Title :
Adaptive dynamic surface control for perturbed nonlinear time-delay systems using neural network
Author :
Ji, Geng ; Hua, Xuebing
Author_Institution :
Sch. of Math. & Inf. Eng., Taizhou Univ., Linhai, China
Abstract :
This paper investigates the adaptive neural network (NN) control problem for a class of perturbed strict-feedback nonlinear systems with unknown time delays. Radial basis function (RBF) neural networks are used to approximate unknown nonlinear functions. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown time delay terms have been compensated. Dynamic surface control (DSC) technique is used to overcome the problem of “explosion of complexity” in backstepping design procedure. In addition, the semiglobally uniformly ultimate boundedness of all the signals in the closed-loop system is proved. Simulation study has been conducted to show the effectiveness of the proposed scheme.
Keywords :
Lyapunov methods; adaptive control; approximation theory; closed loop systems; delays; feedback; neurocontrollers; nonlinear control systems; nonlinear functions; perturbation techniques; radial basis function networks; Lyapunov Krasovskii functionals; adaptive dynamic surface control; adaptive neural network control problem; backstepping design procedure; closed loop system; nonlinear function approximation; perturbed nonlinear time delay systems; perturbed strict feedback nonlinear systems; radial basis function neural networks; Adaptive systems; Artificial neural networks; Backstepping; Complexity theory; Delay effects; Explosions;
Conference_Titel :
Information Science and Technology (ICIST), 2011 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-9440-8
DOI :
10.1109/ICIST.2011.5765121
