Repetitive Learning Control for Class of Unidirectional Control Input Systems with Unknown Nonlinear Dynamics

doi:10.3901/JME.2017.22.109

Abstract

Abstract: An improved saturated learning-based control law is proposed to deal with a class of unidirectional control input systems to make the systems track the target trajectory asymptotically. Considering the systems with unidirectional control input only, the presented control law utilizes a saturated function and repetitive learning estimator to online learn and estimate the unknown periodic nonlinear dynamics, and compensate for the unknown dynamics to ensure the convergence of the tracking error asymptotically. At the same time, an adaptive notch filter(ANF) is utilized to online estimate frequency of the periodic dynamics in conjunction with the improved saturated learning-based control law. Differing from the previous methods, the presented control law only needs the uncertain dynamics is bounded (the structure, parameters and frequency of the uncertain dynamics is not need to be given beforehand). At last, the presented control law is applied to the electromagnetic suspension system(EMS), which can only provide a vertical upward electromagnetic force. The simulation results are given to demonstrate the effectiveness of the proposed control strategy.

Key words: electromagnetic suspension system, nonlinear control, repetitive learning control, unknown dynamics

CLC Number:

TP273

SUN Yougang, LI Wanli, JI Wen. Repetitive Learning Control for Class of Unidirectional Control Input Systems with Unknown Nonlinear Dynamics[J]. Journal of Mechanical Engineering, 2017, 53(22): 109-116.

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