Neuro-adaptive output feedback control of the continuous polymerization reactor subjected to parametric uncertainties and external disturbances

Abstract

This paper proposes an adaptive neural network based output feedback backstepping fast terminal sliding mode control (NN-BFTSMC) for continuous polymerization reactor with external disturbances and parametric uncertainties. Firstly, neural networks (NN) are employed to approximate the uncertain nonlinear functions. Next, the average molecular weight and the reactor temperature tracking controllers are designed based on the finite-time NN-BFTSMC. In addition, the NN-BFSMC effectively estimates and compensates the upper bounds of the external disturbances. Moreover, the chattering effect is eliminated without losing the robustness accuracy. The finite-time stability of the closed-loop system is proved by Lyapunov theory. At last, numerical simulations and comparative studies are introduced to illustrate the superior performance of the proposed method.