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学者姓名：王鼎
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Abstract ：
In this paper, a novel value-iteration-based adaptive critic scheme is developed to address the H-infinity control problem for non-linear non-affine continuous-time (CT) systems with disturbances. Recurrent neural networks are employed to model the non-linear non-affine systems, thereby covering the unknown system dynamics. Based on the transformation of the optimal-robust problem, the H-infinity control issue is established to deal with disturbances. By introducing the accelerated factor, the value-iteration-based adaptive dynamic programming approach is developed to design controllers for non-linear CT systems subject to input constraints. The initial admissible control law is eliminated, which is a tough question for traditional policy iteration. Besides, the speed of the learning process is improved by relying on the accelerated factor. The corresponding convergence of the established method and the stability of the closed-loop system are presented by giving corresponding theorems. Finally, the effectiveness of novel value-iteration-based adaptive critic is validated by conducting two examples.
Keyword ：
H-infinity control H-infinity control value iteration value iteration adaptive dynamic programming adaptive dynamic programming optimal control optimal control
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| GB/T 7714 | Liu, Ao , Wang, Ding , He, Yingyun et al. Value-Iteration-Based Robust Adaptive Critic for Disturbed Non-Affine Continuous-Time Systems [J]. | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2025 . |
| MLA | Liu, Ao et al. "Value-Iteration-Based Robust Adaptive Critic for Disturbed Non-Affine Continuous-Time Systems" . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL (2025) . |
| APA | Liu, Ao , Wang, Ding , He, Yingyun , Ye, Kai , Qiao, Junfei . Value-Iteration-Based Robust Adaptive Critic for Disturbed Non-Affine Continuous-Time Systems . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2025 . |
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Abstract ：
In this paper, a novel self-triggered optimal tracking control method is developed based on the online action- critic technique for discrete-time nonlinear systems. First, an augmented plant is constructed by integrating the system state with the reference trajectory. This transformation redefines the optimal tracking control design as the optimal regulation issue of the reconstructed nonlinear error system. Subsequently, under the premise of ensuring the controlled system stability, a self-sampling function that depends solely on the sampling tracking error is devised, thereby determining the next triggering instant. This approach not only effectively reduces the computational burden but also eliminates the need for continuous evaluation of the triggering condition, as required in traditional event-based methods. Furthermore, the developed control method can be found to possess excellent triggering performance. The model, critic, and action neural networks are constructed to implement the online critic learning algorithm, enabling real-time adjustment of the tracking control policy to achieve optimal performance. Finally, an experimental plant with nonlinear characteristics is presented to illustrate the overall performance of the proposed online self-triggered tracking control strategy.
Keyword ：
Self-triggered mechanism Self-triggered mechanism Adaptive critic control Adaptive critic control Trajectory tracking Trajectory tracking Discrete-time nonlinear systems Discrete-time nonlinear systems Neural networks Neural networks Stability analysis Stability analysis
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| GB/T 7714 | Hu, Lingzhi , Wang, Ding , Wang, Gongming et al. Self-triggered neural tracking control for discrete-time nonlinear systems via adaptive critic learning [J]. | NEURAL NETWORKS , 2025 , 186 . |
| MLA | Hu, Lingzhi et al. "Self-triggered neural tracking control for discrete-time nonlinear systems via adaptive critic learning" . | NEURAL NETWORKS 186 (2025) . |
| APA | Hu, Lingzhi , Wang, Ding , Wang, Gongming , Qiao, Junfei . Self-triggered neural tracking control for discrete-time nonlinear systems via adaptive critic learning . | NEURAL NETWORKS , 2025 , 186 . |
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Abstract ：
Multi-objective evolutionary algorithms suffer from performance degradation when solving dynamic multi- objective optimization problems (DMOPs) with a new conditional configuration from scratch, which motivates the research on knowledge extraction. However, most knowledge extraction strategies only focus on obtaining effective information from a single knowledge source, while ignoring the useful information from other knowledge sources with similar properties. Motivated by this, a weighted multi-source knowledge extraction strategy-based dynamic multiobjective evolutionary algorithm is proposed. First, a similarity criterion based on angle information is constructed to quantify similarity between different source domains and the target domain. Second, a knowledge extraction technique is developed to select a specific number of individuals from each source domain using a distance metric. Third, a generation strategy based on dynamic weighting mechanism is proposed, which generates a certain number of individuals and merges these individuals into the initial population within the new environment. Finally, the comprehensive experiments are conducted on public DMOP benchmarks and demonstrate the devised method significantly outperforms the state-of-the-art competing algorithms.
Keyword ：
Change response Change response Evolutionary environment Evolutionary environment Dynamic multiobjective optimization Dynamic multiobjective optimization Evolutionary algorithms Evolutionary algorithms
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| GB/T 7714 | Xie, Yingbo , Qiao, Junfei , Wang, Ding . A weighted knowledge extraction strategy for dynamic multi-objective optimization [J]. | SWARM AND EVOLUTIONARY COMPUTATION , 2025 , 92 . |
| MLA | Xie, Yingbo et al. "A weighted knowledge extraction strategy for dynamic multi-objective optimization" . | SWARM AND EVOLUTIONARY COMPUTATION 92 (2025) . |
| APA | Xie, Yingbo , Qiao, Junfei , Wang, Ding . A weighted knowledge extraction strategy for dynamic multi-objective optimization . | SWARM AND EVOLUTIONARY COMPUTATION , 2025 , 92 . |
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Abstract ：
This article studies the dynamic event-triggered adaptive finite-time tracking control issue for a robotic manipulator (RM) system with disturbances. First, a new global prescribed performance function (PPF) is designed based on a scaling function such that the tracking error evolves within the constrained bounds and the restriction related to the initial conditions is removed. Then, the finite-time command filter (FTCF) is used to avoid the direct derivations of virtual controllers and the singularity issue of the conventional backstepping technique. Moreover, the filtering errors caused by the FTCF are removed by the designed error compensation mechanism. A novel dynamic event-triggered mechanism (DETM) using the dynamic auxiliary variable is designed to save communication resources. The proposed control scheme can guarantee that all signals of the RM are globally bounded within a finite time, and the tracking error can asymptotically reach zero. Finally, a simulation example and several comparative simulations show the validity of the proposed scheme.
Keyword ：
dynamic event-triggered control (DETC) dynamic event-triggered control (DETC) robotic manipulator (RM) robotic manipulator (RM) Backstepping Backstepping global prescribed performance global prescribed performance Adaptive systems Adaptive systems Convergence Convergence finite-time control (FTC) finite-time control (FTC) Manipulator dynamics Manipulator dynamics Transient analysis Transient analysis Event detection Event detection Asymptotic stability Asymptotic stability Vectors Vectors Symmetric matrices Symmetric matrices Heuristic algorithms Heuristic algorithms Adaptive asymptotic tracking control Adaptive asymptotic tracking control
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| GB/T 7714 | Sui, Jihang , Niu, Ben , Ou, Yongsheng et al. Event-Triggered Adaptive Finite-Time Control for a Robotic Manipulator System With Global Prescribed Performance and Asymptotic Tracking [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2025 . |
| MLA | Sui, Jihang et al. "Event-Triggered Adaptive Finite-Time Control for a Robotic Manipulator System With Global Prescribed Performance and Asymptotic Tracking" . | IEEE TRANSACTIONS ON CYBERNETICS (2025) . |
| APA | Sui, Jihang , Niu, Ben , Ou, Yongsheng , Zhao, Xudong , Wang, Ding . Event-Triggered Adaptive Finite-Time Control for a Robotic Manipulator System With Global Prescribed Performance and Asymptotic Tracking . | IEEE TRANSACTIONS ON CYBERNETICS , 2025 . |
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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 ：
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 ：
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 ：
For discrete-time unknown nonlinear dynamic systems, an online optimal tracking control scheme is developed in this paper, which is implemented by direct heuristic dynamic programming (HDP). To start with, a solution procedure is presented for tracking problems of discrete-time nonlinear systems. Then, the basic structure and mechanism of the direct HDP algorithm are described in detail, including the critic network and the action network. Moreover, based on the direct HDP algorithm, the implementation process of neural networks is provided. Finally, the direct HDP algorithm is applied to enable the original system to track the reference trajectory. The effectiveness of the algorithm is proved in solving tracking problems and online optimal tracking control is achieved.
Keyword ：
discrete-time nonlinear systems discrete-time nonlinear systems optimal tracking control optimal tracking control adaptive dynamic programming adaptive dynamic programming direct heuristic dynamic programming direct heuristic dynamic programming
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| GB/T 7714 | Liu, Nan , Wang, Ding , Ren, Jin et al. Online Nonaffine Optimal Tracking Control via Direct Heuristic Dynamic Programming [J]. | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 , 2024 : 1748-1753 . |
| MLA | Liu, Nan et al. "Online Nonaffine Optimal Tracking Control via Direct Heuristic Dynamic Programming" . | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 (2024) : 1748-1753 . |
| APA | Liu, Nan , Wang, Ding , Ren, Jin , Zhao, Mingming . Online Nonaffine Optimal Tracking Control via Direct Heuristic Dynamic Programming . | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 , 2024 , 1748-1753 . |
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