Adaptive Neural Control of Nonlinear Systems With Unknown Control Directions and Input Dead-Zone

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 48; no. 11; pp. 1897 - 1907
• Main Authors: Wang, Huanqing, Karimi, Hamid Reza, Liu, Peter Xiaoping, Yang, Hongyan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive algorithms; Adaptive control; Adaptive neural control; Adaptive systems; Backstepping; Basis functions; Computer simulation; Control algorithms; Control systems; Control theory; Dynamical systems; Feedback; input dead-zone nonlinearity; Neural networks; Nonlinear control; Nonlinear dynamical systems; Nonlinear systems; Nonlinearity; Radial basis function; unavailable control gains; Viability
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2017.2709813

This paper presents an adaptive neural control approach for nonstrict-feedback nonlinear systems in presence of unmodeled dynamics, unknown control directions and input dead-zone nonlinearity. To handle the difficulty due to uncertain control directions, Nussbaum gain functions are applied. Based on the structural characteristic of radial basis function neural networks, a backstepping-based adaptive neural control algorithm is developed. The main contributions of this paper lie in the fact that a backstepping-based neural control algorithm is developed for nonstrict-feedback nonlinear systems with unmodeled dynamics, unknown control directions and actuator dead-zone, and the total number of adaptive laws is not greater than the order of control system. As a beneficial result, the controller is much easier to be implemented in practice with less computational burden. A simulation example is given to reveal the viability of the presented approach. It is demonstrated by both theoretical analysis and simulation study that the presented control strategy ensures the semiglobally uniform ultimate boundedness of all closed-loop system signals.