Ripple force compensation based on ILC–ASMC for linear servo system

Abstract

The ripple force, which displays intricate dynamic characteristics and is fundamentally nonlinear in linear servo systems, poses challenges in modeling and compensation. To achieve superior suppression of the ripple force, this paper proposes a new scheme called the iterative learning control (ILC)–adaptive sliding-mode control (ASMC), which combines ASMC and ILC. Initially, the servo system model is established, followed by an analysis of the ripple force mechanism and obtaining a simplified model. Then, an adaptive sliding-mode structure with a feed-forward component is proposed to enhance the servo dynamic response based on the design of a sliding-mode surface. In addition, a new ripple force compensator based on the ILC is designed, allowing for iterative learning from the previous period to accurately estimate the ripple force online. It is worth noting that the process is divided into the 0th iteration and the ith ( i ≥ 1) iteration to avoid the selection for initial values. The 0th iteration ensures initial system stability and provides the initial iteration values, while subsequent iterations approximate the real ripple force through iterative learning. Importantly, the controller and the compensator are combined through the same sliding-mode surface, obviously simplifying the system structure. Finally, experimental results validate the effectiveness of the proposed scheme.