Stochastic Point-to-Point Iterative Learning Tracking Without Prior Information on System Matrices
This paper contributes to a point-to-point iterative learning control problem for stochastic systems without prior information on system matrices. The stochastic approximation technique with gradient estimation by random difference is introduced to design the update law for input. It is strictly proved that the input sequence would converge almost surely to the optimal one, which minimizes the averaged tracking performance index. An illustrative simulation shows the effectiveness of the proposed algorithm.