Robust predictive iterative learning control for linear time-varying systems

A novel control technique is proposed by combining iterative learning control (ILC) with model predictive control (MPC) for a trajectory tracking problem of linear time-varying systems (LTVSs) in this paper. By introducing prediction mechanism along both of the iteration and time directions instead of only one direction, the control law is successively updated along two directions by learning from the predictive and compensatory information. The new control law can improve convergence speed and effectively suppress the uncertainties in initial states, external disturbances, and model mismatches. The convergence conditions are rigorously derived and analyzed as well. An example is given to illustrate the effectiveness of the obtained results.