Title :
NN-based backstepping control for strict-feedback block nonlinear system
Author_Institution :
Dept. of Autom. Control, Naval Aeronaut. Eng. Acad., Yantai, China
Abstract :
Radial-Basis-Function (RBF) NNs are used to identify the nonlinear parametric uncertainties of the system. Considering the known information, neural network and robust control are used to deal with the possible singularities of the controller. All the signals of the closed-loop system are uniform and ultimately bounded.
Keywords :
Lyapunov methods; adaptive control; closed loop systems; control system synthesis; feedback; function approximation; neurocontrollers; nonlinear systems; radial basis function networks; robust control; Lyapunov stability theory; RBF network; adaptive control; backstepping control; block nonlinear system; closed loop system; function approximation; neurocontrollers; parametric uncertainties; radial basis function neural network; robust control; strict feedback; Adaptive control; Aerospace engineering; Backstepping; Control systems; Design methodology; Neural networks; Nonlinear control systems; Nonlinear systems; Robust control; Uncertainty;
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
DOI :
10.1109/WCICA.2004.1342073
