Adaptive Neural Network Controller for Non-affine Nonlinear Systems and Its Application to CSTR

Abstract

In this paper, a new robust adaptive neural network controller (RANNC) is presented for a class of non-affine nonlinear systems in the presence of unknown nonlinearities and disturbances. Firstly, the existence of an ideal implicit feedback linearization control (IFLC) is established based on implicit function theory. Using Taylor series expansion, it is shown that the control of non-affine nonlinear systems is equivalent to the control of affine nonlinear systems in the neighborhood of the operating trajectory under mild conditions. Then, a robust adaptive neural network control scheme is presented for the transformed nonlinear systems by using neural networks as universal approximators for the unknown system nonlinearities. The proposed RANNC can guarantee that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to zero. Simulation studies on the control of a continuously stirred tank reactor (CSTR) system are used to show the effectiveness of the scheme.