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学者姓名：王鼎
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Abstract ：
In this paper, based on the adaptive critic control method, an improved event-based trajectory tracking mechanism of continuous-time (CT) nonlinear multiplayer zero-sum games (MZSGs) is established. It is worthy of note that previous papers studying the trajectory tracking issue of nonlinear CT MZSGs only apply to the case where the reference trajectory eventually converges to zero. Consequently, this paper develops an improved mechanism to overcome this weakness. Later, an event-triggered framework is brought in to reduce the amount of computation and improve control efficiency. In this process, an innovative triggering condition is provided. At the same time, the infamous Zeno behavior is ruled out through theoretical analysis. Furthermore, the event-based near-optimal controls and event-based near-worst disturbances for tracking error dynamics are gained by building and adjusting a single critic neural network. Immediately after, by utilizing the Lyapunov method, the uniform ultimate boundedness stability of the tracking error and the weight estimation error is ensured. Lastly, an example containing two case studies is offered to validate the validity of the established mechanism. Note to Practitioners-Complex industrial processes often involve multiple control inputs and may also be affected by multiple disturbances at the same time, which can be referred to as a MZSG. Since many industrial processes can be viewed as a tracking question of nonlinear systems and the event-triggered mechanism can decrease the computational cost, the tracking problem for event-based nonlinear MZSGs is studied in this paper, which is significant for control practitioners. Moreover, the Hamilton-Jacobi-Isaacs equation is often difficult to solve when dealing with the game problem. Hence, an adaptive critic technique is presented to acquire the near-optimal controls and the near-worst disturbances, which replaces the traditional actor-critic framework and thus simplifies the theoretical analysis. Note that this paper proposes an innovative triggering condition to relax the restriction on the choice of disturbance rejection level. Compared to previous works dealing with the tracking problem of nonlinear MZSGs, the method presented in this paper makes the choice of the reference trajectory more flexible and thus enhances the applicability in general industrial processes. Finally, stability analysis and simulation results are given. Note that for different practical situations, practitioners can adjust the related parameters to achieve the tracking control of MZSGs and minimize the computational cost.
Keyword ：
Adaptive critic designs Adaptive critic designs Games Games nonlinear continuous-time (CT) systems nonlinear continuous-time (CT) systems Neural networks Neural networks Process control Process control event-based tracking control event-based tracking control Game theory Game theory Vectors Vectors Trajectory tracking Trajectory tracking Computational efficiency Computational efficiency Mathematical models Mathematical models Trajectory Trajectory adaptive dynamic programming (ADP) adaptive dynamic programming (ADP) multiplayer zero-sum games (MZSGs) multiplayer zero-sum games (MZSGs) Dynamic programming Dynamic programming
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| GB/T 7714 | Li, Menghua , Wang, Ding , Qiao, Junfei . An Improved Trajectory Tracking Mechanism With Adaptive Critic for Event-Based Multiplayer Zero-Sum Games [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 . |
| MLA | Li, Menghua 等. "An Improved Trajectory Tracking Mechanism With Adaptive Critic for Event-Based Multiplayer Zero-Sum Games" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2024) . |
| APA | Li, Menghua , Wang, Ding , Qiao, Junfei . An Improved Trajectory Tracking Mechanism With Adaptive Critic for Event-Based Multiplayer Zero-Sum Games . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 . |
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Abstract ：
In this article, an evolution-guided adaptive dynamic programming (EGADP) algorithm is developed to address the optimal regulation problems for the nonlinear systems. In the traditional adaptive dynamic programming algorithms, policy improvement is typically reliant on the gradient information, according to the first order necessity condition. However, these methods encounter limitations when calculating the gradient information becomes infeasible or system dynamics is not differentiable. In response to this challenge, the evolutionary computation is harnessed by EGADP to search for a superior policy during policy improvement. Therefore, compared with the traditional methods, scenarios that gradient information is unavailable can effectively be handled by EGADP. Additionally, the convergence of the algorithm is proven to enhance the rigorousness of the developed method. Finally, the three simulation experiments with realistic physical backgrounds are conducted to comprehensively demonstrate the effectiveness of the established method from different perspectives.
Keyword ：
intelligent control intelligent control adaptive dynamic programming (ADP) adaptive dynamic programming (ADP) optimal control optimal control evolutionary computation (EC) evolutionary computation (EC) reinforcement learning (RL) reinforcement learning (RL) Adaptive critic designs Adaptive critic designs
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| GB/T 7714 | Wang, Ding , Huang, Haiming , Liu, Derong et al. Evolution-Guided Adaptive Dynamic Programming for Nonlinear Optimal Control [J]. | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 , 54 (10) : 6043-6054 . |
| MLA | Wang, Ding et al. "Evolution-Guided Adaptive Dynamic Programming for Nonlinear Optimal Control" . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 54 . 10 (2024) : 6043-6054 . |
| APA | Wang, Ding , Huang, Haiming , Liu, Derong , Zhao, Mingming , Qiao, Junfei . Evolution-Guided Adaptive Dynamic Programming for Nonlinear Optimal Control . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 , 54 (10) , 6043-6054 . |
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Abstract ：
In this brief, a novel accelerated Q-learning algorithm is developed to address optimal control problems for discrete-time nonlinear systems. First, the accelerated Q-learning scheme is proposed by introducing the relaxation factor. Note that the relaxation factor leads to the adjustability of the convergence rate. Second, the convergence of the Q-function is analyzed with different relaxation factors. Third, the adjustable Q-learning scheme is developed with guaranteed convergence, which can adaptively change the value of the relaxation factor. Finally, the simulation results demonstrate the effectiveness of this proposed algorithm.
Keyword ：
Heuristic algorithms Heuristic algorithms Optimal control Optimal control nonlinear systems nonlinear systems optimal control optimal control Convergence Convergence Iterative methods Iterative methods Q-learning Q-learning Power system dynamics Power system dynamics Adaptation models Adaptation models Adaptive dynamic programming Adaptive dynamic programming fast convergence rate fast convergence rate
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| GB/T 7714 | Wang, Ding , Wang, Yuan , Zhao, Mingming et al. Iterative Q-Learning for Model-Free Optimal Control With Adjustable Convergence Rate [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS , 2024 , 71 (4) : 2224-2228 . |
| MLA | Wang, Ding et al. "Iterative Q-Learning for Model-Free Optimal Control With Adjustable Convergence Rate" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 71 . 4 (2024) : 2224-2228 . |
| APA | Wang, Ding , Wang, Yuan , Zhao, Mingming , Qiao, Junfei . Iterative Q-Learning for Model-Free Optimal Control With Adjustable Convergence Rate . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS , 2024 , 71 (4) , 2224-2228 . |
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Abstract ：
This article investigates the adaptive prescribed-time consensus tracking control problem for nonlinear multi-agent systems (MASs), where the states of systems are unmeasured and the actuators suffer from the deception attacks. Firstly, a novel coordinate transformation technology is developed by introducing a time-varying constraint function, such that the prescribed-time tracking control problem of nonlinear MASs is converted into the constraint problem of the error variables. Then, a new attack compensator is proposed to address the unknown time-varying attack gains caused by the actuator deception attacks. Further, the state observers are designed to estimate the unavailable state variables and fuzzy-logic systems (FLSs) are employed to handle the unknown functions that exist within the systems. In addition, the attack compensator-based controller ensures the boundedness of all signals, while the error variables converge to the predefined region in a specified time. The upper bound of the whole tracking errors in the mean square sense can be decreased by selecting the appropriate design parameters. At last, the simulation example illustrates the availability of the developed control method. Note to Practitioners-In the industry, consensus tracking control of nonlinear MASs exists in many different systems, such as mobile robot networks, intelligent transportation management, surveillance and monitoring. Since the above systems operate in a network environment, the security problems of the systems cannot be ignored. Hence, considering the unmeasured states, the unknown functions, and the unknown time-varying attack gains existing simultaneously in the studied systems, it is a challenging and meaningful task to achieve the desired security control objectives. On the other hand, based on a time-varying constraint function, this article presents an adaptive prescribed-time consensus tracking control scheme for the nonlinear MASs under the deception attacks. It provides a viable strategy for industrial applications.
Keyword ：
deception attacks deception attacks prescribed-time control prescribed-time control state observers state observers Nonlinear multi-agent systems Nonlinear multi-agent systems fuzzy-logic systems fuzzy-logic systems
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| GB/T 7714 | Niu, Ben , Gao, Yahui , Zhang, Guangju et al. Adaptive Prescribed-Time Consensus Tracking Control Scheme of Nonlinear Multi-Agent Systems Under Deception Attacks [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 . |
| MLA | Niu, Ben et al. "Adaptive Prescribed-Time Consensus Tracking Control Scheme of Nonlinear Multi-Agent Systems Under Deception Attacks" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2024) . |
| APA | Niu, Ben , Gao, Yahui , Zhang, Guangju , Zhao, Xudong , Wang, Huanqing , Wang, Ding et al. Adaptive Prescribed-Time Consensus Tracking Control Scheme of Nonlinear Multi-Agent Systems Under Deception Attacks . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 . |
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Abstract ：
In this paper, the self-organizing heuristic dynamic programming algorithm is established to solve the approximate optimal control issue for affine nonlinear systems. A self-organizing neural network modeling method based on the particle swarm optimization algorithm is introduced to construct the model network. In contrast to the traditional backpropagation neural network, it has stronger adaptive ability and higher modeling precision for a variety of different complex systems, which substantially boosts the efficiency of the method. In addition, the action network and the critic network are constructed to obtain the approximate optimal control strategy and the optimal cost function, respectively. The convergence of the cost function is proved. It also proved that the state estimation errors and the weight vector estimation errors are uniformly ultimately bounded. Several nonlinear complex systems are selected in the experimental simulation to prove the efficiency of the method.
Keyword ：
Reinforcement learning Reinforcement learning Adaptive dynamic programming Adaptive dynamic programming Particle swarm optimization Particle swarm optimization Nonlinear systems Nonlinear systems Neural networks Neural networks
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| GB/T 7714 | Ma, Hongyu , Wang, Ding , Ren, Jin et al. Self-organizing neural intelligent control for nonlinear discrete-time systems with particle swarm optimization [J]. | NONLINEAR DYNAMICS , 2024 . |
| MLA | Ma, Hongyu et al. "Self-organizing neural intelligent control for nonlinear discrete-time systems with particle swarm optimization" . | NONLINEAR DYNAMICS (2024) . |
| APA | Ma, Hongyu , Wang, Ding , Ren, Jin , Qiao, Junfei . Self-organizing neural intelligent control for nonlinear discrete-time systems with particle swarm optimization . | NONLINEAR DYNAMICS , 2024 . |
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Abstract ：
In this article, we construct a novel generalized policy iteration framework to address optimal regulation problems for discrete-time nonlinear systems in a more efficient way. Relevant properties are investigated for the framework, including monotonicity and convergence of the iterative value function sequence as well as the admissibility of the iterative control policy. Additionally, an innovative approach is developed to seek an initial admissible control policy for the framework with an adjustable searching speed. Based on these, an evolving control algorithm is presented with stability guarantee. This algorithm employs iterative control policies for system control during the computation of the optimal control policy, as opposed to waiting for the generation of the optimal control policy before implementing control. Eventually, two simulation experiments are conducted with real-world physical backgrounds, in order to illustrate the performance of the proposed strategy.
Keyword ：
Admissible control policy Admissible control policy Optimal control Optimal control Adaptive dynamic programming Adaptive dynamic programming Adaptive critic designs Adaptive critic designs Evolving control Evolving control Generalized policy iteration Generalized policy iteration
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| GB/T 7714 | Huang, Haiming , Wang, Ding , Wang, Hua et al. Novel generalized policy iteration for efficient evolving control of nonlinear systems [J]. | NEUROCOMPUTING , 2024 , 608 . |
| MLA | Huang, Haiming et al. "Novel generalized policy iteration for efficient evolving control of nonlinear systems" . | NEUROCOMPUTING 608 (2024) . |
| APA | Huang, Haiming , Wang, Ding , Wang, Hua , Wu, Junlong , Zhao, Mingming . Novel generalized policy iteration for efficient evolving control of nonlinear systems . | NEUROCOMPUTING , 2024 , 608 . |
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Abstract ：
In this paper, the model-free robust control problem is investigated for nonlinear systems with a relaxed condition of initial admissible control. An advanced integral reinforcement learning method is developed, which merges the adaptive network-based fuzzy inference system (ANFIS) and pre-training of the initial weights. To loose the condition for choosing the initial control law, pre-training of initial weights is established by utilizing the ANFIS to provide the information corresponding to the system model, which is applicable to the model-free issue. Based on the actor-critic structure, the approximate optimal control law is obtained by employing adaptive dynamic programming. Redesigning the obtained control law, the robust controller can be derived to stabilize the system with the uncertain term. Eventually, two examples are utilized to verify the effectiveness of the constructed algorithm.
Keyword ：
adaptive dynamic programming adaptive dynamic programming robust control robust control integral reinforcement learning integral reinforcement learning adaptive network-based fuzzy inference systems adaptive network-based fuzzy inference systems
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| GB/T 7714 | Liu, Ao , Wang, Ding , Qiao, Junfei . An advanced robust integral reinforcement learning scheme with the fuzzy inference system [J]. | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2024 , 34 (17) : 11745-11759 . |
| MLA | Liu, Ao et al. "An advanced robust integral reinforcement learning scheme with the fuzzy inference system" . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 34 . 17 (2024) : 11745-11759 . |
| APA | Liu, Ao , Wang, Ding , Qiao, Junfei . An advanced robust integral reinforcement learning scheme with the fuzzy inference system . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2024 , 34 (17) , 11745-11759 . |
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Abstract ：
自适应评判技术已经广泛应用于求解复杂非线性系统的最优控制问题,但利用其求解离散时间非线性随机系统的无限时域最优控制问题还存在一定局限性.本文融合自适应评判技术,建立一种数据驱动的离散随机系统折扣最优调节方法.首先,针对宽松假设下的非线性随机系统,研究带有折扣因子的无限时域最优控制问题.所提的随机系统Q-learn-ing算法能够将初始的容许策略单调不增地优化至最优策略.基于数据驱动思想,随机系统Q-learning算法在不建立模型的情况下直接利用数据进行策略优化.其次,利用执行-评判神经网络方案,实现了随机系统Q-learning算法.最后,通过两个基准系统,验证本文提出的随机系统Q-learning算法的有效性.
Keyword ：
Q-learning Q-learning 随机最优控制 随机最优控制 离散系统 离散系统 数据驱动 数据驱动 自适应评判设计 自适应评判设计 神经网络 神经网络
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| GB/T 7714 | 王鼎 , 王将宇 , 乔俊飞 . 融合自适应评判的随机系统数据驱动策略优化 [J]. | 自动化学报 , 2024 , 50 (5) : 980-990 . |
| MLA | 王鼎 et al. "融合自适应评判的随机系统数据驱动策略优化" . | 自动化学报 50 . 5 (2024) : 980-990 . |
| APA | 王鼎 , 王将宇 , 乔俊飞 . 融合自适应评判的随机系统数据驱动策略优化 . | 自动化学报 , 2024 , 50 (5) , 980-990 . |
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Abstract ：
This article presents two new event-triggered control (ETC) schemes based on the online critic learning technique, which aims at tackling the optimal regulation problem of discrete-time constrained nonlinear systems with the disturbance input. First, a novel stability criterion condition is designed to obtain an initial admissible policy pair by using an offline iterative method under the time-triggered control framework. Then, starting from the stability of the constrained system, a nonperiodic ETC method and a periodic ETC method are developed by adopting an online learning algorithm. In addition, four kinds of neural networks are constructed for the implementation of the event-based online H(infinity )optimal control strategy. Finally, two experimental examples with physical backgrounds are provided to illustrate the effectiveness and superiority of the developed schemes.
Keyword ：
nonperiodic and periodic event-triggered control (ETC) nonperiodic and periodic event-triggered control (ETC) con- strained control con- strained control neural networks neural networks Adaptive dynamic programming (ADP) Adaptive dynamic programming (ADP) online H-infinity control online H-infinity control
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| GB/T 7714 | Wang, Ding , Hu, Lingzhi , Wang, Hua et al. Nonperiodic and Periodic Event-Triggered Online H∞ Control for Constrained Nonlinear Systems [J]. | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
| MLA | Wang, Ding et al. "Nonperiodic and Periodic Event-Triggered Online H∞ Control for Constrained Nonlinear Systems" . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2024) . |
| APA | Wang, Ding , Hu, Lingzhi , Wang, Hua , Qiao, Junfei . Nonperiodic and Periodic Event-Triggered Online H∞ Control for Constrained Nonlinear Systems . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
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Abstract ：
This paper designs two novel event-triggered control (ETC) schemes based on the critic learning technique for constrained discrete-time nonlinear systems. First, starting from the stability of the constrained system, a static ETC method is developed to reduce the computational burden. Then, a nonnegative dynamic variable is introduced into the static event-triggered mechanism, so as to establish the dynamic ETC method, which further improves the resource utilization rate and possesses the anti-interference ability. Moreover, a speedy value iteration architecture is designed to obtain an initially admissible optimal control policy, which can ensure the normal execution of the designed ETC methods. Finally, two experimental examples are provided to illustrate the effectiveness and superiority of the developed schemes.
Keyword ：
Static and dynamic event-triggered control Static and dynamic event-triggered control Adaptive dynamic programming Adaptive dynamic programming Constrained nonlinear systems Constrained nonlinear systems Speedy value iteration Speedy value iteration Adaptive critic control Adaptive critic control
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| GB/T 7714 | Hu, Lingzhi , Wang, Ding , Qiao, Junfei . Static/dynamic event-triggered learning control for constrained nonlinear systems [J]. | NONLINEAR DYNAMICS , 2024 , 112 (16) : 14159-14174 . |
| MLA | Hu, Lingzhi et al. "Static/dynamic event-triggered learning control for constrained nonlinear systems" . | NONLINEAR DYNAMICS 112 . 16 (2024) : 14159-14174 . |
| APA | Hu, Lingzhi , Wang, Ding , Qiao, Junfei . Static/dynamic event-triggered learning control for constrained nonlinear systems . | NONLINEAR DYNAMICS , 2024 , 112 (16) , 14159-14174 . |
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