Quasi-Newton-type optimized iterative learning control for discrete linear time invariant systems

In this paper, a quasi-Newton-type optimized iterative learning control (ILC)algorithm is investigatedfor a class of discrete linear time-invariant systems. The proposed learningalgorithm is to update the learning gain matrix by a quasi-Newton-type matrix instead of theinversion of the plant. By means of the mathematical inductive method, the monotoneconvergence of the proposed algorithm is analyzed, which showsthat the tracking error monotonously converges to zero after afinite number of iterations. Compared with the existing optimized ILCalgorithms, due to the superlinear convergence of quasi-Newton method, the proposed learning law operates with a fasterconvergent rate and is robust to the ill-condition of thesystem model, and thus owns a wide range of applications.Numerical simulations demonstrate the validity andeffectiveness.