Data-driven mode shape selection and model-based vibration suppression of 3-RRR parallel manipulator with flexible actuation links

TL;DR

This paper introduces a data-driven approach to identify mode shapes and a model-based control strategy for vibration suppression in a flexible 3-RRR parallel manipulator, improving accuracy and vibration control performance.

Contribution

It combines data-driven algorithms with model-based control to accurately identify mode shapes and effectively suppress vibrations in flexible manipulators.

Findings

Data-driven mode shape identification using DMD and SINDy algorithms.

Proposed neural network-based observer improves vibration suppression.

Model-based controller outperforms model-free methods in accuracy.

Abstract

The mode shape function is difficult to determine in modeling manipulators with flexible links using the assumed mode method. In this paper, for a planar 3-RRR parallel manipulator with flexible actuation links, we provide a data-driven method to identify the mode shape of the flexible links and propose a model-based controller for the vibration suppression. By deriving the inverse kinematics of the studied mechanism in analytical form, the dynamic model is established by using the assumed mode method. To select the mode shape function, the software of multi-body system dynamics is used to simulate the dynamic behavior of the mechanism, and then the data-driven method which combines the DMD and SINDy algorithms is employed to identify the reasonable mode shape functions for the flexible links. To suppress the vibration of the flexible links, a state observer for the end-effector is constructed by a neural network, and the model-based control law is designed on this basis. In comparison with the model-free controller, the proposed controller with developed dynamic model has promising performance in terms of tracking accuracy and vibration suppression.