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Intelligent motion control for linear piezoelectric ceramic motor drive
1Department of Electrical Engineering, Yuan Ze University, Chung Li 320, Taiwan, ROC. rjwai@saturn.yzu.edu.tw
Summary
This study introduces a novel LLCC resonant driving circuit and sliding-mode fuzzy-neural-network control (SMFNNC) for linear piezoelectric ceramic motors (LPCMs). The system ensures accurate motion control despite motor nonlinearities, validated by experiments.
Area of Science:
- Robotics and Control Systems
- Materials Science and Engineering
- Electrical Engineering
Background:
- Linear piezoelectric ceramic motors (LPCMs) exhibit complex nonlinear and time-varying dynamics.
- Accurate position control of LPCMs is challenging due to these dynamic characteristics.
- Existing control strategies often require precise motor models, which are difficult to obtain.
Purpose of the Study:
- To develop an advanced driving circuit and control system for precise motion control of LPCMs.
- To address the challenges posed by the nonlinear and time-varying nature of LPCM dynamics.
- To achieve robust and stable position control without relying on exact dynamic models.
Main Methods:
- Design and implementation of a double-inductance double-capacitance (LLCC) resonant driving circuit.
- Development of a sliding-mode fuzzy-neural-network control (SMFNNC) system.
- Utilizing Lyapunov stability analysis for adaptive learning algorithms within the SMFNNC system.
Main Results:
- The LLCC resonant inverter operates at an optimal switching frequency, ensuring consistent output voltage regardless of quality factor variations.
- The SMFNNC system demonstrates favorable tracking performance, effectively managing LPCM motion.
- Lyapunov stability analysis guarantees system-tracking stability in the closed-loop configuration.
- Experimental results validate the effectiveness of the proposed driving circuit and control system.
Conclusions:
- The integrated LLCC resonant driving circuit and SMFNNC system provide a robust solution for accurate LPCM motion control.
- The proposed control strategy overcomes the limitations of traditional methods by not requiring precise dynamic models.
- The system's stability and performance are theoretically guaranteed and experimentally verified.
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