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

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 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: 1) a disturbance invariant control scheme, where the sensitivity of the system response with respect to the control variable is estimated using a fuzzy neural model; and 2) a model predictive scheme in which disturbances are predicted online and the fuzzy neural model is used to predict the control action for a desired set point. The proposed controllers have been implemented for a nonlinear pH reactor through simulation. Simulation results show that the proposed scheme provides guaranteed tracking and regulatory performance