A new adaptive iterative learning control motivated by discrete-time adaptive control

Key Words:iterative learning control; adaptive control; time-varying parameters; nonidentical initial condition; non-identical trajectory

Abstract:In this work, a new discrete-time adaptive iterative learning control (AILC) approach is developed to deal with systems with time-varying parametric uncertainties. Using the analogy between the discrete time axis and the iterative learning axis, the new adaptive ILC can incorporate a Projection algorithm, thus the learning gain can be tuned iteratively along the learning axis and pointwisely along the time axis. The major advantage of the new AILC is that it can relax the identical conditions on the initial state and reference trajectory simultaneously. That is when the initial states are random and the reference is iteration-varying, the new AILC can achieve the pointwise convergence over a finite time interval asymptotically along the iteration learning axis.

Volume:4

Issue:6

Translation or Not:no