Learning control algorithms for tracking “slowly” varying trajectories
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
International Journal of Systems Science
Iterative learning controller for trajectory tracking tasks based on experience database
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
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Prediction-based Iterative Learning Control (PILC) is proposed in this paper for a class of time varying nonlinear uncertain systems. Convergence of PILC is analyzed and the uniform boundedness of tracking error is obtained in the presence of uncertainty and disturbances. It is shown that the learning algorithm not only guarantees the robustness, but also improves the learning rate despite the presence of disturbances and slowly varying desired trajectories in succeeding iterations. The effectiveness of the proposed PILC is presented by simulations.