Energy-Selected Iterative Learning Control: A Novel Perspective to Analyze Precision Motion Control Tasks

Iterative learning control (ILC) achieves high control precision across various motion systems during repetitive tracking tasks by successively updating the compensation. In identical control circumstances, tracking errors primarily comprise repetitive components to be eliminated, induced by the input signals and regular system disturbances. In order to mitigate repetitive errors efficiently, conventional ILC methods treat the high-frequency components of tracking errors as nonrepetitive noise and disturbances, employing the low-pass filter to exclude these components. However, this specific frequency criterion is not entirely accurate, as there can be interference in the low-frequency range and effective components in the high-frequency range. Therefore, an energy-selected ILC is proposed in this article to identify these components, thereby enhancing the filtering validity. The proposed method proposes a novel energy criterion to construct the robust filter, improving the capability to distinguish repetitive components. Based on this advanced robust filter, the trajectory modification is designed as the learning filter to accelerate the convergence rate. The stability and convergence of this method are thoroughly proven and analyzed. Various comparative experiments have been conducted to illustrate the effectiveness of this novel energy-selected ILC approach. Generally, the proposed method has the following superiorities: it achieves high control precision across various motion scenarios; it broadens wide bandwidth applicable in high-frequency and nonsmooth circumstances; and it has an accurate error analysis suitable for practical applications. Meanwhile, it improves the control performance of classic ILC while maintaining its ease of implementation.