Extended Iterative Learning Control for Inconsistent Tracking Problems with Random Dropouts

For the inconsistent tracking problem where the given reference cannot be precisely generated by the system dynamics, it is generally required to achieve the best approximation to the reference trajectory. This study first clarify a general convergence condition of this best tracking performance for the traditional P-type learning control scheme. However, such performance may be no longer achievable under incomplete information such as data dropouts. To resolve this problem, a novel extended iterative learning control scheme is proposed for linear time-varying systems. In this scheme, the tracking errors are modified utilizing the output sampling, which has low-memory footprints and is data driven. The input sequence is proved convergent to the desired input, which produces the closest output to the desired reference in the least-square sense. Numerical simulations are provided to verify the theoretical results.