Journal articles on the topic 'Model predictive controller (MPC)'
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1
Rezaee, Alireza. "Model predictive Controller for Mobile Robot." Transactions on Environment and Electrical Engineering 2, no. 2 (June 27, 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 illustrates advantage of the designed controller and its ability for exact control of the robot on a specified guide path.
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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 (May 20, 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 impact of the demand uncertainty. The proposed MPC and SMPC control models are compared to an optimal energy controller with perfect and fixed load forecast profiles and a standard set-point controller. The results show that the optimal controllers, which utilise a load forecast, improve peak reduction and cost savings of the storage device compared to the traditional control algorithm. Further improvements are presented for the receding horizon controllers, MPC and SMPC, which better handle the volatility of the crane demand. Furthermore, a computational cost analysis for optimal controllers is presented to evaluate the complexity for a practical implementation of the predictive optimal control systems.
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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 (February 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 columns by UNISIM R390.1 software. The optimization process was carried out by tuning the MPC controller parameters such as sampling time (Ts = 1 – 240 s), prediction horizon (P = 1-400), and the control horizon (M=1-400). The comparison between the performance of MPC and PI controller is presented and Integral Absolut Error (IAE) was used as comparison parameter. The results indicate that the performance of MPC was better than PI controller for set point change 0.95 to 0.94 on distillate product composition using a modified model 1 with IAE 0.0584 for MPC controller and 0.0782 for PI controller.
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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 (February 26, 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 MPC controller can achieve the best tracking of the reference path. The MPC problem is formulated as a linear time-varying (LTV) MPC controller to achieve a predictive model from nonlinear vehicle dynamics to continuous online linearization. The MPC-PFT controller method performs well by increasing the effective length of the reference path. Compared with MPC and PFT controllers, the effectiveness and robustness of the proposed method are proved by simulations of two typical working conditions.
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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 (November 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 is able to cover the general dynamics of the considered subsystem. The controller adapts the model parameters online according to the past measurements of the controlled system using a moving horizon estimation. The developed self-adjusting MPC is applied to three heating coils in a simulation. Compared with a PID controller, the self-adjusting MPC is able to increase the control quality up to 10%, while no manual tuning is needed. Additionally, the model predictive approach is able to reduce the power consumption of the pump by 80%.
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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 (August 1, 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 industrial model predictive control technology has been presented first followed by a some concepts like the receding horizon, moves etc. which form the basis of the MPC. It follows the Optimization problem which ultimately leads to the description of the Dynamic Matrix Control (DMC).The MPC presented in this report is based on DMC. After this the application summary and the limitations of the existing technology has been discussed and the next generation MPC, with an emphasis on potential business and research opportunities has been reviewed. Finally in the last part we generate Matlab code to implement basic model predictive controller and introduce noise into the model.
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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 (July 16, 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, potentially resulting in a deterioration of the nominal sensitivities and robustness properties of an existing closed-loop and (ii) the default preview action from MPC can be poor, degrading the original feedback control performance. In our previous work, the former problem is addressed by presenting a modular MPC design on top of a given output-feedback controller, which retains the nominal closed-loop robustness and frequency-domain properties of the latter, despite the addition of the preview design. In this paper, we address the second problem; the preview compensator design in the modular MPC formulation. Specifically, we derive the key conditions that ensure, under a given closed-loop tuning, the preview compensator within the modular MPC formulation is systematic and well-designed in a sense that the preview control actions complement the existing feedback control law rather than opposing it. In addition, we also derive some important results, showing that the modular MPC can be implemented in a cascade over any given linear controllers and the proposed conditions hold, regardless of the observer design for the modular MPC. The key benefit of the modular MPC is that the preview control with constraint handling can be implemented without replacing the existing feedback controller. This is illustrated through some numerical examples.
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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 CSTR. Simulation data inspector is used to compare the performance of different types of control systems: PID, MPC and MPC-PID. It has been observed that the hybrid MPC-PID has a more effective control action than a PID controller; with some tuning, the MPC controller can maintain the temperature within a reference or set point range. The control and simulation toolbox is used to construct the model predictive control method in LabVIEW platform. The performance of controllers is measured in terms of settling time, rise time and percentage of overshoot. Finally, a comparative overview of PID, MPC and Hybrid MPC-PID controllers on system performance is presented and discussed.
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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 (January 7, 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 similar to how an equipment operator quickly learns to manually control a new processing unit because of related skills learned from controlling the prior unit. This reduces the amount of data required to train the approximate MPC controller, and also improves the performance on the target system. Additionally, learning the MPC actions alleviates the computational burden of online optimization calculations, although this approach is limited to learning from systems where an MPC has already been developed. The paper reviews approximate MPC formulations with a case study that illustrates the use of neural networks and transfer learning to create a multiple-input multiple-output (MIMO) approximate MPC. The performance of the resulting controller is similar to that of a controller trained on an existing MPC, but it requires less than a quarter of the target system data for training. The main contributions of this paper are a summary survey of approximate MPC formulations and a motivating case study that includes a discussion of future development work in this area. The case study presents an example of using neural networks and transfer learning to create a MIMO approximate MPC and discusses the potential for further research and development in this area. Overall, the goal of this paper is to provide an overview of the current state of research in approximate MPC, as well as to inspire and guide future work in transfer learning.
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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 (September 1, 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 ammonia removal and aeration energy consumption than the ammonia PI controller because of the measurable disturbances used. On the other hand, with the ammonia MPC, comparable or even slightly poorer results than with the ammonia feedforward-PI controller are obtained. Further improvements to the MPC could be possible with an improved accuracy of the nonlinear reduced model of the ammonia nitrogen, more sophisticated control criteria used inside the controller and the extension of the problem from univariable ammonia to multivariable total nitrogen control.
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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 long period of time. Therefore, this study introduces an adaptive MPC where the parameters of a linear grey-box model are continuously updated in real-time. Two alternative versions of this adaptive control are investigated. The first one, called partially adaptive MPC, only updates the effective window area of the grey-box model, while the second one, called fully adaptive MPC, updates all the parameters of the grey-box model. Results show that the partially adaptive MPC is not able to deliver satisfactory prediction performance. The fully adaptive MPC shows better performance compared to the other models when implemented in a MPC, especially in avoiding thermal comfort violation.
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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 (May 20, 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 operation point. Then, the gain-scheduled MPC was designed over the entire operating range with the switched strategy, which was validated by experimental data. Furthermore, compared with the PID controller, the performance of the system was improved with a gain-scheduled MPC controller.
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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 (July 25, 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 of the paper is to reduce the total harmonic distortion (THD) of the output voltage of the three-phase voltage source inverter (VSI). A high total harmonic distortion (THD) value exists in the voltage waveform of the three-phase voltage source inverter (VSI), feeding a non-linear load. A MATLAB simulation was performed using three control techniques for a three-phase VSI feeding: linear load, unbalanced load, and non-linear load. The PB-MPC performs better than the FCS-MPC and IDA-PBC in terms of having a low THD value in the output voltage of the converter under all types of applied loads, improving the THD by up to 30%, and having low variation in THD with mismatched filter parameters, as shown in the bar charts in the results section. Overall, the PB-MPC controller improves the robustness under parameter mismatch and reduces the computational burden. PB-MPC reduces the THD value because it integrates power shaping and the injection of damping resistances into the VSI.
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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 (March 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. MPC is an advanced control strategy that uses the internal dynamic model of the process and a history of past control moves and a combination of many different technologies to predict the future plant output. The dynamics of the pasteurization process was estimated by using system identification from the experimental data. The quality of different model structures was checked using best fit with data validation, residual and stability analysis. Auto-regressive with exogenous input (ARX322) model was chosen as a model structure of the pasteurization process and fits about 80.37% with datavalidation. MPC and PID control strategies were designed using ARX322 model structure. The controller performance was compared based on settling time, percent of overshoot and stability analysis and the results are presented.
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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 efficiency of the Pronk gait by 15 percent over a range of speed from 0.75 m/s to 1.75 m/s.
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Zhang, Kanghua, Jixin Wang, Xueting Xin, Xiang Li, Chuanwen Sun, Jianfei Huang, and Weikang Kong. "A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms." Applied Sciences 12, no. 4 (February 14, 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 path tracking control in mobile platforms is discussed by considering three aspects, namely, learning and optimizing the prediction model, the controller design, and the controller output under uncertain disturbances. Furthermore, some research challenges faced by LB-MPC for path tracking control in mobile platforms are discussed.
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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 (October 1, 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 installation, and compare them to linear MPC. It is shown that controller performance can be improved, without increasing controller execution-time excessively.
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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 (February 11, 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 closed-loop response and an optimal trajectory, aiming a controller with low computational cost and good performance. The proposed method was tested in two benchmark processes using different MPC formulations, showing satisfactory results.
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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 (September 1, 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 training data for ANN controller is derived from MPC algorithm and trained offline with an accuracy of 70.3%. The proposed ANN controller shows a similar and better performance than MPC controller in terms of total harmonic distortion (THD), peak overshoot during dynamic change in reference current and dynamic change in load parameter and less computation with less execution time. Further, ANN controller for matrix converter is tested in OPAL-RT using hardware in-loop (HIL) simulation and showed that it outperforms MPC controller.
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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 its behavior. The MPC controller determines the sequence of control signal values that provides the best predicted trajectory for controlled variable. The implementation of the MPC approach is shown on the example of supply VAV ventilation system of the classroom. In the considered ventilation system, the change of heat load for the room is compensated by the change of amount of supply air coming from the central supply ventilation unit at its constant temperature. To simulate ventilation system in the Simulink environment of the MATLAB application package, the block diagram was developed, and the Model Predictive Control Toolbox was used to synthesize the MPC controller. The study of transient processes in VAV ventilation system was carried out, transient process in the system without controller, with PID controller and MPC controller were compared. Comparison of the results showed that the use of the MPC controller makes it possible to improve the regulation process of thermal regime in the room by increasing the regulation quality.
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Kardos, Tamás, and Dénes Nimród Kutasi. "Model-based Predictive Control of an HVAC System." Műszaki Tudományos Közlemények 11, no. 1 (October 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 we introduced into the controller as disturbances.
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Domina, Ádám, and Viktor Tihanyi. "Model Predictive Controller Approach for Automated Vehicle’s Path Tracking." Sensors 23, no. 15 (August 1, 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 from the path-following controller in a second MPC. Both steering system models and the cascade MPC are evaluated in simulation and on a test vehicle. The reference trajectory is calculated based on a fixed predefined path by transforming the necessary path segment to the vehicle ego coordinate system, thereby describing the reference for the path-following task in a novel way. The MPC method computes the optimal steering angle vector at each time step for following the path. The longitudinal dynamics is controlled separately by a PI controller. After simulation evaluation, experimental tests were conducted on a test vehicle on an asphalt surface. Both simulation and experimental results prove the effectiveness of the proposed reference definition method. The effect of the applied steering system models is evaluated. The inclusion of the steering dynamics in the prediction model resulted in a significant increase in controller performance. Finally, the computational requirements of the proposed control and modeling methods are also discussed.
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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 (September 1, 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 the Model<br />Predictive Controllers.
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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 predictive controller based on a nonminimal state space model where the state is perfectly known. It is an infinite prediction horizon controller, and it is assumed that there is uncertainty in the stable part of the model, which may also include integrating modes that are frequently present in the process plants. The method is illustrated with a simulation example of the process industry using linear models based on a real process.
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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 (April 23, 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 the most promising methods of control is Model Predictive Control - MPC. The method base on predictive models of control objects. The quality of the controller's control depends on how well the system dynamics described by the model used to design the controller. In most cases, MPC-based control use to handle optimal control problems on the Manufacturing Execution System-MES. However, thanks to the development of microprocessors and increased CPU performance, it becomes possible to apply the principles of control with a predictive model at a lower level, and perform real-time operational control in optimal modes. The work presents the algorithm of MPC controller. The control object is a SISO object with a nonlinear characteristic and a long transition process. Studies of the developed MPC regulator showed that the quality of regulation, compared to the PID regulator, increased by more than 20%, the time to get to set point decreased, and there was almost no over-regulation. The most effective application of the MPC controller is seen in processes with long transitions and with a significant delay time.
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Xia, Jiyu, and Zhou Zhou. "Model Predictive Control Based on ILQR for Tilt-Propulsion UAV." Aerospace 9, no. 11 (November 4, 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 framework, including global trajectory planning and finite horizon control, is designed. Taking the planned global trajectory as the reference input, a controller is proposed with an inner layer based on ILQR optimization and an outer layer based on feedback correction and forward rolling of the MPC frame. The ILQR-MPC controller has high computational efficiency to deal with nonlinear problems, and has the ability to give full play to UAV’s control ability and suppress uncertainty. Finally, the simulation results show that ILQR-MPC controller obviously performs better than the ILQR feedforward controller, and gains a scheduling PID controller and MPC controller.
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Sulaiman, Siti Fatimah, M. F. Rahmat, Ahmad Athif Faudzi, Khairuddin Osman, S. I. Samsudin, A. F. Z. Abidin, and Noor Asyikin Sulaiman. "Pneumatic positioning control system using constrained model predictive controller: Experimental repeatability test." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (October 1, 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 accuracy of pneumatic positioning system while considering the constraints of the system. The mathematical model of pneumatic system was determined by system identification technique and the control signal to the valves were considered as the constraints of the pneumatic system when developing the controller. In order to verify the accuracy and reliability of CMPC, repetitive experiments on the CMPC strategy was implemented. The existing predictive controller, that was used to control the pneumatic system such as predictive functional control (PFC), was also compared. The experimental results revealed that CMPC effectively improved the position accuracy of the pneumatic system compared to PFC strategy. However, CMPC not capable to provide a fast response as PFC.
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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 (October 20, 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 adaptive nonlinear MPC (ANMPC) for a tracking control problem of nonlinear two-link planar manipulators. This ANMPC uses a new reference trajectory having control parameters that must be tuned based on the desired controlled system’s responses and properties. To reduce troublesome parameter tuning, we propose new parameter-tuning method for ANMPC by a quantitative analysis of the relationship between a system’s behavior and ANMPC parameters. Numerically simulating the two-link nonlinear manipulator’s tracking control under various conditions demonstrates that proposed tuning method tunes the ANMPC effectively.
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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 structures of the NC-MPC controller leads to a combinatorial optimization problem. Therefore, we also propose heuristic reduction methods, to keep the number of NC-MPC problems tractable to be solved. To show the benefits of the proposed framework, a case study of a set of coupled water tanks is presented.
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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 control formulations among chemical engineers, manly due to its ability to account for the actuator (input) constraints that inevitably exist due to finite actuator power and its ability to handle state constraints within an optimal control setting. In controller synthesis, the initially parabolic partial differential equation of the diffusion reaction type is transformed by the Galerkin method into a system of ordinary differential equations (ODEs) that capture the dominant dynamics of the PDE system. Systems obtained in such a way (ODEs) are used as the basis for the synthesis of the MPC controller that explicitly accounts for the input and state constraints. Namely, the modified MPC formulation includes a penalty term that is directly added to the objective function and through the appropriate structure of the controller state constraints accounts for the infinite dimensional nature of the state of the PDE system. The MPC controller design method is successively applied to control of the diffusion-reaction process described by linear parabolic PDE, by demonstrating stabilization of the non-dimensional temperature profile around a spatially uniform unstable steady-state under satisfaction of the input (actuator) constraints and allowable non-dimensional temperature (state) constraints.
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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 (February 1, 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 comparison analysis is carried out with other conventional controllers, including genetic algorithm-based PI, firefly algorithm-based PI and PI controller. Simulation results clearly exhibit the outclass performance of MPC over all other controllers.
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Deghboudj, Imen, and Samir Ladaci. "Automatic voltage regulator performance enhancement using a fractional order model predictive controller." Bulletin of Electrical Engineering and Informatics 10, no. 5 (October 1, 2021): 2424–32. http://dx.doi.org/10.11591/eei.v10i5.2435.
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In this paper, a new design method for fractional order model predictive control (FO-MPC) is introduced. The proposed FO-MPC is synthesized for the class of linear time invariant system and applied for the control of an automatic voltage regulator (AVR). The main contribution is to use a fractional order system as prediction model, whereas the plant model is considered as an integer order one. The fractional order model is implemented using the singularity function approach. A comparative study is given with the classical MPC scheme. Numerical simulation results on the controlled AVR performances show the efficiency and the superiority of the fractional order MPC.
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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 (May 3, 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 with learning methods, for which we consider three main categories. Most of the research addresses learning for automatic improvement of the prediction model from recorded data. There is, however, also an increasing interest in techniques to infer the parameterization of the MPC controller, i.e., the cost and constraints, that lead to the best closed-loop performance. Finally, we discuss concepts that leverage MPC to augment learning-based controllers with constraint satisfaction properties.
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Yehia Sayed Mohammed, Ahmed A. Zaki Diab, Ahmed G. Mahmoud A. Aziz, Hamdi Ali,. "Investigation of the Performance of Model Predictive Control for Induction Motor Drives." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 16, 2021): 1007–15. http://dx.doi.org/10.17762/itii.v9i1.235.
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The current work presents speed, torque and flux control of an induction motor (IM) drive, founded on model predictive control (MPC). Via the MPC techniques, the motor electromagnetic torque and flux linkage are controlled as an internal loop. However, the speed is controlled as the external loop. The internal control loop is founded on finite control set FCS-MPC, and the external control founded on the torque PI controller. The performance of the MPC is tested with various conditions of the drive operation, and the outcomes approve the excellent steady-state and dynamic operation of the system in a wide range of speeds and with torque disturbance.
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P, Chenchu Saibabu, and Srinivasan C R. "Synthesis of model predictive controller for an identified model of MIMO process." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 2 (February 1, 2020): 941. http://dx.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 the estimation of two polynomial model, name ARX model (Autoregressive exogenous) model and OE (Output Error) model. With each of model output generated, the Fit-Rates of models are compared to find out most efficient model. The model equations are now considered as plant for developing a Model Predictive Controller (MPC). Two sets of results are obtained after the development of MPC and tested. One is without noise and one is with noise. Both sets of results were a success as the output signals traces step input signals after some steady oscillations in real time with in a very short period of time which indicated a good response time. The MPC developed can be applied to any polynomial model with a good Fit-Rate, it predicts and control the process variables automatically.
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Wróbel, Karol, Piotr Serkies, and Krzysztof Szabat. "Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches." Energies 13, no. 5 (March 5, 2020): 1193. http://dx.doi.org/10.3390/en13051193.
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In the paper a comparative study of the two control structures based on MPC (Model Predictive Control) for an electrical drive system with an induction motor are presented. As opposed to the classical approach, in which DFOC (Direct Field Oriented Control) with four controllers is considered, in the current study only one MPC controller is utilized. The proposed control structures have a cascade free structure that consists of a vector of electromagnetic (torque, flux) and mechanical (speed) states of the system. The first investigated framework is based on the finite-set MPC. A short horizon predictive window is selected. The continuous set MPC is used in the second framework. In this case the predictive horizon contains several samples. The computational complexity of the algorithm is reduced by applying its explicit version. Different implementation aspects of both MPC structures, for instance the model used in prediction, complexity of the control algorithms, and their properties together with the noise level are analyzed. The effectiveness of the proposed approach is validated by some experimental tests.
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Aboelhassan, Ahmed, M. Abdelgeliel, Ezz Eldin Zakzouk, and Michael Galea. "Design and Implementation of Model Predictive Control Based PID Controller for Industrial Applications." Energies 13, no. 24 (December 14, 2020): 6594. http://dx.doi.org/10.3390/en13246594.
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Advanced control approaches are essential for industrial processes to enhance system performance and increase the production rate. Model Predictive Control (MPC) is considered as one of the promising advanced control algorithms. It is suitable for several industrial applications for its ability to handle system constraints. However, it is not widely implemented in the industrial field as most field engineers are not familiar with the advanced techniques conceptual structure, the relation between the parameter settings and control system actions. Conversely, the Proportional Integral Derivative (PID) controller is a common industrial controller known for its simplicity and robustness. Adapting the parameters of the PID considering system constraints is a challenging task. Both controllers, MPC and PID, merged in a hierarchical structure in this work to improve the industrial processes performance considering the operational constraints. The proposed control system is simulated and implemented on a three-tank benchmark system as a Multi-Input Multi-Output (MIMO) system. Since the main industrial goal of the proposed configuration is to be easily implemented using the available automation technology, PID controller is implemented in a PLC (Programable Logic Controller) controller as a lower controller level, while MPC controller and the adaptation mechanism are implemented within a SCADA (Supervisory Control And Data Acquisition) system as a higher controller level.
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Dubravić, Amila, and Anel Hasić. "Legendre Orthonormal Functions Based Model Predictive Control of DC Motor." B&H Electrical Engineering 13, no. 1 (December 1, 2019): 44–49. http://dx.doi.org/10.2478/bhee-2019-0005.
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Abstract The paper presents modelling and control of very common actuator, DC motor. The control of the DC motor was implemented with the Model predictive control (MPC) controller based on Legendre orthonormal functions. As PID (Proportional-Integral-Derivative) controller is simple and effective controller, mainly used in industrial applications, above mentioned MPC controller’s performance was compared to the PID controller performance. A criterion used is the fastest response with the maximum of 2% overshoots with step input and step disturbance treated.
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Bak, Yeongsu. "Dynamic Characteristic Improvement of Integrated On-Board Charger Using a Model Predictive Control." Energies 15, no. 22 (November 21, 2022): 8745. http://dx.doi.org/10.3390/en15228745.
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This paper proposes a dynamic characteristic improvement of an integrated on-board charger (OBC) using a model predictive control (MPC) method. The integrated OBC performs both battery charging and starter generator (SG) driving for engine starting in plug-in hybrid electric vehicles (PHEVs). If it performs battery charging, battery-side voltage and battery-side current are control objects which are usually controlled by using a proportional-integral (PI) controller. However, it has the disadvantage of undesirable dynamic characteristics, and gain tuning of the PI controller is necessary to properly control the voltage and current. Therefore, this paper proposes the MPC method for the dynamic characteristic improvement of integrated OBC. It can achieve not only dynamic characteristic improvement, but also robustness from the abrupt change of load impedance. By using the proposed MPC method for integrated OBC, the settling time to control the output voltage is decreased by 50% in the transient state compared to that by using the PI controller. The effectiveness of the proposed MPC method is verified by simulation and experimental results.
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Kassem, Ahmed M., and A. A. Hassan. "Performance Improvements of a Permanent Magnet Synchronous Machine via Functional Model Predictive Control." Journal of Control Science and Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/319708.
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This paper investigates the application of the model predictive control (MPC) approach to control the speed of a permanent magnet synchronous motor (PMSM) drive system. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. In order to validate the effectiveness of the proposed FMPC scheme, the performance of the proposed controller is compared with a classical PI controller through simulation studies. Obtained results show that accurate tracking performance of the PMSM has been achieved.
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Negri, Gabriel Hermann, Arthur Garcia Bartsch, Mariana Santos Matos Cavalca, and Ademir Nied. "Frequency Response Comparison of PI-Based FOC and Cascade-Free MPC using 1 kHz SVM Applied to PMSM drive." Journal of Applied Instrumentation and Control 5, no. 2 (April 11, 2018): 1. http://dx.doi.org/10.3895/jaic.v5n2.5945.
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Abstract— This paper presents a 1 kHz SVM-FOC (Space Vector Modulation with Field Oriented Control) drive system for a Permanent Magnet Synchronous Motor, using different control strategies. Such strategies are internal model and frequency response designed PI (Proportional and Integral) controllers and a multivariable MPC (Model Predictive Control) controller using a state-space prediction model. This MPC method becomes interesting for improving the closed-loop speed frequency response, since it results in a cascade-free controller. The performance of each controller was evaluated in a qualitative manner through simulations and quantitatively by load torque and speed reference AC sweeps, generating dynamic stiffness curves and Bode diagrams for the utilized techniques. Results show that the MPC approach is useful for enabling fast dynamic responses with the reduced switching frequency, which reduces the drive system cost and improves its efficiency.
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Hu, Zhen, Daqi Zhu, Caicha Cui, and Bing Sun. "Trajectory Tracking and Re-planning with Model Predictive Control of Autonomous Underwater Vehicles." Journal of Navigation 72, no. 2 (September 21, 2018): 321–41. http://dx.doi.org/10.1017/s0373463318000668.
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The trajectory tracking of Autonomous Underwater Vehicles (AUV) is an important research topic. However, in the traditional research into AUV trajectory tracking control, the AUV often follows human-set trajectories without obstacles, and trajectory planning and tracking are separated. Focusing on this separation, a trajectory re-planning controller based on Model Predictive Control (MPC) is designed and added into the trajectory tracking controller to form a new control system. Firstly, an obstacle avoidance function is set up for the design of an MPC trajectory re-planning controller, so that the re-planned trajectory produced by the re-planning controller can avoid obstacles. Then, the tracking controller in the MPC receives the re-planned trajectory and obtains the optimal tracking control law after calculating the object function with a Sequential Quadratic Programming (SQP) optimisation algorithm. Lastly, in a backstepping algorithm, the speed jump can be sharp while the MPC tracking controller can solve the speed jump problem. Simulation results of different obstacles and trajectories demonstrate the efficiency of the proposed MPC trajectory re-planning tracking control algorithm for AUVs.
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Ali Leman, Zulkarnain, Mohd Hatta Mohammad Ariff, Hairi Zamzuri, Mohd Azizi Abdul Rahman, Saiful Amri Mazlan, Irfan Bahiuddin, and Fitri Yakub. "ADAPTIVE MODEL PREDICTIVE CONTROLLER FOR TRAJECTORY TRACKING AND OBSTACLE AVOIDANCE ON AUTONOMOUS VEHICLE." Jurnal Teknologi 84, no. 4 (May 30, 2022): 139–48. http://dx.doi.org/10.11113/jurnalteknologi.v84.13778.
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Advancement in active steering technology is vital as the autonomous vehicle technology is preparing to enter the commercialization phase. Accurate trajectory tracking and collision free motion have become an active topic being discussed in research field recently. During an emergency obstacle avoidance manoeuvre conditions, tyre force saturation can easily happened when availability of lateral tyre forces is limited by the law of tyre friction circle. This greatly affects the trajectory tracking performance of the vehicle. Existing controllers such as generic model predictive controller (MPC) and geometric controller (Stanley) need a proper gain tuning to cope with this condition. This is due to the control gains were determined by trial and error basis via linearization process at a certain targeted speed. Therefore, the control performance is limited considering the presence of speed variation as well as extreme manoeuvre trajectory. This paper proposes an Adaptive Model Predictive Controller (MPC) controller to solve aforementioned issues. First, optimized weighting gains for the steering control were obtained using PSO algorithm. The optimised weighting gains were then scheduled into the proposed Model predictive Controller via a look-up table strategy. In this work, the proposed adaptive MPC controller was designed by using the linearization of the 7 degree-of-freedom (DOF) non-linear vehicle model. Here, the linearized model for controller design was update based on the instantaneous longitudinal speed of the vehicle system plant.
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Essa, Mohamed El-Sayed M., Mahmoud Elsisi, Mohamed Saleh Elsayed, Mohamed Fawzy Ahmed, and Ahmed M. Elshafeey. "An Improvement of Model Predictive for Aircraft Longitudinal Flight Control Based on Intelligent Technique." Mathematics 10, no. 19 (September 26, 2022): 3510. http://dx.doi.org/10.3390/math10193510.
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This paper introduces a new intelligent tuning for the model predictive control (MPC) based on an effective intelligent algorithm named the bat-inspired algorithm (BIA) for the aircraft longitudinal flight. The tuning of MPC horizon parameters represents the main challenge to adjust the system performance. So, the BIA algorithm is intended to overcome the tuning issue of MPC parameters due to conventional methods, such as trial and error or designer experience. The BIA is adopted to explore the best parameters of MPC based on the minimization of various time domain objective functions. The suggested aircraft model takes into account the aircraft dynamics and constraints. The nonlinear dynamics of aircraft, gust disturbance, parameters uncertainty and environment variations are considered the main issues against the control of aircraft to provide a good flight performance. The nonlinear autoregressive moving average (NARMA-L2) controller and proportional integral (PI) controller are suggested for aircraft control in order to evaluate the effectiveness of the proposed MPC based on BIA. The proposed MPC based on BIA and suggested controllers are evaluated against various criteria and functions to prove the effectiveness of MPC based on BIA. The results confirm that the accomplishment of the suggested BIA-based MPC is outstanding to the NARMA-L2 and traditional PI controllers according to the cross-correlation criteria, integral time absolute error (ITAE), system overshoot, response settling time, and system robustness.
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Liu, Cheng, Ting Sun, and Qizhi Hu. "Synchronization Control of Dynamic Positioning Ships Using Model Predictive Control." Journal of Marine Science and Engineering 9, no. 11 (November 8, 2021): 1239. http://dx.doi.org/10.3390/jmse9111239.
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Underway replenishment is essential for ships performing long-term missions at sea, which can be formulated into the problem of leader-tracking configuration. Not only the position and orientation but also the velocities are required to be controlled for ensuring the synchronization; additionally, the control inputs are constrained. On this basis, in this paper, a novel synchronization controller on account of model predictive control (MPC) for dynamic positioning (DP) ships is devised to achieve underway replenishment. Firstly, a novel synchronization controller based on MPC is devised to ensure the synchronization of not only the position and orientation but the velocities; furthermore, it is a beneficial solution for its advantages in handling the control input constraints ignored in most studies of underway replenishment. Secondly, a neurodynamic optimization system is applied to the implementation of MPC, which can improve the computational efficiency and shorten the simulation time. Thirdly, stability, frequently neglected by traditional MPC, is ensured by the means of adding a terminal cost function exported from the Lyapunov equation into the objective function. Finally, the effectiveness and advantages of the proposed control design are illustrated by extensive simulations.
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Sockeel, Nicolas, Jian Shi, Masood Shahverdi, and Michael Mazzola. "Sensitivity Analysis of the Battery Model for Model Predictive Control: Implementable to a Plug-In Hybrid Electric Vehicle." World Electric Vehicle Journal 9, no. 4 (November 6, 2018): 45. http://dx.doi.org/10.3390/wevj9040045.
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Developing an efficient online predictive modeling system (PMS) is a major issue in the field of electrified vehicles as it can help reduce fuel consumption, greenhouse gasses (GHG) emission, but also the aging of power-train components, such as the battery. For this manuscript, a model predictive control (MPC) has been considered as PMS. This control design has been defined as an optimization problem that uses the projected system behaviors over a finite prediction horizon to determine the optimal control solution for the current time instant. In this manuscript, the MPC controller intents to diminish simultaneously the battery aging and the equivalent fuel consumption. The main contribution of this manuscript is to evaluate numerically the impacts of the vehicle battery model on the MPC optimal control solution when the plug hybrid electric vehicle (PHEV) is in the battery charge sustaining mode. Results show that the higher fidelity model improves the capability of accurately predicting the battery aging.
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白家納, 白家納, and 黃崇能 Pachara Opattrakarnkul. "以深度學習模式估測控制之駕駛輔助系統的研發." 理工研究國際期刊 12, no. 1 (April 2022): 015–24. http://dx.doi.org/10.53106/222344892022041201002.
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<p>Adaptive cruise control (ACC) systems are designed to provide longitudinal assistance to enhance safety and driving comfort by adjusting vehicle velocity to maintain a safe distance between the host vehicle and the preceding vehicle. Generally, using model predictive control (MPC) in ACC systems provides high responsiveness and lower discomfort by solving real-time constrained optimization problems but results in computational load. This paper presents an architecture of deep learning based on model predictive control in ACC systems to avoid real-time optimization problems required by MPC, which in turn, reduces computational load. The learning dataset is acquired from the simulation data of the input/output of the MPC controller. We designed the proposed deep learning controller using long short-term memory networks (LSTMs) and simulated it in MATLAB/Simulink using the vehicle’s characteristics from the advanced vehicle simulator (ADVISOR). Finally, the safety and driving comfort are compared with the PID-based control to demonstrate the performance of the proposed deep-learning architecture.</p> <p> </p>
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Yazar, I., F. Caliskan, and R. Vepa. "Model Predictive Control and Controller Parameter Optimisation of Combustion Instabilities." International Journal of Turbo & Jet-Engines 36, no. 2 (May 27, 2019): 185–94. http://dx.doi.org/10.1515/tjj-2017-0062.
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Abstract In this paper the application of model predictive control (MPC) to a two-mode model of the dynamics of the combustion process is considered. It is shown that the MPC by itself does not stabilize the combustor and the control gains obtained by applying the MPC algorithms need to be optimized further to ensure that the phase difference between the two modes is also stable. The results of applying the algorithm are compared with the open loop model amplitude responses and to the closed loop responses obtained by the application of a direct adaptive control algorithm. It is shown that the MPC coupled with the cost parameter optimisation proposed in the paper, always guarantees the closed loop stability, a feature that may not always be possible with an adaptive implementations.
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Hou, Juan, Haoran Li, and Natasa Nord. "Optimal control of secondary side supply water temperature for substation in district heating systems." E3S Web of Conferences 111 (2019): 06015. http://dx.doi.org/10.1051/e3sconf/201911106015.
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Low temperature is the most significant feature of the future district heating system- the 4th generation district heating (4GDH). However, a widely used control strategy for supply water temperature in substation is weather- compensated control. It is a feedforward control without any dynamic information about buildings, which can lead to higher or lower supply water temperature. This paper presents model predictive controller (MPC) applied to the supply water temperature control for substations in district heating systems. MPC is an advanced control technique, which can make full use of dynamic information of buildings to determine the optimal supply water temperature of substations. In this paper, a multiple inputs and single output dynamic model was identified by subspace methods. Two different MPC controllers were designed in Simulink. The MPC controller 1 focused on keeping indoor air temperature at reference values. The MPC controller 2 focused on both keeping indoor air temperature at reference values and tracking the minimum supply water temperature in order to find the temperature potential for the future DH systems. Both of the MPC controllers proved to have a better tracking effect for indoor air temperature and lower average supply temperatures compared to weather- compensated. The MPC controller 2 could further lower supply water temperature compared to the MPC controller 1 by tracking minimum supply water temperature in its objective function. The average supply water temperatures for the weather- compensated, the MPC controller1, and the MPC controller 2 were 52°C, 51°C and 50°C, respectively. The results showed that MPC has a great potential in the area of supply water temperature control of the district heating systems.
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Ha, Vo Thanh, and Pham Thi Giang. "Control for induction motor drives using predictive model stator currents and speeds control." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 4 (December 1, 2022): 2005. http://dx.doi.org/10.11591/ijpeds.v13.i4.pp2005-2013.
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<span>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 – model predictive control (FCS-MPC), while the outer ring to take full advantage of the high dynamic response of the inner circle uses the deadbeat MPC. MATLAB simulation results show that this control method has performance equivalent to traditional controllers while minimizing overshoot and having fast, on-demand response times.</span>