Guaranteed tracking and regulatory performance of nonlinear dynamic systems using fuzzy neural networks

Abstract

A new technique for the design of stable tracking and regulatory control systems for nonlinear systems using fuzzy neural networks is described. A class of nonlinear systems is considered where a few of the input variables can be manipulated (controlled) while the rest are considered as disturbances. System dynamics are modelled using a fuzzy neural network where each fuzzy operating region is associated with a series–parallel linear model. Two new control strategies are proposed using the Lyapunov synthesis approach. In one, a disturbance invariant control scheme is proposed where the sensitivity of the system response with respect to the control variable is estimated using fuzzy neural model. In the other a model predictive scheme is developed in which disturbances are predicted online and the fuzzy neural model is used to predict the control action for a desired setpoint. The proposed controllers have been implemented for a nonlinear pH reactor through simulation. In a pH control problem the acid and buffer flow rate are considered to be disturbances, while the base flow rate is controlled to maintain a constant pH. Simulation results show that the proposed scheme provides guaranteed tracking and regulatory performance.