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Volume 10
Issue 12
10.3390/pr10122618
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Article

An Improved Adaptive Dynamic Programming Algorithm Based on Fuzzy Extended State Observer for Dissolved Oxygen Concentration Control

by
Xueliang Chen
,
Weimin Zhong
*,
Xin Peng
,
Peihao Du
and
Zhongmei Li
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
*
Author to whom correspondence should be addressed.
Processes 2022, 10(12), 2618; https://doi.org/10.3390/pr10122618
Submission received: 4 November 2022 / Revised: 18 November 2022 / Accepted: 2 December 2022 / Published: 7 December 2022
(This article belongs to the Special Issue Advanced Performance-Oriented Evaluation, Diagnosis and Fault-Tolerant Control Techniques for Industrial Processes)
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Abstract

To solve the anti-disturbance control problem of dissolved oxygen concentration in the wastewater treatment plant (WWTP), an anti-disturbance control scheme based on reinforcement learning (RL) is proposed. An extended state observer (ESO) based on the Takagi–Sugeno (T-S) fuzzy model is first designed to estimate the the system state and total disturbance. The anti-disturbance controller compensates for the total disturbance based on the output of the observer in real time, online searches the optimal control policy using a neural-network-based adaptive dynamic programming (ADP) controller. For reducing the computational complexity and avoiding local optimal solutions, the echo state network (ESN) is used to approximate the optimal control policy and optimal value function in the ADP controller. Further analysis demonstrates the observer estimation errors for system state and total disturbance are bounded, and the weights of ESNs in the ADP controller are convergent. Finally, the effectiveness of the proposed ESO-based ADP control scheme is evaluated on a benchmark simulation model of the WWTP.
Keywords: disturbance rejection; reinforcement learning (RL); extended state observer (ESO); adaptive dynamic programming (ADP); echo state network (ESO); wastewater treatment disturbance rejection; reinforcement learning (RL); extended state observer (ESO); adaptive dynamic programming (ADP); echo state network (ESO); wastewater treatment

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MDPI and ACS Style

Chen, X.; Zhong, W.; Peng, X.; Du, P.; Li, Z. An Improved Adaptive Dynamic Programming Algorithm Based on Fuzzy Extended State Observer for Dissolved Oxygen Concentration Control. Processes 2022, 10, 2618. https://doi.org/10.3390/pr10122618

AMA Style

Chen X, Zhong W, Peng X, Du P, Li Z. An Improved Adaptive Dynamic Programming Algorithm Based on Fuzzy Extended State Observer for Dissolved Oxygen Concentration Control. Processes. 2022; 10(12):2618. https://doi.org/10.3390/pr10122618

Chicago/Turabian Style

Chen, Xueliang, Weimin Zhong, Xin Peng, Peihao Du, and Zhongmei Li. 2022. "An Improved Adaptive Dynamic Programming Algorithm Based on Fuzzy Extended State Observer for Dissolved Oxygen Concentration Control" Processes 10, no. 12: 2618. https://doi.org/10.3390/pr10122618

APA Style

Chen, X., Zhong, W., Peng, X., Du, P., & Li, Z. (2022). An Improved Adaptive Dynamic Programming Algorithm Based on Fuzzy Extended State Observer for Dissolved Oxygen Concentration Control. Processes, 10(12), 2618. https://doi.org/10.3390/pr10122618

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