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学者姓名：王鼎
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Abstract ：
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.
Keyword ：
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
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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 . |
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Abstract ：
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.
Keyword ：
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
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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 . |
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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 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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Abstract ：
In this article, an evolution-guided value iteration (EGVI) algorithm is established to address optimal tracking problems for nonlinear nonaffine systems. Conventional adaptive dynamic programming algorithms rely on gradient information to improve the policy, which adheres to the first order necessity condition. Nonetheless, these methods encounter limitations when gradient information is intricate or system dynamics lack differentiability. In response to this challenge, evolutionary computation is leveraged by EGVI to search for the optimal policy without requiring an action network. The competition within the policy population serves as the driving force for policy improvement. Therefore, EGVI can effectively handle complex and non-differentiable systems. Additionally, this innovative method has the potential to enhance exploration efficiency and bolster the robustness of algorithms due to its population-based characteristics. Furthermore, the convergence of the algorithm and the stability of the policy are investigated based on the EGVI framework. Finally, the effectiveness of the established method is comprehensively demonstrated through two simulation experiments.
Keyword ：
Adaptive dynamic programming Adaptive dynamic programming Intelligent control Intelligent control Optimal tracking Optimal tracking Reinforcement learning Reinforcement learning Adaptive critic designs Adaptive critic designs Evolutionary computation Evolutionary computation
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| GB/T 7714 | Huang, Haiming , Wang, Ding , Zhao, Mingming et al. Evolution-guided value iteration for optimal tracking control [J]. | NEUROCOMPUTING , 2024 , 593 . |
| MLA | Huang, Haiming et al. "Evolution-guided value iteration for optimal tracking control" . | NEUROCOMPUTING 593 (2024) . |
| APA | Huang, Haiming , Wang, Ding , Zhao, Mingming , Hu, Qinna . Evolution-guided value iteration for optimal tracking control . | NEUROCOMPUTING , 2024 , 593 . |
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Abstract ：
The wastewater treatment process (WWTP) is beneficial for maintaining sufficient water resources and recycling wastewater. A crucial link of WWTP is to ensure that the dissolved oxygen (DO) concentration is continuously maintained at the predetermined value, which can actually be considered as a tracking problem. In this article, an experience replay-based action-dependent heuristic dynamic programming (ER-ADHDP) method is developed to design the model-free tracking controller to accomplish the tracking goal of the DO concentration. First, the online ER-ADHDP controller is regarded as a supplementary controller to conduct the model-free tracking control alongside a stabilizing controller with a priori knowledge. The online ER-ADHDP method can adaptively adjust weight parameters of critic and action networks, thereby continuously ameliorating the tracking result over time. Second, the ER technique is integrated into the critic and action networks to promote the data utilization efficiency and accelerate the learning process. Third, a rational stability result is provided to theoretically ensure the usefulness of the ER-ADHDP tracking design. Finally, simulation experiments including different reference trajectories are conducted to show the superb tracking performance and excellent adaptability of the proposed ER-ADHDP method.
Keyword ：
wastewater treatment applications wastewater treatment applications tracking control tracking control Action-dependent heuristic dynamic programming (ADHDP) Action-dependent heuristic dynamic programming (ADHDP) adaptive dynamic programming (ADP) adaptive dynamic programming (ADP) adaptive critic control adaptive critic control
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| GB/T 7714 | Qiao, Junfei , Zhao, Mingming , Wang, Ding et al. Action-Dependent Heuristic Dynamic Programming With Experience Replay for Wastewater Treatment Processes [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (4) : 6257-6265 . |
| MLA | Qiao, Junfei et al. "Action-Dependent Heuristic Dynamic Programming With Experience Replay for Wastewater Treatment Processes" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 4 (2024) : 6257-6265 . |
| APA | Qiao, Junfei , Zhao, Mingming , Wang, Ding , Li, Menghua . Action-Dependent Heuristic Dynamic Programming With Experience Replay for Wastewater Treatment Processes . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (4) , 6257-6265 . |
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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 ：
This paper investigates the optimal tracking issue for continuous -time (CT) nonlinear asymmetric constrained zero -sum games (ZSGs) by exploiting the neural critic technique. Initially, an improved algorithm is constructed to tackle the tracking control problem of nonlinear CT multiplayer ZSGs. Also, we give a novel nonquadratic function to settle the asymmetric constraints. One thing worth noting is that the method used in this paper to solve asymmetric constraints eliminates the strict restriction on the control matrix compared to the previous ones. Further, the optimal controls, the worst disturbances, and the tracking Hamilton-Jacobi-Isaacs equation are derived. Next, a single critic neural network is built to estimate the optimal cost function, thus obtaining the approximations of the optimal controls and the worst disturbances. The critic network weight is updated by the normalized steepest descent algorithm. Additionally, based on the Lyapunov method, the stability of the tracking error and the weight estimation error of the critic network is analyzed. In the end, two examples are offered to validate the theoretical results.
Keyword ：
Optimal tracking control Optimal tracking control Multiplayer zero-sum games Multiplayer zero-sum games Asymmetric input constraints Asymmetric input constraints Neural critic technique Neural critic technique Adaptive dynamic programming Adaptive dynamic programming
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| GB/T 7714 | Li, Menghua , Wang, Ding , Ren, Jin et al. Advanced optimal tracking integrating a neural critic technique for asymmetric constrained zero-sum games ☆ [J]. | NEURAL NETWORKS , 2024 , 177 . |
| MLA | Li, Menghua et al. "Advanced optimal tracking integrating a neural critic technique for asymmetric constrained zero-sum games ☆" . | NEURAL NETWORKS 177 (2024) . |
| APA | Li, Menghua , Wang, Ding , Ren, Jin , Qiao, Junfei . Advanced optimal tracking integrating a neural critic technique for asymmetric constrained zero-sum games ☆ . | NEURAL NETWORKS , 2024 , 177 . |
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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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