Fuzzy learning control of nonlinear systems using input-output linearization

The problem of learning control is addressed for a class of nonlinear systems for which there is no available analytic model. Based on the ability of fuzzy systems to approximate any nonlinear function, a global architecture is proposed where the unknown input-output linearizing controller is replaced by a fuzzy system whose rule conclusions are learned on line. A direct learning algorithm is implemented in which the parameter evolution is guided simultaneously by the tracking error and a prediction error of the fuzzy system output. During the learning phase, additive components, such as bounding control and sliding mode control are introduced to guarantee system´s stability and parameter convergence. Stability of the closed loop system is proved according to Lyapunov theory. Simulation results for the inverted pendulum are included to demonstrate the method applicability