Adaptive fuzzy controller for multivariable nonlinear state time-varying delay systems subject to input nonlinearities

• Published in: Fuzzy sets and systems Vol. 164; no. 1; pp. 45 - 65
• Main Authors: Boulkroune, A., M’Saad, M., Farza, M.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01.02.2011; Elsevier
• Subjects: Adaptive control; Adaptive control systems; Applied sciences; Automatic; Automatic Control Engineering; Circuit properties; Combinatorics; Combinatorics. Ordered structures; Computer Science; Computer science; control theory; systems; Convergence; Dead-zone; Delay; Designs and configurations; Digital circuits; Electric, optical and optoelectronic circuits; Electronic circuits; Electronics; Engineering Sciences; Exact sciences and technology; Fuzzy; Fuzzy logic; Fuzzy set theory; Fuzzy system; Information, signal and communications theory; Mathematical methods; Mathematics; MIMO nonlinear systems; Miscellaneous; Nonlinearity; Sciences and techniques of general use; Sector nonlinearity; Simulation; Telecommunications and information theory; Theoretical computing; Time-delay; Tracking errors; Variable-structure control; Sector nonlinearity; Fuzzy system; MIMO nonlinear systems; Time-delay; Adaptive control; Dead-zone; Variable-structure control; Exponential convergence; Error estimation; Decomposition method; Delay system; Fuzzy set; Non linear system; Engineering; Lyapunov functional; Information processing; Nonlinearity; Numerical stability
• ISSN: 0165-0114; 1872-6801
• DOI: 10.1016/j.fss.2010.09.001

In this paper, an adaptive fuzzy variable-structure controller is investigated for a class of uncertain multi-input multi-output (MIMO) nonlinear time-delay systems with both sector nonlinearities and dead-zones. A decomposition property of the control-gain matrix is fully exploited in the controller design and the stability analysis. The unknown time-varying delay uncertainties are compensated for using an appropriate Lyapunov–Krasovskii functional. The boundedness of all signals of the closed-loop system as well as the exponential convergence of the underlying tracking errors to an adjustable region are established. The effectiveness of the proposed fuzzy adaptive controller is illustrated throughout simulation results.