Abstract:
This paper contributes to a point-to-point iterative learning control problem for stochastic systems without prior information on system matrices. The stochastic approxim...Show MoreMetadata
Abstract:
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.
Published in: IEEE Transactions on Automation Science and Engineering ( Volume: 14, Issue: 1, January 2017)