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学者姓名：王鼎
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摘要 ：
In this paper, the decentralised tracking control (DTC) problem is investigated for a class of continuous-time large-scale systems with external disturbance by utilising adaptive dynamic programming (ADP). Firstly, the DTC problem is solved by designing corresponding optimal controllers of the isolated subsystems, which are formulated with N augmented subsystems consisting of the tracking error and the reference trajectory. Then, considering the external disturbance, we can effectively construct the DTC scheme by means of adding suitable feedback gains to the optimal control strategies associated with each augmented tracking isolated subsystems (ATISs). Due to the approximate nature, a series of critic neural networks are constructed to solve the Hamilton-Jacobi-Isaacs equation, so as to derive the estimation of the Nash equilibrium solution containing the optimal control strategy and the worst disturbance law. Herein, a modified weight updating criterion is developed by employing a stabilising term. Consequently, we remove the requirement of initial admissible control in the proposed algorithm. After that, stability analysis of the ATIS is performed through the Lyapunov theory, in the sense that tracking states and weight approximation errors are uniformly ultimately bounded. Finally, an experimental simulation is demonstrated to ensure the validity of the proposed DTC scheme.
关键词 ：
optimal control optimal control Adaptive dynamic programming (ADP) Adaptive dynamic programming (ADP) interconnected systems interconnected systems disturbance rejection disturbance rejection decentralised tracking control (DTC) decentralised tracking control (DTC) neural networks neural networks
引用：
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| GB/T 7714 | Wang, Ding , Fan, Wenqian , Li, Menghua et al. Decentralised tracking control based on critic learning for nonlinear disturbed interconnected systems [J]. | INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE , 2023 , 54 (5) : 1150-1164 . |
| MLA | Wang, Ding et al. "Decentralised tracking control based on critic learning for nonlinear disturbed interconnected systems" . | INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 54 . 5 (2023) : 1150-1164 . |
| APA | Wang, Ding , Fan, Wenqian , Li, Menghua , Qiao, Junfei . Decentralised tracking control based on critic learning for nonlinear disturbed interconnected systems . | INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE , 2023 , 54 (5) , 1150-1164 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this article, in order to achieve optimal tracking control of unknown linear discrete sys-tems, a model-free scheme based on Q-learning is established online. First, we introduce an innovative performance index function, so as to eliminate the tracking error and avert the calculation for stable control policies of the reference trajectory. Taking value iteration and policy iteration into consideration, the corresponding model-based approaches are derived. Then, the Q-function is developed and the model-free algorithm utilizing Q-learning is given for the sake of dealing with the linear quadratic tracking (LQT) problem online with-out relying on system dynamics information. In addition, novel stability analysis based on Q-learning is provided for the discounted LQT control issue and the probing noise is demonstrated that it does not result in any excitation noise bias. Finally, by means of con-ducting numerical simulation, the proposed Q-learning algorithm is demonstrated to be effective and practicable.(c) 2023 Elsevier Inc. All rights reserved.
关键词 ：
Model -free control Model -free control Discounted linear quadratic tracking Discounted linear quadratic tracking Adaptive critic Adaptive critic Q-function Q-function Reinforcement learning Reinforcement learning
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| GB/T 7714 | Wang, Ding , Ren, Jin , Ha, Mingming . Discounted linear Q-learning control with novel tracking cost and its stability [J]. | INFORMATION SCIENCES , 2023 , 626 : 339-353 . |
| MLA | Wang, Ding et al. "Discounted linear Q-learning control with novel tracking cost and its stability" . | INFORMATION SCIENCES 626 (2023) : 339-353 . |
| APA | Wang, Ding , Ren, Jin , Ha, Mingming . Discounted linear Q-learning control with novel tracking cost and its stability . | INFORMATION SCIENCES , 2023 , 626 , 339-353 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
With the industrialization of modern society, the pollution of water resources becomes more and more serious. Although purifying urban sewage through the wastewater treatment plants eases the burden of fragile ecosystems, the nonlinearities and uncertainties of biochemical reactions are difficult to address. In this article, a dynamic prioritized policy gradient adaptive dynamic programming (ADP) method is developed to solve the optimal control problem of nonaffine nonlinear discrete-time systems, along with convergence analysis of the algorithm. To the best of our knowledge, it is indispensable to conduct system modeling during the previous ADP research on wastewater treatment process control. By introducing the dynamic prioritized replay buffer and neural networks, the proposed ADP controller can track the setpoints of the wastewater treatment plant and alleviate the effects of disturbance without system modeling. The test results verify that the devised control method outperforms the proportional-integral-derivative strategy with less oscillation when unknown interference occurred.
关键词 ：
Process control Process control Mathematical model Mathematical model Adaptive dynamic programming (ADP) Adaptive dynamic programming (ADP) experience replay experience replay data-driven control data-driven control Wastewater Wastewater wastewater treatment wastewater treatment Heuristic algorithms Heuristic algorithms Wastewater treatment Wastewater treatment Optimal control Optimal control reinforcement learning reinforcement learning Dynamic programming Dynamic programming
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| GB/T 7714 | Yang, Ruyue , Wang, Ding , Qiao, Junfei . Policy Gradient Adaptive Critic Design With Dynamic Prioritized Experience Replay for Wastewater Treatment Process Control [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2022 , 18 (5) : 3150-3158 . |
| MLA | Yang, Ruyue et al. "Policy Gradient Adaptive Critic Design With Dynamic Prioritized Experience Replay for Wastewater Treatment Process Control" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 18 . 5 (2022) : 3150-3158 . |
| APA | Yang, Ruyue , Wang, Ding , Qiao, Junfei . Policy Gradient Adaptive Critic Design With Dynamic Prioritized Experience Replay for Wastewater Treatment Process Control . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2022 , 18 (5) , 3150-3158 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The decomposition-based evolutionary algorithm (MOEA/D) has attained excellent performance in solving optimization problems involving multiple conflicting objectives. However, the Pareto-optimal front (POF) of many multiobjective optimization problems (MOPs) has irregular properties, which weakens the performance of MOEA/D. To address this issue, we devise a dynamic transfer reference point-oriented MOEA/D with local objective-space knowledge (DTR-MOEA/D). The design principle is based on three original and rigorous mechanisms. First, the individuals are projected onto a line segment (two-objective case) or a 3-D plane (three-objective case) after being normalized in the objective space. The line segment or the plane is divided into three different regions: 1) the central region; 2) the middle region; and 3) the edge region. Second, a dynamic transfer criterion of the reference point is developed based on the population density relationships in different regions. Third, a strategy of population diversity enhancement guided by local objective-space knowledge is adopted to improve the diversity of the population. Finally, the experimental results conducted on 16 benchmark MOPs and eight modified MOPs with irregular POF shapes verify that the proposed DTR-MOEA/D has attained a strong competitiveness compared with other representative algorithms.
关键词 ：
Shape Shape Pareto optimization Pareto optimization multiobjective optimization multiobjective optimization Optimization Optimization Decomposition Decomposition Statistics Statistics local objective space local objective space dynamic transfer reference point dynamic transfer reference point Convergence Convergence Sociology Sociology Optical fibers Optical fibers
引用：
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| GB/T 7714 | Xie, Yingbo , Yang, Shengxiang , Wang, Ding et al. Dynamic Transfer Reference Point-Oriented MOEA/D Involving Local Objective-Space Knowledge [J]. | IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION , 2022 , 26 (3) : 542-554 . |
| MLA | Xie, Yingbo et al. "Dynamic Transfer Reference Point-Oriented MOEA/D Involving Local Objective-Space Knowledge" . | IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 26 . 3 (2022) : 542-554 . |
| APA | Xie, Yingbo , Yang, Shengxiang , Wang, Ding , Qiao, Junfei , Yin, Baocai . Dynamic Transfer Reference Point-Oriented MOEA/D Involving Local Objective-Space Knowledge . | IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION , 2022 , 26 (3) , 542-554 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, an adaptive critic method based on neural networks is established to solve the tracking con-trol problem for multi-person zero-sum games with constrained nonlinear dynamics. First, an augmented system is constructed with the tracking error system and the reference system, an appropriate function is introduced to handle the constrained problem, and a constrained tracking Hamilton-Jacobi-Isaacs (HJI) equation is derived for the augmented system. Then, a constrained tracking design with neural critic learning for multi-person zero-sum games is developed to approximately solve the tracking HJI equation with input constraints. A new updating rule is given and only one critic network is employed during neural critic learning. In addition, we prove that the tracking error in the augmented system is uniformly ulti-mately bounded by using Lyapunov's direct method. Finally, an example is given to verify the effectiveness of the proposed method. In this example, we make the number of control inputs less than the number of disturbance inputs. (C) 2022 Elsevier B.V. All rights reserved.
关键词 ：
Multi -person zero -sum games Multi -person zero -sum games Adaptive dynamic programming Adaptive dynamic programming Neural critic learning Neural critic learning Constrained tracking control Constrained tracking control
引用：
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| GB/T 7714 | Li, Menghua , Wang, Ding , Qiao, Junfei . Neural critic learning for tracking control design of constrained nonlinear multi-person zero-sum games [J]. | NEUROCOMPUTING , 2022 , 512 : 456-465 . |
| MLA | Li, Menghua et al. "Neural critic learning for tracking control design of constrained nonlinear multi-person zero-sum games" . | NEUROCOMPUTING 512 (2022) : 456-465 . |
| APA | Li, Menghua , Wang, Ding , Qiao, Junfei . Neural critic learning for tracking control design of constrained nonlinear multi-person zero-sum games . | NEUROCOMPUTING , 2022 , 512 , 456-465 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
For discounted optimal regulation design, the stability of the controlled system is affected by the discount factor. If an inappropriate discount factor is employed, the optimal control policy might be unstabilizing. Therefore, in this article, the effect of the discount factor on the stabilization of control strategies is discussed. We develop the system stability criterion and the selection rules of the discount factor with respect to the linear quadratic regulator problem under the general discounted value iteration algorithm. Based on the monotonicity of the value function sequence, the method to judge the stability of the controlled system is established during the iteration process. In addition, once some stability conditions are satisfied at a certain iteration step, all control policies after this iteration step are stabilizing. Furthermore, combined with the undiscounted optimal control problem, the practical rule of how to select an appropriate discount factor is constructed. Finally, several simulation examples with physical backgrounds are conducted to demonstrate the present theoretical results.
关键词 ：
Regulators Regulators Stability criteria Stability criteria reinforcement learning (RL) reinforcement learning (RL) discount factor discount factor Costs Costs Adaptive critic design Adaptive critic design optimal control optimal control Asymptotic stability Asymptotic stability stability stability Heuristic algorithms Heuristic algorithms value iteration (VI) value iteration (VI) Optimal control Optimal control linear quadratic regulator (LQR) linear quadratic regulator (LQR) Cost function Cost function
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| GB/T 7714 | Wang, Ding , Ren, Jin , Ha, Mingming et al. System Stability of Learning-Based Linear Optimal Control With General Discounted Value Iteration [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2022 , 34 (9) : 6504-6514 . |
| MLA | Wang, Ding et al. "System Stability of Learning-Based Linear Optimal Control With General Discounted Value Iteration" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 34 . 9 (2022) : 6504-6514 . |
| APA | Wang, Ding , Ren, Jin , Ha, Mingming , Qiao, Junfei . System Stability of Learning-Based Linear Optimal Control With General Discounted Value Iteration . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2022 , 34 (9) , 6504-6514 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
By utilizing a neural-network-based adaptive critic mechanism, the optimal tracking control problem is investigated for nonlinear continuous-time (CT) multiplayer zero-sum games (ZSGs) with asymmetric constraints. Initially, we build an augmented system with the tracking error system and the reference system. Moreover, a novel nonquadratic function is introduced to address asymmetric constraints. Then, we derive the tracking Hamilton-Jacobi-Isaacs (HJI) equation of the constrained nonlinear multiplayer ZSG. However, it is extremely hard to get the analytical solution to the HJI equation. Hence, an adaptive critic mechanism based on neural networks is established to estimate the optimal cost function, so as to obtain the near-optimal control policy set and the near worst disturbance policy set. In the process of neural critic learning, we only utilize one critic neural network and develop a new weight updating rule. After that, by using the Lyapunov approach, the uniform ultimate boundedness stability of the tracking error in the augmented system and the weight estimation error of the critic network is verified. Finally, two simulation examples are provided to demonstrate the efficacy of the established mechanism.
关键词 ：
Adaptive critic designs Adaptive critic designs Optimal control Optimal control adaptive dynamic programming (ADP) adaptive dynamic programming (ADP) Neural networks Neural networks Mathematical models Mathematical models Nonlinear systems Nonlinear systems asymmetric constraints asymmetric constraints Hamilton-Jacobi-Isaacs (HJI) equation Hamilton-Jacobi-Isaacs (HJI) equation optimal tracking control optimal tracking control nonlinear continuous-time (CT) systems nonlinear continuous-time (CT) systems Target tracking Target tracking multiplayer zero-sum games (ZSGs) multiplayer zero-sum games (ZSGs) Cost function Cost function Games Games
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| GB/T 7714 | Qiao, Junfei , Li, Menghua , Wang, Ding . Asymmetric Constrained Optimal Tracking Control With Critic Learning of Nonlinear Multiplayer Zero-Sum Games [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2022 , 35 (4) : 5671-5683 . |
| MLA | Qiao, Junfei et al. "Asymmetric Constrained Optimal Tracking Control With Critic Learning of Nonlinear Multiplayer Zero-Sum Games" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 35 . 4 (2022) : 5671-5683 . |
| APA | Qiao, Junfei , Li, Menghua , Wang, Ding . Asymmetric Constrained Optimal Tracking Control With Critic Learning of Nonlinear Multiplayer Zero-Sum Games . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2022 , 35 (4) , 5671-5683 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this article, a new event-based adaptive critic algorithm with multiple triggering conditions is investigated to address multi-player nonzero-sum game problems for discrete-time nonlinear dynamics. In order to improve resource utilization while ensure mutual independence among players, the corresponding novel triggering conditions are designed for each player. The corresponding control input is updated only when the relevant triggering condition is violated. It is emphasized that these triggering conditions are established based on the iteration of the time-triggered mechanism. Then, according to the setting triggering conditions, we prove that the real cost function possesses a predetermined upper bound, which realizes the cost guarantee of the controlled system. Additionally, the multi-player closed-loop system is proved to be asymptotically stable and the multi-event-triggered control method is implemented by constructing three kinds of neural networks. Finally, the effectiveness of the developed multi-event-triggered control approach is verified through conducting two simulation examples.
关键词 ：
neural networks neural networks adaptive critic adaptive critic multi-player games multi-player games guaranteed cost guaranteed cost multi-event-triggered control multi-event-triggered control nonlinear control nonlinear control
引用：
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| GB/T 7714 | Wang, Ding , Hu, Lingzhi , Qiao, Junfei . Multi-event-triggered adaptive critic control with guaranteed cost for discrete-time nonlinear nonzero-sum games [J]. | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2022 , 32 (18) : 10292-10308 . |
| MLA | Wang, Ding et al. "Multi-event-triggered adaptive critic control with guaranteed cost for discrete-time nonlinear nonzero-sum games" . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 32 . 18 (2022) : 10292-10308 . |
| APA | Wang, Ding , Hu, Lingzhi , Qiao, Junfei . Multi-event-triggered adaptive critic control with guaranteed cost for discrete-time nonlinear nonzero-sum games . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2022 , 32 (18) , 10292-10308 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The idea of optimization can be regarded as an important basis of many disciplines and hence is extremely useful for a large number of research fields, particularly for artificial-intelligence-based advanced control design. Due to the difficulty of solving optimal control problems for general nonlinear systems, it is necessary to establish a kind of novel learning strategies with intelligent components. Besides, the rapid development of computer and networked techniques promotes the research on optimal control within discrete-time domain. In this paper, the bases, the derivation, and recent progresses of critic intelligence for discrete-time advanced optimal control design are presented with an emphasis on the iterative framework. Among them, the so-called critic intelligence methodology is highlighted, which integrates learning approximators and the reinforcement formulation.
关键词 ：
Advanced optimal control Advanced optimal control Intelligent critic Intelligent critic Dynamic systems Dynamic systems
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| GB/T 7714 | Wang, Ding , Ha, Mingming , Zhao, Mingming . The intelligent critic framework for advanced optimal control [J]. | ARTIFICIAL INTELLIGENCE REVIEW , 2022 , 55 (1) : 1-22 . |
| MLA | Wang, Ding et al. "The intelligent critic framework for advanced optimal control" . | ARTIFICIAL INTELLIGENCE REVIEW 55 . 1 (2022) : 1-22 . |
| APA | Wang, Ding , Ha, Mingming , Zhao, Mingming . The intelligent critic framework for advanced optimal control . | ARTIFICIAL INTELLIGENCE REVIEW , 2022 , 55 (1) , 1-22 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this article, a novel value iteration scheme is developed with convergence and stability discussions. A relaxation factor is introduced to adjust the convergence rate of the value function sequence. The convergence conditions with respect to the relaxation factor are given. The stability of the closed-loop system using the control policies generated by the present VI algorithm is investigated. Moreover, an integrated VI approach is developed to accelerate and guarantee the convergence by combining the advantages of the present and traditional value iterations. Also, a relaxation function is designed to adaptively make the developed value iteration scheme possess fast convergence property. Finally, the theoretical results and the effectiveness of the present algorithm are validated by numerical examples.
关键词 ：
Numerical stability Numerical stability reinforcement learning (RL) reinforcement learning (RL) Stability criteria Stability criteria Adaptive dynamic programming (ADP) Adaptive dynamic programming (ADP) discrete-time nonlinear systems discrete-time nonlinear systems value iteration value iteration convergence rate convergence rate Heuristic algorithms Heuristic algorithms Approximation algorithms Approximation algorithms Optimal control Optimal control Convergence Convergence admissible control policy admissible control policy Iterative algorithms Iterative algorithms
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| GB/T 7714 | Ha, Mingming , Wang, Ding , Liu, Derong . A Novel Value Iteration Scheme With Adjustable Convergence Rate [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2022 , 34 (10) : 7430-7442 . |
| MLA | Ha, Mingming et al. "A Novel Value Iteration Scheme With Adjustable Convergence Rate" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 34 . 10 (2022) : 7430-7442 . |
| APA | Ha, Mingming , Wang, Ding , Liu, Derong . A Novel Value Iteration Scheme With Adjustable Convergence Rate . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2022 , 34 (10) , 7430-7442 . |
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