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Appl. Sci. 2017, 7(4), 406; doi:10.3390/app7040406

Learning-Based Optimal Desired Compensation Adaptive Robust Control for a Flexure-Based Micro-Motion Manipulator

1,2
,
3
,
1,2,* and 1,2
1
Institute of Manufacturing Engineering, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
2
Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipment and Control, Tsinghua University, Beijing 100084, China
3
Institute of Instrument Science and Technology, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Academic Editor: Chien-Hung Liu
Received: 12 March 2017 / Revised: 11 April 2017 / Accepted: 12 April 2017 / Published: 17 April 2017
(This article belongs to the Section Mechanical Engineering)
View Full-Text   |   Download PDF [2682 KB, uploaded 19 April 2017]   |  

Abstract

Flexure-based micro-motion mechanisms activated by piezoelectric actuators have a wide range of applications in modern precision industry, due to their inherent merits. However, system performance is negatively affected by model uncertainty, disturbance and uncertain nonlinearity, such as the cross-coupling effect and the hysteresis of the actuator. This paper presents an integrated learning-based optimal desired compensation adaptive robust control (LODCARC) methodology for a flexure-based parallel micro-motion manipulator. The proposed LODCARC optimizes the reference trajectory used in the desired compensation adaptive robust control (DCARC) by iterative learning control (ILC), which can greatly compensate for the effect of repetitive disturbance and uncertainty. The proposed control approach was tested on the flexure-based micro-motion manipulator, with the comparative results of high-speed tracking experiments verifying that the proposed LODCARC controller can achieve excellent tracking and contouring performances with parametric adaption and disturbance robustness. Furthermore, the iterative reference optimization can effectively accommodate the effects of unmodeled repetitive uncertainty from the micro-motion system. This study provides a practical and effective technique for the flexure-based micro-motion manipulator to achieve high-precision motion. View Full-Text
Keywords: flexure-based manipulator; precision micro-motion; desired compensation adaptive robust control; reference optimization flexure-based manipulator; precision micro-motion; desired compensation adaptive robust control; reference optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Jia, S.; Jiang, Y.; Li, T.; Du, Y. Learning-Based Optimal Desired Compensation Adaptive Robust Control for a Flexure-Based Micro-Motion Manipulator. Appl. Sci. 2017, 7, 406.

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