Adaptive Fuzzy Terminal Sliding-Mode Observer with Experimental Applications

Abstract

In this paper, conventional gradient-descent-based adaptive fuzzy observer is improved by using the terminal sliding-mode theory for a class of nonlinear systems. The improvement is made in two ways: first, the switching term of the sliding-mode approach is added to the state of the observer. Second, the measurement error of the system is designed as the input of the observer instead of measured state. The stability of the observer and boundedness of the parameters are proved using Lyapunov approach. Contributions of the paper are summarized as follows: (i) the robustness and convergence properties of newly proposed observer are improved, (ii) the proposed adaptive fuzzy terminal sliding-mode observer, conventional adaptive fuzzy observer, adaptive neural-network observer, and Euler filtering approaches are compared in terms of their ability to estimate velocities of three real-time experimental systems reliably. The performance of the designed observers is discussed with root mean squared-error criterion where the proposed adaptive fuzzy terminal sliding-mode observer provided much accurate state estimation results than classical observers.