Filtering-error rectified iterative learning control for systems with input dead-zone
YAN Qiu-zhen
SUN Ming-xuan
CAI Jian-ping
Abstract：This paper presents the filtering-error rectified adaptive iterative learning control algorithms to tackle the trajectory-tracking problem for a class of nonparametric uncertain systems with unknown input dead-zone, in the presence of arbitrary initial states. To overcome the arbitrary initial states, two construction programs of the rectified filtering-error are proposed. Two iterative learning controllers are designed by applying Lyapunov synthesis, suitable to the case that the lower bound of the dead-zone's slope is known and the case that it is unknown respectively, dealing with the nonparametric uncertainties and the unknown dead-zone nonlinearity according to the robust learning strategy. As iteration increases, the filtering error converges to zero on the specified interval. The rectified filtering-error signal can be simply constructed, and the proposed learning control scheme, whose effectiveness is demonstrated in the presented numerical results, is easy for implementation.
Keywords：dead-zoneiterative learning controlinitial condition problemnonparametric uncertaintiesLyapunov approach
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 77-84 )