Neural network-based adaptive control for a class of chemical reactor systems with non-symmetric dead-zone

Abstract

In this paper, an adaptive predictive control algorithm is employed to controlling a class of continuous stirred tank reactor (CSTR) system. The main contribute of this paper is that the CSTR system are in discrete-time form and non-symmetric dead-zone inputs are considered here. The design parameters of control algorithm for the CSTR systems are not so much than before, such that the calculated amount of the control algorithm is less than before. By considering the Radial basis function neural networks (RBFNN), the unknown functions are approximated, the mean value theorem is utilized in the algorithm design process. Based on the Lyapunov analysis method, and choosing the design parameters appropriately, all the signals in the closed-loop system are proved to be semi-global uniformly ultimately bounded (SGUUB) and the tracking error is converged to a small compact set. A simulation example for CSTR systems is studied to demonstrate the effectiveness of the proposed approach.