Robust adaptive fuzzy control of nonlinear systems with input saturation based on DSC and K-filter techniques

In this paper, a novel adaptive fuzzy output feedback control scheme is presented for a class of SISO uncertain nonlinear systems in the presence of input saturation. The control design is achieved by combining adaptive fuzzy K-filter observer technique and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The proposed controller can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of fuzzy adaptive parameters is reduced to one, and three problems of “computational explosion”, “dimension curse”, “unmeasured states” are solved. A numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.