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学者姓名：王鼎
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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 等. "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 ：
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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Abstract ：
This article mainly considers the adaptive secure bipartite consensus tracking control (BCTC) problem for nonlinear multiagent systems (MASs) under false data injection (FDI) attacks with predefined accuracy. Since FDI attacks produce unknown attack gains, which increases the difficulty of the controller design, an adaptive secure control strategy is given based on the essential property of Nussbaum functions. By improving the traditional coordinate transformation in the current literatures that can only achieve unilateral consensus control, a backstepping-based control algorithm is put forward attaining bilateral consensus control. In addition, the appropriate Lyapunov functions are generated by a class of non-negative functions to construct the adaptive secure bipartite consensus controllers, which not only makes certain that the bilateral errors ultimately converge to a predefined interval, but also guarantees that all the closed-loop signals within the investigated system are bounded. Conclusively, a practical example is provided to validate the effectiveness of the proposed control strategy.
Keyword ：
predefined accuracy predefined accuracy Bipartite consensus tracking control (BCTC) Bipartite consensus tracking control (BCTC) nonlinear multiagent systems (MASs) nonlinear multiagent systems (MASs) false data injection (FDI) attacks false data injection (FDI) attacks
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| GB/T 7714 | Wen, Luyao , Niu, Ben , Wang, Ding et al. Adaptive Secure Bipartite Consensus Tracking Control for Nonlinear Multiagent Systems Under FDI Attacks With Predefined Accuracy [J]. | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
| MLA | Wen, Luyao et al. "Adaptive Secure Bipartite Consensus Tracking Control for Nonlinear Multiagent Systems Under FDI Attacks With Predefined Accuracy" . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2024) . |
| APA | Wen, Luyao , Niu, Ben , Wang, Ding , Jiang, Yuqiang , Liu, Chao , Wang, Huanqing . Adaptive Secure Bipartite Consensus Tracking Control for Nonlinear Multiagent Systems Under FDI Attacks With Predefined Accuracy . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
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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 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 ：
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 et al. "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 ：
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 ：
The efficiency and economy of the nonlinear optimal control process in wastewater treatment plants are two crucial indicators, corresponding to achieving the control objective faster and reducing the preset cost function. To accomplish this, an integrated online Q-learning (IOQL) algorithm, driven by a prior policy and an exploration policy, is proposed for nonlinear discrete-time systems characterized by nonaffine features and unknown structures. First, a prior policy based on historical or artificial experience is designed to reduce the training time of the controller and provide a more stable learning environment. By introducing a weighting factor, the impact of the prior policy on the overall learning process can be adjusted. Second, an exploration policy is trained online through new experiences collected from the real environment. By leveraging two policies, we can swiftly and smoothly adjust the critic network for approximating the cost function and the action network for approximating the exploration policy, which can gradually enhance the control outcomes. Third, a stability condition with reasonable bounds is presented for the IOQL design. Finally, experimental and comparative results pertaining to a wastewater treatment plant, specifically evaluating learning speed and cost consumption, clearly demonstrate the significant advantages and superiority of the IOQL algorithm.
Keyword ：
Adaptive critic control Adaptive critic control nonlinear optimal control nonlinear optimal control online Q-learning online Q-learning wastewater treatment applications wastewater treatment applications adaptive dynamic programming adaptive dynamic programming
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| GB/T 7714 | Zhao, Mingming , Wang, Ding , Ren, Jin et al. Integrated Online Q-Learning Design for Wastewater Treatment Processes [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 . |
| MLA | Zhao, Mingming et al. "Integrated Online Q-Learning Design for Wastewater Treatment Processes" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2024) . |
| APA | Zhao, Mingming , Wang, Ding , Ren, Jin , Qiao, Junfei . Integrated Online Q-Learning Design for Wastewater Treatment Processes . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 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 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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