Robust model reference adaptive control of nonlinear systems using fuzzy systems

The paper presents a model reference adaptive control architecture for a class of nonlinear dynamic systems, which are either ill-defined or rather complex. The architecture employs fuzzy systems to model the unknown plant nonlinearity. Then an adaptive law is constructed based on these fuzzy systems. Global asymptotic stability of the algorithm is established in the Lyapunov sense and is shown to be robust with respect to the modelling en or, which resulted from the fuzzy systems' approximate representation of the nonlinear plant. Simulation results for an inverted pendulum system show that the proposed control architecture provides fast convergence.