An observer-based adaptive type-2 fuzzy-neural controller for a class of MIMO systems with uncertainties

An observer-based adaptive controller based on a type-2 fuzzy neural network (type-2 FNN) is developed for a class of multi-input multi-output (MIMO) nonaffine nonlinear system. The interval type-2 fuzzy system is proposed in this paper as an alternative solution when a MIMO system has a large amount of uncertainties or the training data is corrupted by noise. By using implicit function theorem and Lyapunov theorem, the observer-based control law and the weight update law of the adaptive type-2 FNN controller are derived. Based on the design of the type-2 fuzzy neural network, the observer-based adaptive controller can improve its robustness to noise. In this paper, we prove that the proposed observer-based adaptive controller can guarantee that all signals involved are bounded and the outputs of the closed-loop system asymptotically track the desired output trajectories. Simulations results are reported to show the performance of the proposed control system mode and algorithms.