Adaptive Critic Control Design With Knowledge Transfer for Wastewater Treatment Applications

Wang, Ding; Li, Xin; Zhao, Mingming; Qiao, Junfei

doi:10.1109/tii.2023.3278875

Cited by 27 publications

(4 citation statements)

References 27 publications

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“…The VI algorithm iterates between the above two formulas. According to Reference 30, the monotonicity of the iterative value function sequence can be determined by the relationship between J (0) (e k , s k ) and J (1) (e k , s k ). For simplicity, the iterative value function sequence of the general VI scheme is denoted by {J (𝓁) }.…”

Section: The General VI Schemementioning

confidence: 99%

“…For any e k and s k , let the value function and the control policy be updated by (11) and (12), respectively. If J (0) (e k , s k ) ≤ J (1) (e k , s k ), the value function sequence is monotonically nondecreasing and…”

Section: Lemma 1 (Compare Reference 30)mentioning

confidence: 99%

“…It simulates the learning mechanism of human in real environment, and interacts with the environment so as to generate rewards or punishments and update the control policy. It has been widely used in wastewater treatment, 1,2 frequency control of power systems, 3 and the game theory. 4 With great relation to RL, adaptive dynamic programming (ADP), including value iteration (VI) and policy iteration (PI), can be used to solve optimal control problems well.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved value iteration for nonlinear tracking control with accelerated learning

Wang,

et al. 2024

Intl J Robust & Nonlinear

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In this article, an adaptive critic scheme with a novel performance index function is developed to solve the tracking control problem, which eliminates the tracking error and possesses the adjustable convergence rate in the offline learning process. Under some conditions, the convergence and monotonicity of the accelerated value function sequence can be guaranteed. Combining the advantages of the adjustable and general value iteration schemes, an integrated algorithm is proposed with a fast guaranteed convergence, which involves two stages, namely the acceleration stage and the convergence stage. Moreover, an effective approach is given to adaptively determine the acceleration interval. With this operation, the fast convergence of the new value iteration scheme can be fully utilized. Finally, compared with the general value iteration, the numerical results are presented to verify the fast convergence and the tracking performance of the developed adaptive critic design.

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Section: The General VI Schemementioning

confidence: 99%

Section: Lemma 1 (Compare Reference 30)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved value iteration for nonlinear tracking control with accelerated learning

Wang,

et al. 2024

Intl J Robust & Nonlinear

Self Cite

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show abstract

“…In fact, the regulation problem can be seen as a simplified special case of the tracking problem. To overcome these challenges, some scholars have started to develop optimal learning control algorithms based on the ADP framework for solving tracking problems [26] . Currently, tracking control algorithms based on the ADP framework can be categorized into three groups.…”

Section: Introductionmentioning

confidence: 99%

Multi-step policy evaluation for adaptive-critic-based tracking control towards nonlinear systems

Li,

Ren,

Wang

2023

Complex Engineering Systems

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Currently, there are a large number of tracking problems in the industry concerning nonlinear systems with unknown dynamics. In order to obtain the optimal control policy, a multi-step adaptive critic tracking control (MsACTC) algorithm is developed in this paper. By constructing a steady control law, the tracking problem is transformed into a regulation problem. The MsACTC algorithm has an adjustable convergence rate during the iterative process by incorporating a multi-step policy evaluation mechanism. The convergence proof of the algorithm is provided. In order to implement the algorithm, three neural networks are built, including the model network, the critic network, and the action network. Finally, two numerical simulation examples are given to verify the effectiveness of the algorithm. Simulation results show that the MsACTC algorithm has satisfactory performance in terms of the applicability, tracking accuracy, and convergence speed.

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An advanced robust integral reinforcement learning scheme with the fuzzy inference system

Liu,

Wang,

Qiao

2024

Intl J Robust & Nonlinear

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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.

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Adaptive Critic Control Design With Knowledge Transfer for Wastewater Treatment Applications

Cited by 27 publications

References 27 publications

Improved value iteration for nonlinear tracking control with accelerated learning

Improved value iteration for nonlinear tracking control with accelerated learning

Multi-step policy evaluation for adaptive-critic-based tracking control towards nonlinear systems

An advanced robust integral reinforcement learning scheme with the fuzzy inference system

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