Optimal adaptive control for a plug flow reactor by integral reinforcement learning

This paper delves into optimal adaptive control for a plug flow reactor (PFR) partial differential equations (PDEs) model using the integral reinforcement learning (IRL) technique. Initially, it introduces a policy iterative algorithm designed to learn the solution of the corresponding matrix Riccati differential equation in real time. Notably, this method operates independently of explicit insight into the internal dynamics of the PFR process. Moreover, this paper establishes the convergence of the algorithm, contingent upon the initial action being stabilizing. Furthermore, an alternative algorithm is introduced to enhance the practical implementation of the IRL approach. The paper substantiates its findings through numerical simulations, demonstrating the efficacy of the developed algorithm.