Title :
An adaptive approach to iterative learning control with experiments on an industrial robot
Author_Institution :
Dept. of Electr. Eng., Linkoping Univ., Linkoping, Sweden
Abstract :
An adaptive approach to Iterative Learning Control (ILC) based on a Kalman filter and an optimization of a quadratic criterion is presented. By estimating one of the design parameters in the Kalman filter an adaptive gain in the ILC updating formula is created. The proposed ILC design is compared with two other ILC schemes and they are all implemented on an industrial robot. The results show that the proposed adaptive ILC scheme is fast, and also robust since the gain is reduced as the error is decreased.
Keywords :
Kalman filters; adaptive control; industrial robots; iterative learning control; ILC updating formula; Kalman filter; adaptive ILC scheme; adaptive approach; adaptive gain; design parameter estimation; industrial robot; iterative learning control; quadratic criterion optimization; Adaptation models; Algorithm design and analysis; Computational modeling; Kalman filters; Mathematical model; Service robots; Iterative Learning Control; Robot Application; Robot Motion Control; Sampled Data Systems;
Conference_Titel :
Control Conference (ECC), 2001 European
Conference_Location :
Porto
Print_ISBN :
978-3-9524173-6-2
