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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.