reaction processes." Chemical Industry and Chemical Engineering Quarterly 11, no. 1 (2005): 10–18. http://dx.doi.org/10.2298/ciceq0501010d.

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Parabolic partial differential equations naturally arise as an adequate representation of a large class of spatially distributed systems, such as diffusion-reaction processes, where the interplay between diffusive and reaction forces introduces complexity in the characterization of the system, for the purpose of process parameter identification and subsequent control. In this work we introduce a model predictive control (MPC) framework for the control of input and state constrained parabolic partial differential equation (PDEs) systems. Model predictive control (MPC) is one of the most popular

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