Reinforcement Learning-Boosted Event-Triggered Reliability Control for Uncertain CSTR System With Asymmetric Constraints

Abstract

From the perspective of industrial production reliability, a robust event-triggered (ET) control strategy is presented for uncertain continuous stirred tank reactor (CSTR) system with asymmetric input constraints. To begin with, we propose a nonquadratic performance function to transform the robust control issue by constructing the relevant auxiliary dynamics. For effectively mitigating the pressure of data transmission and controller execution, a dynamic ET scheme (DETS) with an adjustable threshold function is adopted. Subsequently, we formulate the DETS-based Hamilton–Jacobi–Bellman (DET-HJB) equation according to optimality theory. In addition, a DETS-assisted reinforcement learning algorithm with a unique critic neural network can efficiently tackle the derived DET-HJB equation. Meanwhile, the corresponding critic weight is regulated on the basis of gradient descent technique and experience replay approach. By presenting a rigorous analysis under two situations, the uniform ultimate boundedness of auxiliary dynamics and weight approximation error can be ensured. Eventually, the feasibility of the proposed algorithm is demonstrated by experimental results of CSTR system.