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学者姓名：王鼎
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Abstract ：
This paper studies the adaptive fuzzy resilient fixed-time bipartite consensus tracking control problem for a class of nonlinear multi-agent systems (MASs) under sensor deception attacks. Firstly, in order to reduce the impact of unknown sensor deception attacks on the nonlinear MASs, a novel coordinate transformation technique is proposed, which is composed of the states after being attacked. Then, in the case of unbalanced directed topological graph, a partition algorithm (PA) is utilized to implement the bipartite consensus tracking control, which is more widely applicable than the previous control strategies that only apply to balanced directed topological graph. Moreover, the fixed-time control strategy is extended to nonlinear MASs under sensor deception attacks, and the singularity problem that exists in fixed-time control is successfully avoided by employing a novel switching function. The developed distributed adaptive resilient fixed-time control strategy ensures that all the signals in the closed-loop system are bounded and the bipartite consensus tracking control is achieved in fixed time. Finally, the designed control strategy's validity is demonstrated by means of a simulation experiment.
Keyword ：
sensor deception attacks sensor deception attacks Bipartite consensus tracking Bipartite consensus tracking fuzzy logic systems fuzzy logic systems nonlinear MASs nonlinear MASs fixed-time control fixed-time control
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| GB/T 7714 | Niu, Ben , Shang, Zihao , Zhang, Guangju et al. Adaptive Fuzzy Resilient Fixed-Time Bipartite Consensus Tracking Control for Nonlinear MASs Under Sensor Deception Attacks [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 . |
| MLA | Niu, Ben et al. "Adaptive Fuzzy Resilient Fixed-Time Bipartite Consensus Tracking Control for Nonlinear MASs Under Sensor Deception Attacks" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2024) . |
| APA | Niu, Ben , Shang, Zihao , Zhang, Guangju , Chen, Wendi , Wang, Huanqing , Zhao, Xudong et al. Adaptive Fuzzy Resilient Fixed-Time Bipartite Consensus Tracking Control for Nonlinear MASs Under Sensor Deception Attacks . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 . |
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Abstract ：
In this brief, the decentralized optimal control problem of continuous-time input-affine nonlinear systems with mismatched interconnections is investigated by utilizing data-based integral policy iteration. Initially, the decentralized mismatched subsystems are converted into the nominal auxiliary subsystems. Then, we derive the optimal controllers of the nominal auxiliary subsystems with a well-defined discounted cost function under the framework of adaptive dynamic programming. In the implementation process, the integral reinforcement learning algorithm is employed to explore the partially or completely unknown system dynamics. It is worth mentioning that the actor-critic structure is adopted based on neural networks, in order to evaluate the control policy and the performance of the control system. Besides, the least squares method is also involved in this online learning process. Finally, a simulation example is provided to illustrate the validity of the developed algorithm.
Keyword ：
Heuristic algorithms Heuristic algorithms Cost function Cost function Interconnected systems Interconnected systems integral policy iteration integral policy iteration Optimal control Optimal control optimal control optimal control mismatched interconnections mismatched interconnections Reinforcement learning Reinforcement learning decentralized control decentralized control Adaptive dynamic programming Adaptive dynamic programming data-based online control data-based online control Integrated circuit interconnections Integrated circuit interconnections Dynamic programming Dynamic programming neural networks neural networks
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| GB/T 7714 | Wang, Ding , Fan, Wenqian , Liu, Ao et al. Decentralized Optimal Neurocontroller Design for Mismatched Interconnected Systems via Integral Policy Iteration [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS , 2024 , 71 (2) : 687-691 . |
| MLA | Wang, Ding et al. "Decentralized Optimal Neurocontroller Design for Mismatched Interconnected Systems via Integral Policy Iteration" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 71 . 2 (2024) : 687-691 . |
| APA | Wang, Ding , Fan, Wenqian , Liu, Ao , Qiao, Junfei . Decentralized Optimal Neurocontroller Design for Mismatched Interconnected Systems via Integral Policy Iteration . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS , 2024 , 71 (2) , 687-691 . |
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Abstract ：
With the rapid development of industry, the amount of wastewater discharge is increasing. In order to improve the efficiency of the wastewater treatment process (WWTP), we often desire that the dissolved oxygen (DO) concentration and the nitrate nitrogen (NO) concentration can be controlled to track set values. However, the wastewater treatment system is a type of unknown nonlinear plant with time -varying dynamics and strong disturbances. Some traditional control methods are difficult to achieve this goal. To overcome these challenges, a supplementary heuristic dynamic programming (SUP-HDP) control scheme is established by combining the traditional control method and heuristic dynamic programming (HDP). A parallel control structure is constructed in the SUP-HDP control scheme, which not only complements the shortcomings of traditional control schemes in learning and adaptive abilities but also improves the convergence speed and the stability of the learning process of HDP. Besides, the convergence proof of the designed control scheme is provided. The SUP-HDP control scheme is implemented utilizing neural networks. Finally, we validate the effectiveness of the SUP-HDP control method through a benchmark simulation platform for the WWTP. Compared with other control methods, SUP-HDP has better control performance.
Keyword ：
Neural networks Neural networks Wastewater treatment process control Wastewater treatment process control Tracking control Tracking control Reinforcement learning Reinforcement learning programming programming Supplementary heuristic dynamic Supplementary heuristic dynamic
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| GB/T 7714 | Wang, Ding , Li, Xin , Xin, Peng et al. Supplementary heuristic dynamic programming for wastewater treatment process control [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 247 . |
| MLA | Wang, Ding et al. "Supplementary heuristic dynamic programming for wastewater treatment process control" . | EXPERT SYSTEMS WITH APPLICATIONS 247 (2024) . |
| APA | Wang, Ding , Li, Xin , Xin, Peng , Liu, Ao , Qiao, Junfei . Supplementary heuristic dynamic programming for wastewater treatment process control . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 247 . |
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Abstract ：
With the deepening of modernization and industrialization, the issues of water pollution and scarcity have become more pressing. To address these issues, many wastewater treatment factories have been built to improve the reuse of water resources. However, the control of the wastewater treatment process (WWTP) is a complex task due to the highly nonlinear and strongly coupled nature. It is challenging to develop the accurate mechanism models of the wastewater treatment system. The improvement of the efficiency for the WWTP is crucial to safeguard the urban ecological environment. In this paper, adaptive critic with weight allocation (ACWA) is developed to address the optimal control problem in the WWTP. Different from the previous methods of the WWTP, system modeling is not adopted in this paper, which meets the actual physical background of the wastewater treatment system to a great extent. In addition, the actor -critic algorithm in reinforcement learning is used as the basic structure in the ACWA. It is worth noting that a novel weighted action -value function and the advantage function are introduced in the weight updating process of the action network and the critic network. The experimental results show that the control accuracy of the ACWA is greatly improved compared with the previous control methods.
Keyword ：
Adaptive critic design Adaptive critic design Actor-critic Actor-critic Reinforcement learning Reinforcement learning Neural networks Neural networks Wastewater treatment processes Wastewater treatment processes
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| GB/T 7714 | Wang, Ding , Ma, Hongyu , Ren, Jin et al. Adaptive critic design with weight allocation for intelligent learning control of wastewater treatment plants [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 133 . |
| MLA | Wang, Ding et al. "Adaptive critic design with weight allocation for intelligent learning control of wastewater treatment plants" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 133 (2024) . |
| APA | Wang, Ding , Ma, Hongyu , Ren, Jin , Gao, Ning , Qiao, Junfei . Adaptive critic design with weight allocation for intelligent learning control of wastewater treatment plants . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 133 . |
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Abstract ：
In practical industrial processes, the receding optimization solution of nonlinear model predictive control (NMPC) is always a very knotty problem. Based on adaptive dynamic programming, the accelerated value iteration predictive control (AVI-PC) algorithm is developed in this paper. Integrating iteration learning with the receding horizon mechanism of NMPC, a novel receding optimization solution pattern is exploited to resolve the optimal control law in each prediction horizon. Besides, the basic architecture and the specific form of the AVI-PC algorithm are demonstrated, including the relationship among the iterative learning process, the prediction process, and the control process. On this basis, the convergence and admissibility conditions are established, and the relevant properties are comprehensively analyzed when the accelerated factor satisfies the established conditions. Furthermore, the accelerated value iterative function is approximated through the single critic network constructed by utilizing the multiple linear regression method. Finally, the plentiful simulation experiments are conducted from various perspectives to verify the effectiveness and progressiveness of the AVI-PC algorithm.
Keyword ：
Accelerated mechanism Accelerated mechanism Adaptive critic designs Adaptive critic designs Nonlinear model predictive control Nonlinear model predictive control Value iteration Value iteration Neural networks Neural networks
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| GB/T 7714 | Xin, Peng , Wang, Ding , Liu, Ao et al. Neural critic learning with accelerated value iteration for nonlinear model predictive control [J]. | NEURAL NETWORKS , 2024 , 176 . |
| MLA | Xin, Peng et al. "Neural critic learning with accelerated value iteration for nonlinear model predictive control" . | NEURAL NETWORKS 176 (2024) . |
| APA | Xin, Peng , Wang, Ding , Liu, Ao , Qiao, Junfei . Neural critic learning with accelerated value iteration for nonlinear model predictive control . | NEURAL NETWORKS , 2024 , 176 . |
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Abstract ：
In this paper, we develop an improved data-based integral policy iteration method to address the robust control issue for nonlinear systems. Combining multi-step neural networks with pre-training, the condition of selecting the initial admissible control policy is relaxed even though the information of system dynamics is unknown. Based on adaptive critic learning, the established algorithm is conducted to attain the optimal controller. Then, the robust control strategy is derived by adding the feedback gain. Furthermore, the computing error is considered during the process of implementing matrix inverse operation. Finally, two examples are presented to verify the effectiveness of the constructed algorithm.
Keyword ：
multi-step neural networks multi-step neural networks robust control robust control uncertain systems uncertain systems integral policy iteration integral policy iteration Adaptive critic learning Adaptive critic learning
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| GB/T 7714 | Wang, Ding , Liu, Ao , Qiao, Junfei . A model-free deep integral policy iteration structure for robust control of uncertain systems [J]. | INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE , 2024 , 55 (8) : 1571-1583 . |
| MLA | Wang, Ding et al. "A model-free deep integral policy iteration structure for robust control of uncertain systems" . | INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 55 . 8 (2024) : 1571-1583 . |
| APA | Wang, Ding , Liu, Ao , Qiao, Junfei . A model-free deep integral policy iteration structure for robust control of uncertain systems . | INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE , 2024 , 55 (8) , 1571-1583 . |
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Abstract ：
Reinforcement learning (RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming (ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively. Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks, showing how they promote ADP formulation significantly. Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has demonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.
Keyword ：
complex environment complex environment optimal control optimal control data-driven control data-driven control Adaptive dynamic programming (ADP) Adaptive dynamic programming (ADP) nonlinear systems nonlinear systems intelligent control intelligent control advanced control advanced control event-triggered design event-triggered design reinforcement learning (RL) reinforcement learning (RL) neural networks neural networks
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| GB/T 7714 | Wang, Ding , Gao, Ning , Liu, Derong et al. Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications [J]. | IEEE-CAA JOURNAL OF AUTOMATICA SINICA , 2024 , 11 (1) : 18-36 . |
| MLA | Wang, Ding et al. "Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications" . | IEEE-CAA JOURNAL OF AUTOMATICA SINICA 11 . 1 (2024) : 18-36 . |
| APA | Wang, Ding , Gao, Ning , Liu, Derong , Li, Jinna , Lewis, Frank L. . Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications . | IEEE-CAA JOURNAL OF AUTOMATICA SINICA , 2024 , 11 (1) , 18-36 . |
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Abstract ：
In this article, an adaptive critic scheme with a novel performance index function is developed to solve the tracking control problem, which eliminates the tracking error and possesses the adjustable convergence rate in the offline learning process. Under some conditions, the convergence and monotonicity of the accelerated value function sequence can be guaranteed. Combining the advantages of the adjustable and general value iteration schemes, an integrated algorithm is proposed with a fast guaranteed convergence, which involves two stages, namely the acceleration stage and the convergence stage. Moreover, an effective approach is given to adaptively determine the acceleration interval. With this operation, the fast convergence of the new value iteration scheme can be fully utilized. Finally, compared with the general value iteration, the numerical results are presented to verify the fast convergence and the tracking performance of the developed adaptive critic design.
Keyword ：
fast convergence fast convergence value iteration value iteration adaptive critic designs adaptive critic designs adaptive dynamic programming adaptive dynamic programming nonlinear tracking control nonlinear tracking control
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| GB/T 7714 | Wang, Ding , Wang, Yuan , Ha, Mingming et al. Improved value iteration for nonlinear tracking control with accelerated learning [J]. | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2024 , 34 (6) : 4112-4131 . |
| MLA | Wang, Ding et al. "Improved value iteration for nonlinear tracking control with accelerated learning" . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 34 . 6 (2024) : 4112-4131 . |
| APA | Wang, Ding , Wang, Yuan , Ha, Mingming , Ren, Jin , Qiao, Junfei . Improved value iteration for nonlinear tracking control with accelerated learning . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2024 , 34 (6) , 4112-4131 . |
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Abstract ：
Wastewater treatment is important for maintaining a balanced urban ecosystem. To ensure the success of wastewater treatment, the tracking error between the crucial variable concentrations and the set point needs to be minimized as much as possible. Since the multiple biochemical reactions are involved, the wastewater treatment system is a nonlinear system with unknown dynamics. For this class of systems, this paper develops an online action dependent heuristic dynamic programming (ADHDP) algorithm combining the temporal difference with lambda [TD(lambda)], which is called ADHDP(lambda). By introducing the TD(lambda), the future n-step information is considered and the learning efficiency of the ADHDP algorithm is improved. We not only give the implementation process of the ADHDP(lambda) algorithm based on neural networks, but also prove the stability of the algorithm under certain conditions. Finally, the effectiveness of the ADHDP(lambda) algorithm is verified through two nonlinear systems, including a wastewater treatment system and a torsional pendulum system. Simulation results show that the ADHDP(lambda) algorithm has higher learning efficiency compared to the general ADHDP algorithm.
Keyword ：
Reinforcement learning Reinforcement learning Wastewater treatment processes Wastewater treatment processes Temporal difference with lambda Temporal difference with lambda Action dependent heuristic dynamic programming Action dependent heuristic dynamic programming Online control Online control
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| GB/T 7714 | Li, Xin , Wang, Ding , Zhao, Mingming et al. Reinforcement learning control with n-step information for wastewater treatment systems [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 133 . |
| MLA | Li, Xin et al. "Reinforcement learning control with n-step information for wastewater treatment systems" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 133 (2024) . |
| APA | Li, Xin , Wang, Ding , Zhao, Mingming , Qiao, Junfei . Reinforcement learning control with n-step information for wastewater treatment systems . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 133 . |
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Abstract ：
In this paper, an adjustable Q -learning scheme is developed to solve the discrete -time nonlinear zero -sum game problem, which can accelerate the convergence rate of the iterative Q -function sequence. First, the monotonicity and convergence of the iterative Q -function sequence are analyzed under some conditions. Moreover, by employing neural networks, the model -free tracking control problem can be overcome for zerosum games. Second, two practical algorithms are designed to guarantee the convergence with accelerated learning. In one algorithm, an adjustable acceleration phase is added to the iteration process of Q -learning, which can be adaptively terminated with convergence guarantee. In another algorithm, a novel acceleration function is developed, which can adjust the relaxation factor to ensure the convergence. Finally, through a simulation example with the practical physical background, the fantastic performance of the developed algorithm is demonstrated with neural networks.
Keyword ：
Adaptive dynamic programming Adaptive dynamic programming Optimal tracking control Optimal tracking control Neural networks Neural networks Q-learning Q-learning Zero-sum games Zero-sum games Convergence rate Convergence rate
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| GB/T 7714 | Wang, Yuan , Wang, Ding , Zhao, Mingming et al. Neural Q-learning for discrete-time nonlinear zero-sum games with adjustable convergence rate [J]. | NEURAL NETWORKS , 2024 , 175 . |
| MLA | Wang, Yuan et al. "Neural Q-learning for discrete-time nonlinear zero-sum games with adjustable convergence rate" . | NEURAL NETWORKS 175 (2024) . |
| APA | Wang, Yuan , Wang, Ding , Zhao, Mingming , Liu, Nan , Qiao, Junfei . Neural Q-learning for discrete-time nonlinear zero-sum games with adjustable convergence rate . | NEURAL NETWORKS , 2024 , 175 . |
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