Observer-based adaptive fuzzy-neural control for a class of MIMO nonlinear systems

An observer-based adaptive fuzzy-neural controller for a class of multi-input multi-output (MIMO) nonlinear systems is developed, in which observers are used to estimate the time derivatives of the system outputs. The proposed method has the merit that no differentiation of the system output is required in order to avoid the noise amplification associated with numerical differentiation. The stability of the observer-based adaptive fuzzy-neural controller is proven by using the strictly-positive-real Lyapunov theory. The overall adaptive scheme guarantees that all signals involved are bounded and the outputs of the closed-loop system asymptotically track the desired output trajectories. Finally, simulation results are provided to demonstrate the robustness and applicability of the proposed method.