Generalized Iterative Super-Twisting Sliding Mode Control: A Case Study on Flexure-Joint Dual-Drive H-Gantry Stage

TL;DR

This paper presents a novel, model-free control method combining super-twisting and iterative learning for mechatronic systems with uncertain dynamics, validated through a case study on an H-gantry stage.

Contribution

It introduces a new intelligent control approach that does not require system parameters, enhancing motion performance in uncertain mechatronic systems.

Findings

Proven convergence of the control method.

Effective control demonstrated on a dual-drive H-gantry stage.

Improved motion accuracy without system modeling.

Abstract

Mechatronic systems are commonly used in the industry, where fast and accurate motion performance is always required to guarantee manufacturing precision and efficiency. Nevertheless, the system model and parameters are difficult to be obtained accurately. Moreover, the high-order modes, strong coupling in the multi-axis systems, or unmodeled frictions will bring uncertain dynamics to the system. To overcome the above-mentioned issues and enhance the motion performance, this paper introduces a novel intelligent and totally model-free control method for mechatronic systems with unknown dynamics. In detail, a 2-degree-of-freedom (DOF) architecture is designed, which organically merges a generalized super-twisting algorithm with a unique iterative learning law. The controller solely utilizes the input-output data collected in iterations such that it works without any knowledge of the system parameters. The rigorous proof of convergence ability is given and a case study on flexture-joint dual-drive H-gantry stage is shown to validate the effectiveness of the proposed method.