Performance Enhancement of Learning Tracking Systems Over Fading Channels With Multiplicative and Additive Randomness

This paper applies learning control to repetitive systems over fading channels at both output and input sides to improve tracking performance without applying restrictive fading conditions. Both multiplicative and additive randomness of the fading channel are addressed, and the effects of fading communication on the data are carefully analyzed. A decreasing gain sequence and a moving-average operator are introduced to modify the generic learning control algorithm to reduce the fading effect and improve control system performance. Results reveal that the tracking error converges to zero in the mean-square sense as the iteration number increases. Illustrative simulations are presented to verify the theoretical results.