This work focuses on the iterative learning model predictive control (ILMPC) design for nonlinear discrete-time batch systems. Different from the existing results, a novel efficient two-dimensional (2D) ILMPC approach...
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This work focuses on the iterative learning model predictive control (ILMPC) design for nonlinear discrete-time batch systems. Different from the existing results, a novel efficient two-dimensional (2D) ILMPC approach is firstly proposed based on the 2-D system theory, which is able to guarantee the H tracking performance with lower computation load. Furthermore, based on the newly established event-triggered mechanisms, an event-triggered 2-D ILMPC is developed to reduce the occupation of the network resources while ensuring the Htracking performance. For the proposed ILMPC schemes, the sufficient conditions for the Htracking performance are provided explicitly by employing the linear matrix inequalities (LMI) techniques. Finally, the effectiveness of the proposed ILMPC strategies are demonstrated through numerical simulations. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
This paper once again focuses on the research of iterative learning model predictive control (ILMPC) in batch processes, which aims to ensure that the system has fast convergence speed and good non-repetitive disturba...
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This paper once again focuses on the research of iterative learning model predictive control (ILMPC) in batch processes, which aims to ensure that the system has fast convergence speed and good non-repetitive disturbance suppression ability. Firstly, using the process input and output data, a nonlinear batch process composite model consisting of a nominal ARX model and a JITL model is established, where the former is used to describe the process dynamics and the latter to evaluate the modeling error caused by the process nonlinearity. Then, an improved ILMPC (IILMPC) method is proposed, which considers the current iteration input, the input increment along the iteration axis, and the input increment in the time axis in an integrated two-dimensional feedback design framework. Meanwhile, a slack variable is also taken into account in the IILMPC design algorithm to ensure that a feasible solution will always exist. These advantages drive the presented control strategy to give better tracking performance than existing ILMPC. The convergence of the IILMPC algorithm is analyzed under mild conditions. Finally, a simulation case is given to verify the effectiveness of the proposed control method.
iterative learning model predictive control (ILMPC) has been considered as potential control strategy for batch processes. ILMPC can converge to the desired reference trajectory with high precision along batches and e...
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iterative learning model predictive control (ILMPC) has been considered as potential control strategy for batch processes. ILMPC can converge to the desired reference trajectory with high precision along batches and ensure system stability within batches. However, as a model-based control method, the control performance of the ILMPC algorithm deteriorates when exists model parameter uncertainty. Therefore, guaranteeing system tracking performance in the case of model parameter uncertainty is a challenging task in the framework designing of ILMPC method. To this end, we develop a two-stage robust ILMPC strategy for batch processes, which integrates the robust iterativelearningcontrol (ILC) in the domain of batch-axis and robust modelpredictivecontrol (MPC) in the domain of time-axis into one comprehensive control scheme. The integrated control law of the developed two-stage robust ILMPC algorithm is obtained by solving two convex optimization problems. As a result, the developed control method obtains faster convergence speed and better tracking performance in the case of model parameter uncertainty. Moreover, the convergence analysis of the system is presented. Finally, comparative simulations are provided to verify the superiority of the developed control algorithm.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.
iterative learning model predictive control (ILMPC) is an effective control technique for improving the performance of a batch process under model uncertainty and rejecting real-time disturbances. Industrial batch pro...
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iterative learning model predictive control (ILMPC) is an effective control technique for improving the performance of a batch process under model uncertainty and rejecting real-time disturbances. Industrial batch processes often have stochastic disturbance and noise and ILMPC cannot guarantee convergence for such systems. In this work, we propose a novel stochastic ILMPC that combines stochastic approximation with ILMPC algorithm. The proposed algorithm ensures the almost sure convergence property. In comparison with the ILMPC, the proposed control algorithm also shows better performance in terms of the tracking error. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.
iterative learning model predictive control (ILMPC) is an effective control technique for improving the performance of a batch process under model uncertainty and rejecting real-time disturbances. Industrial batch pro...
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iterative learning model predictive control (ILMPC) is an effective control technique for improving the performance of a batch process under model uncertainty and rejecting real-time disturbances. Industrial batch processes often have stochastic disturbance and noise and ILMPC cannot guarantee convergence for such systems. In this work, we propose a novel stochastic ILMPC that combines stochastic approximation with ILMPC algorithm. The proposed algorithm ensures the almost sure convergence property. In comparison with the ILMPC, the proposed control algorithm also shows better performance in terms of the tracking error.
superheat degree of evaporator in heating, ventilating, and air-conditioning (HVAC) systems is a crucial variable, which not only maintains the system stability, but also impacts the system efficiency. In this study, ...
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ISBN:
(纸本)9789881563958
superheat degree of evaporator in heating, ventilating, and air-conditioning (HVAC) systems is a crucial variable, which not only maintains the system stability, but also impacts the system efficiency. In this study, an iterative learning model predictive control (ILMPC) strategy for the evaporator in vapor compressor refrigeration cycle (VCC) system is proposed. First of all, a simple model of evaporator has been presented by mechanism knowledge and system identification. Then, based on the iterativelearning theory and modelpredictivecontrol method, an ILMPC controller is proposed to control the superheat degree. Finally, a simulation is provided to test the effectiveness of the proposed control strategy.
In this paper, we propose a modelpredictivecontrol (MPC) technique combined with iterativelearningcontrol (ILC), called the iterative learning model predictive control (ILMPC), for constrained multivariable contro...
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In this paper, we propose a modelpredictivecontrol (MPC) technique combined with iterativelearningcontrol (ILC), called the iterative learning model predictive control (ILMPC), for constrained multivariable control of batch processes. Although the general ILC makes the outputs converge to reference trajectories under model uncertainty, it uses open-loop control within a batch;thus, it cannot reject real-time disturbances. The MPC algorithm shows identical performance for all batches, and it highly depends on model quality because it does not use previous batch information. We integrate the advantages of the two algorithms. The proposed ILMPC formulation is based on general MPC and incorporates an iterativelearning function into MPC. Thus, it is easy to handle various issues for which the general MPC is suitable, such as constraints, time-varying systems, disturbances, and stochastic characteristics. Simulation examples are provided to show the effectiveness of the proposed ILMPC. (C) 2016 Elsevier Ltd. All rights reserved.
superheat degree of evaporator in heating, ventilating, and air-conditioning(HVAC) systems is a crucial variable, which not only maintains the system stability, but also impacts the system efficiency. In this study, a...
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superheat degree of evaporator in heating, ventilating, and air-conditioning(HVAC) systems is a crucial variable, which not only maintains the system stability, but also impacts the system efficiency. In this study, an iterative learning model predictive control(ILMPC) strategy for the evaporator in vapor compressor refrigeration cycle(VCC) system is *** of all, a simple model of evaporator has been presented by mechanism knowledge and system identification. Then, based on the iterativelearning theory and modelpredictivecontrol method,an ILMPC controller is proposed to control the superheat ***, a simulation is provided to test the effectiveness of the proposed control strategy.
In this paper, the predictivecontrol problem of two-dimensional iterativelearningmodel based on just-in-time learning (JITL) model is studied for batch processes. A new error compensation strategy is proposed based...
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ISBN:
(纸本)9798350321050
In this paper, the predictivecontrol problem of two-dimensional iterativelearningmodel based on just-in-time learning (JITL) model is studied for batch processes. A new error compensation strategy is proposed based on two-dimensional JITL model by using MPC-ILC integrated control method. Batch axis and time axis are integrated into a comprehensive objective function, and the JITL model is used to solve the problem of large computation of comprehensive objective function. The proposed control algorithm is applied to a typical batch reactor, and the results show that the proposed control strategy has good control performance.
In this work, a novel energy management control framework is developed for hybrid electric vehicles (HEVs) driving in car-following scenarios. In order to enhance the energy efficiency while maintaining the driving sa...
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In this work, a novel energy management control framework is developed for hybrid electric vehicles (HEVs) driving in car-following scenarios. In order to enhance the energy efficiency while maintaining the driving safety, a hierarchical control approach consisting of an upper level speed tracking control scheme and a lower level energy management control strategy is proposed. For the upper level tracking control system, an iterative learning model predictive control (ILMPC) scheme is developed to guarantee the tracking performance and the driving safety simultaneously. Additionally, a modelpredictivecontrol (MPC) algorithm is adopted at the lower level to optimize the torque distribution in real-time based on the driving cycles generated by the upper level control system. With the proposed hierarchical control framework, HEVs are able to improve the energy efficiency significantly by taking the advantages of the operational repeatability. The convergence of the proposed control strategy is analyzed rigorously, and its effectiveness is illustrated through numerical simulations. A hierarchical learning EMS is developed for hybrid electric vehicles (HEVs) in car-following scenarios. A tube-based iterative learning model predictive control scheme is proposed for speed tracking under uncertainties. The operation repetition is helpful to improve fuel efficiency of HEVs. image
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