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

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.