Title :
Adaptive Neural Control of Non-Affine Pure-Feedback Systems
Author :
Wang, Cong ; Hill, David J. ; Ge, Shuzhi S.
Author_Institution :
Coll. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou
Abstract :
Controlling non-affine nonlinear systems is a challenging problem in the control community. In this paper, an adaptive neural control approach is presented for the completely non-affine pure-feedback system with only one mild assumption. By combining adaptive neural design with input-to-state stability (ISS) analysis and the small-gain theorem, the difficulty in controlling non-affine pure-feedback system is overcome by achieving the so-called "ISS-modularity" of the controller-estimator. The ISS-modular approach provides an effective way for controlling non-affine nonlinear systems with uncertainties. Simulation studies are included to demonstrate the effectiveness of the proposed approach
Keywords :
adaptive control; control system synthesis; feedback; neurocontrollers; nonlinear control systems; stability; uncertain systems; adaptive neural control; adaptive neural design; input-to-state stability analysis; nonaffine nonlinear systems; nonaffine pure-feedback systems; small-gain theorem; Adaptive control; Aerospace control; Automatic control; Backstepping; Control systems; Mechanical variables control; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control;
Conference_Titel :
Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation
Conference_Location :
Limassol
Print_ISBN :
0-7803-8936-0
DOI :
10.1109/.2005.1467031
