Robust adaptive neural network prescribed performance control for uncertain CSTR system with input nonlinearities and external disturbance

In this paper, the control problem for continuously stirred tank reactor (CSTR) systems with input nonlinearities and unknown disturbances is addressed. To ensure constraints satisfaction on the input and the output of CSTR, we employ a system transformation technique to transform the original constrained model of CSTR into an equivalent unconstrained model, whose stability is sufficient to achieve the tracking control of the original CSTR system with a priori prescribed performance. To deal with two kinds of input nonlinearities, two neural networks (NNs)-based adaptive controllers are proposed. In the first approach, a robust control term is introduced to overcome the effects of the unknown input dead zone; however, in the second approach, an anti-windup compensator is designed to reduce the influence of the actuator saturation. In the proposed control methods, NNs are employed to approximate the unknown dynamics of CSTR and additional adaptive compensators are introduced to cope with NNs approximation errors and external disturbance. The proposed adaptive neural tracking controllers are designed with only one adaptive parameter by using the second Lyapunov stability method. In comparison with the traditional back-stepping-based techniques, usually used in CSTR control, the structures of the proposed controllers are much simpler with few design parameters since the causes for the problem of complexity growing are completely eliminated. Simulation results are presented to show the effectiveness of the proposed controllers.