Accelerated Iterative Learning Control for Linear Discrete Time Invariant Switched Systems

For a class of linear discrete time invariant stochastic switched systems with repetitive operation characteristics, a novel P-type accelerated iterative learning control algorithm with association correction is proposed. Firstly, the concrete form of accelerated learning law is given, and the generation of correction in the algorithm is explained in detail; secondly, on the premise that the switching sequence does not change along the iteration axis but only along the time axis, the convergence of the algorithm is strictly mathematically proved by using the super vector method, and the sufficient conditions for the convergence of the algorithm are given; finally, the theoretical results show that the convergence speed mainly depends on the controlled object, the proportional gain of the control law, the switching sequence, the correction coefficient, the association factor, and the size of the learning interval. Simulation results show that the proposed algorithm has a faster convergence speed than the traditional open-loop P-type algorithm under the same conditions.