Iterative Learning Control based on Sliding Modes

Y.-S. Lu (Taiwan)


Sliding Mode, Iterative Learning, Repetitive Control


An iterative learning scheme based on sliding-mode techniques is proposed to enhance the system performance of pole-placement feedback design for uncertain systems subject to periodic reference signals and external disturbances. In the ideal case, which has no system uncertainty, the pole-placement design achieves desired system performance by placing closed-loop poles at desired locations. However, its performance degrades rapidly with the presence of external disturbance or system uncertainty, referred to as perturbation. To rectify the perturbation in the nominal form of pole-placement design, a feedforward control, constructed iteratively by the proposed learning process, is incorporated as an additional input. The proposed scheme required neither precise information on system parameters nor measurement of the time-derivative of state vector. Chatter and the unfavorable effects of conservative bounds on system perturbation, often encountered in conventional sliding-mode control, are diminished by the proposed scheme, while the existence of a sliding mode is ensured throughout an entire response. Using the invariance property of a sliding mode, sufficient conditions for the stability of the proposed iterative learning system are derived for uncertain systems subject to periodic disturbance. Simulation results show the rapid convergence of tracking error and the robustness of convergence rate to system uncertainty.

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