PREDICTIVE OPTIMAL ITERATIVE LEARNING CONTROL

A new optimization-based iterative learning control algorithm is propo sed and its properties derived. An important characteristic of this al gorithm is that it uses present and future predicted errors to compute the current control, in a similar manner to model-based predictive co ntrol using a receding horizon. In particular, it enables the algorith m designer to achieve good control over convergence rate. The actual i mplementation has a multimodel structure but uses standard linear quad ratic regulator methods for a causal formulation (in the iterative lea rning sense) of what is originally a non-causal algorithm. The results are illustrated by simulations.