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学者姓名：王鼎
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摘要 ：
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.
关键词 ：
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
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
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
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The core of the optimal tracking control problem for nonlinear systems is how to ensure that the controlled system tracks the desired trajectory. The utility functions in previous studies have different properties which affect the final tracking effect of the intelligent critic algorithm. In this paper, we introduce a novel utility function and propose a Q -function based policy iteration algorithm to eliminate the final tracking error. In addition, neural networks are used as function approximator to approximate the performance index and control policy. Considering the impact of the approximation error on the tracking performance, an approximation error bound for each iteration of the novel Q -function is established. Under the given conditions, the approximation Q -function converges to the finite neighborhood of the optimal value. Moreover, it is proved that weight estimation errors of neural networks are uniformly ultimately bounded. Finally, the effectiveness of the algorithm is verified by the simulation example.
关键词 ：
Optimal tracking control Optimal tracking control Policy iteration Policy iteration Neural networks Neural networks Approximation errors Approximation errors Model-free control Model-free control Adaptive dynamic programming Adaptive dynamic programming
引用：
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| GB/T 7714 | Gao, Ning , Wang, Ding , Zhao, Mingming et al. Model-free intelligent critic design with error analysis for neural tracking control [J]. | NEUROCOMPUTING , 2024 , 572 . |
| MLA | Gao, Ning et al. "Model-free intelligent critic design with error analysis for neural tracking control" . | NEUROCOMPUTING 572 (2024) . |
| APA | Gao, Ning , Wang, Ding , Zhao, Mingming , Hu, Lingzhi . Model-free intelligent critic design with error analysis for neural tracking control . | NEUROCOMPUTING , 2024 , 572 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
This article focuses on the adaptive fuzzy practical predefined-time bipartite consensus tracking control (BCTC) problem for heterogeneous nonlinear multiagent systems (HNMASs) with actuator faults. First, the fuzzy logic systems are used to approximate the unknown nonlinear functions. Then, the partial loss of effectiveness and bias fault of actuator are considered simultaneously in the HNMASs, which is effectively handled by using adaptive compensation technology. In addition, the developed bipartite consensus tracking control protocol based on a practical predefined-time strategy not only ensures the fast convergence of the studied systems, but also predetermines the convergence time not relied on the initial conditions. The theoretical result shows that all signals of the closed-loop system are semiglobally uniformly predefined-time bounded, and the BCTC performance is guaranteed within the predefined time. Finally, the simulation example based on the agents of different orders shows the validity of the obtained results.
关键词 ：
Actuators Actuators bipartite consensus tracking bipartite consensus tracking predefined-time (PT) control predefined-time (PT) control Symmetric matrices Symmetric matrices Nonlinear systems Nonlinear systems Fuzzy systems Fuzzy systems Fuzzy logic Fuzzy logic Convergence Convergence heterogeneous nonlinear multiagent systems (HNMASs) heterogeneous nonlinear multiagent systems (HNMASs) Actuator faults Actuator faults Multi-agent systems Multi-agent systems
引用：
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| GB/T 7714 | Niu, Ben , Sui, Jihang , Zhao, Xudong et al. Adaptive Fuzzy Practical Predefined-Time Bipartite Consensus Tracking Control for Heterogeneous Nonlinear MASs With Actuator Faults [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (5) : 3071-3083 . |
| MLA | Niu, Ben et al. "Adaptive Fuzzy Practical Predefined-Time Bipartite Consensus Tracking Control for Heterogeneous Nonlinear MASs With Actuator Faults" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 32 . 5 (2024) : 3071-3083 . |
| APA | Niu, Ben , Sui, Jihang , Zhao, Xudong , Wang, Ding , Zhao, Xinliang , Niu, Yi . Adaptive Fuzzy Practical Predefined-Time Bipartite Consensus Tracking Control for Heterogeneous Nonlinear MASs With Actuator Faults . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (5) , 3071-3083 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
This article develops a novel data-driven safe Q-learning method to design the safe optimal controller which can guarantee constrained states of nonlinear systems always stay in the safe region while providing an optimal performance. First, we design an augmented utility function consisting of an adjustable positive definite control obstacle function and a quadratic form of the next state to ensure the safety and optimality. Second, by exploiting a pre-designed admissible policy for initialization, an off-policy stabilizing value iteration Q-learning (SVIQL) algorithm is presented to seek the safe optimal policy by using offline data within the safe region rather than the mathematical model. Third, the monotonicity, safety, and optimality of the SVIQL algorithm are theoretically proven. To obtain the initial admissible policy for SVIQL, an offline VIQL algorithm with zero initialization is constructed and a new admissibility criterion is established for immature iterative policies. Moreover, the critic and action networks with precise approximation ability are established to promote the operation of VIQL and SVIQL algorithms. Finally, three simulation experiments are conducted to demonstrate the virtue and superiority of the developed safe Q-learning method.
关键词 ：
Adaptive critic control Adaptive critic control Optimal control Optimal control Safety Safety Mathematical models Mathematical models stabilizing value iteration Q-learning (SVIQL) stabilizing value iteration Q-learning (SVIQL) Heuristic algorithms Heuristic algorithms Learning systems Learning systems adaptive dynamic programming (ADP) adaptive dynamic programming (ADP) control barrier functions (CBF) control barrier functions (CBF) state constraints state constraints Q-learning Q-learning Iterative methods Iterative methods
引用：
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| GB/T 7714 | Zhao, Mingming , Wang, Ding , Song, Shijie et al. Safe Q-Learning for Data-Driven Nonlinear Optimal Control with Asymmetric State Constraints [J]. | IEEE-CAA JOURNAL OF AUTOMATICA SINICA , 2024 , 11 (12) : 2408-2422 . |
| MLA | Zhao, Mingming et al. "Safe Q-Learning for Data-Driven Nonlinear Optimal Control with Asymmetric State Constraints" . | IEEE-CAA JOURNAL OF AUTOMATICA SINICA 11 . 12 (2024) : 2408-2422 . |
| APA | Zhao, Mingming , Wang, Ding , Song, Shijie , Qiao, Junfei . Safe Q-Learning for Data-Driven Nonlinear Optimal Control with Asymmetric State Constraints . | IEEE-CAA JOURNAL OF AUTOMATICA SINICA , 2024 , 11 (12) , 2408-2422 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, an optimal trajectory tracking control problem for general nonlinear systems is investigated. An adaptive critic control method with the digital twin (DT) theory is developed. Divergent from the existing tracking control methods, the advantages of adaptive dynamic programming (ADP) and the theory of DT are combined in this paper, and the novel multilayer artificial system structure is constructed. The actioncritic structure is employed by each artificial system to obtain an approximate optimal control policy. The model network (MN) is built by using the actual input and output data sets of the controlled system, which means the dependence on the dynamics of the system is overcame. Then, the weights of the trained action network (AN) and MN are passed to the real system to realize the optimal tracking control. The feasibility of the algorithm is proved by theoretical analysis. Finally, the algorithm is applied to a simple nonlinear torsional pendulum system and an industrial wastewater treatment system (WWTS), and the effectiveness of the algorithm is verified. The algorithm effectively realizes the tracking control of nonlinear systems.
关键词 ：
Neural networks Neural networks Data-driven control Data-driven control Digital twin theory Digital twin theory Tracking control Tracking control Wastewater treatment Wastewater treatment Adaptive dynamic programming Adaptive dynamic programming
引用：
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| GB/T 7714 | Wang, Ding , Ma, Hongyu , Qiao, Junfei . Multilayer adaptive critic design with digital twin for data-driven optimal tracking control and industrial applications [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 133 . |
| MLA | Wang, Ding et al. "Multilayer adaptive critic design with digital twin for data-driven optimal tracking control and industrial applications" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 133 (2024) . |
| APA | Wang, Ding , Ma, Hongyu , Qiao, Junfei . Multilayer adaptive critic design with digital twin for data-driven optimal tracking control and industrial applications . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 133 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
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
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
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
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
This brief leverages a value-iteration-based Q-learning (VIQL) scheme to tackle optimal tracking problems for nonlinear nonaffine systems. The optimal policy is learned from measured data instead of a precise mathematical model. Furthermore, a novel criterion is proposed to determine the stability of the iterative policy based on measured data. The evolving control algorithm is developed to verify the proposed criterion by employing these stable policies for system control. The advantage of the early elimination of tracking errors is provided by this approach since various stable policies can be employed before obtaining the optimal strategy. Finally, the effectiveness of the developed algorithm is demonstrated by a simulation experiment.
关键词 ：
intelligent control intelligent control optimal tracking control system optimal tracking control system Adaptive dynamic programming Adaptive dynamic programming value-iteration-based Q-learning value-iteration-based Q-learning stability criterion stability criterion
引用：
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| GB/T 7714 | Wang, Ding , Huang, Haiming , Zhao, Mingming . Model-Free Optimal Tracking Design With Evolving Control Strategies via Q-Learning [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS , 2024 , 71 (7) : 3373-3377 . |
| MLA | Wang, Ding et al. "Model-Free Optimal Tracking Design With Evolving Control Strategies via Q-Learning" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 71 . 7 (2024) : 3373-3377 . |
| APA | Wang, Ding , Huang, Haiming , Zhao, Mingming . Model-Free Optimal Tracking Design With Evolving Control Strategies via Q-Learning . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS , 2024 , 71 (7) , 3373-3377 . |
| 导入链接 | NoteExpress RIS BibTex |
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