Title :
Nonlinear systems identification and control using dynamic multi-time scales neural networks
Author :
Han, Xuan ; Xie, Wen-Fang
Author_Institution :
Dept. of Mech. & Ind. Eng., Concordia Univ., Montreal, QC, Canada
Abstract :
An on-line identification algorithm via dynamic neural networks with different time-scales followed by controller design is proposed for the dynamic systems with nonlinearity and uncertainty in this paper. The main contribution of the paper is that the Lyapunov function analysis, singularly perturbed technique and sliding mode methodology are combined to develop the control laws for trajectory tracking with consideration of the modeling error and disturbance. Simulations are given to demonstrate the effectiveness of the theoretical results.
Keywords :
Lyapunov methods; adaptive control; control nonlinearities; control system analysis; control system synthesis; identification; neurocontrollers; nonlinear control systems; position control; singularly perturbed systems; uncertain systems; variable structure systems; Lyapunov function analysis; adaptive control; control nonlinearities; dynamic neural network; modeling disturbance; modeling error; multitime scale; nonlinear system; online identification algorithm; singularly perturbed technique; sliding mode methodology; trajectory tracking; uncertain system; Algorithm design and analysis; Control systems; Heuristic algorithms; Lyapunov method; Neural networks; Nonlinear control systems; Nonlinear systems; Sliding mode control; Trajectory; Uncertainty;
Conference_Titel :
Automation and Logistics, 2009. ICAL '09. IEEE International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-4794-7
Electronic_ISBN :
978-1-4244-4795-4
DOI :
10.1109/ICAL.2009.5262970
