Resonance Suppression Based on Improved BFGS Notch Filter and Simplified Linear Triangular Model for Double-Inertia Servo Control

Chang, He; Lu, Shaowu; Zheng, Shiqi; Song, Bao

doi:10.1109/tmech.2023.3335341

Cited by 1 publication

(1 citation statement)

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“…The ripple force is mainly caused by cogging force, flux harmonics, and current errors, among which cogging force is the main reason (Zhang et al, 2022a). Typically, according to the compensation principle, the compensation strategies of the ripple force can be divided into two types: The first type is to reduce the ripple force as much as possible by changing the motor structure at the physical level, and the second type is to identify the ripple force through some observers (Bi et al, 2022; Chang et al, 2023; Cho and Nam, 2016; Han et al, 2019), adaptive control (Tan et al, 2004), neural network (Ding et al, 2022; Heydarzadeh et al, 2018), and robust control (Huang et al, 2021; Li et al, 2022; Liu et al, 2022b). The first-type method has different solutions for different motors, while increases structural complexity and motor cost, but produces limited effect, so the feasibility rate is not high.…”

Section: Introductionmentioning

confidence: 99%

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

Chu,

Lu,

Chang

et al. 2024

Transactions of the Institute of Measurement and Control

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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.

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