Title :
Adaptive iterative learning neural control: An error-tracking approach
Author :
Mingxuan Sun ; Guofeng Zhang ; Tao Wu
Author_Institution :
Coll. of Inf. Eng., Zhejiang Univ. of Technol., Hangzhou, China
Abstract :
In this paper, the problem of adaptive iterative learning control using neural networks is addressed by an error tracking approach for systems with arbitrary initial states. The desired error trajectory is pre-specified at the design stage. It is shown that the tracking error is ensured to converge to an adjustable neighborhood of a pre-specified one. The performance improvement is made possible in case of non-zero approximation error, due to the use of an appropriate Lyapunov functional adopted in the design.
Keywords :
Lyapunov methods; adaptive control; approximation theory; control system synthesis; iterative methods; learning systems; neurocontrollers; Lyapunov functional; adaptive iterative learning neural control; adjustable neighborhood; arbitrary initial states; design stage; error trajectory; error-tracking approach; neural networks; nonzero approximation error; Automation; Initial conditions; adaptive iterative learning control; neural networks; nonlinear systems;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7052750
