Title :
Composite adaptation for neural network-based controllers
Author :
Patre, Parag M. ; Bhasin, Shubhendu ; Wilcox, Zachary D. ; Dixon, Warren E.
Author_Institution :
Dept. of Mech. & Aerosp. Eng., Univ. of Florida, Gainesville, FL, USA
Abstract :
This paper presents a novel approach to design a composite adaptation law for neural networks that uses both the system tracking errors and a prediction error containing parametric information by devising an innovative swapping procedure that uses the recently developed Robust Integral of the Sign of the Error (RISE) feedback method. Semi-global asymptotic tracking is proven for an Euler-Lagrange system.
Keywords :
Lyapunov methods; adaptive systems; asymptotic stability; feedback; neurocontrollers; robust control; Euler-Lagrange system; composite adaptation; neural network-based controller; parametric information; prediction error; robust integral of the sign of the error feedback method; semiglobal asymptotic tracking; swapping procedure; system tracking error; Adaptive control; Asymptotic stability; Control systems; Error correction; Feedback; Neural networks; Neurofeedback; Robust control; Sliding mode control; Uncertainty;
Conference_Titel :
Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3871-6
Electronic_ISBN :
0191-2216
DOI :
10.1109/CDC.2009.5400453
