文献类型：期刊文献

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

作者：Chen, Xueliang[1];Zhong, Weimin[1];Peng, Xin[1];Du, Peihao[1];Li, Zhongmei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2022

卷号：10

期号：12

外文期刊名：PROCESSES

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000904142400001)】；

基金：This work was supported by the National Natural Science Foundation of China (61890930-3), National Natural Science Fund for Distinguished Young Scholars (61925305), National Natural Science Foundation of China (62173145), Shanghai Pujiang Program (21PJ1402200) and Shanghai AI Lab.

语种：英文

外文关键词：disturbance rejection; reinforcement learning (RL); extended state observer (ESO); adaptive dynamic programming (ADP); echo state network (ESO); wastewater treatment

摘要：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.