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Precision Motion Control of a Linear Permanent Magnet Synchronous Machine Based on Linear Optical-Ruler Sensor and
1Department of Electrical Engineering, National United University, Miaoli, 36063, Taiwan. jhlin@nuu.edu.tw.
Sensors (Basel, Switzerland)
|October 11, 2018
Summary
A new adaptive control system enhances linear motor positioning accuracy. This method uses an Elman neural network and backstepping control to overcome nonlinear friction and improve mover positioning precision.
Area of Science:
- Control Systems Engineering
- Robotics and Automation
- Machine Learning Applications
Background:
- Linear permanent magnet synchronous machines (LPMSMs) face challenges in precise mover positioning due to nonlinear friction and uncertainties.
- Traditional linear controllers struggle to achieve high accuracy in LPMSM systems.
- Accurate position sensing, using a 1um precision optical-ruler sensor, is crucial for LPMSM performance.
Purpose of the Study:
- To develop an advanced control system for enhancing the mover positioning precision of LPMSMs.
- To address the limitations of linear controllers in the presence of system nonlinearities and uncertainties.
- To improve the dynamic control performance and robustness of LPMSM drive systems.
Main Methods:
- Implementation of an adaptive amended Elman neural network backstepping (AAENNB) control system.
- Utilization of a backstepping scheme for controlling the tracing motion of the LPMSM.
- Development of an adaptive amended Elman neural network uncertainty observer (AAENNUO) to estimate lumped uncertainties.
- Application of Lyapunov stability theorem for on-line parameter training of the amended Elman neural network (AENN) using adaptive laws.
- Employing a modified particle swarm optimization (PSO) algorithm to optimize AENN learning rates.
Main Results:
- The proposed AAENNB control system demonstrated superior performance in mover positioning for LPMSMs.
- The system exhibited enhanced dynamic control performance and robustness against uncertainties.
- Experimental results verified the effectiveness and improved precision achieved by the control strategy.
- The AAENNUO effectively estimated system uncertainties, contributing to better control outcomes.
Conclusions:
- The AAENNB control system provides a robust and precise solution for LPMSM mover positioning.
- The integration of neural networks and backstepping control effectively handles nonlinear dynamics.
- The developed method offers significant improvements over conventional control techniques for LPMSM drives.
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