Robust Adaptive Fuzzy Tracking Control for Pure-Feedback Stochastic Nonlinear Systems With Input Constraints

• Published in: IEEE transactions on cybernetics Vol. 43; no. 6; pp. 2093 - 2104
• Main Authors: Wang, Huanqing, Chen, Bing, Liu, Xiaoping, Liu, Kefu, Lin, Chong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive fuzzy control; Adaptive systems; Algorithms; Approximation methods; Artificial Intelligence; Backstepping; Control systems; Decision Support Techniques; Feedback; Fuzzy Logic; Nonlinear Dynamics; Nonlinear systems; nonsymmetric input saturation; Pattern Recognition, Automated - methods; pure-feedback nonlinear stochastic systems; Stability analysis; Stochastic Processes
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2013.2240296

This paper is concerned with the problem of adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with input saturation. To overcome the design difficulty from nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function; then, an adaptive fuzzy tracking controller based on the mean-value theorem is constructed by using backstepping technique. The proposed adaptive fuzzy controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.