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Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

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Intelligent complementary sliding-mode control for LUSMS-based X-Y-theta motion control stage.

Faa-Jeng Lin¹, Syuan-Yi Chen, Kuo-Kai Shyu

¹Department of Electrical Engineering, National Central University, Chungli, Taiwan. linfj@ee.ncu.edu.tw

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

|July 20, 2010

Summary

An intelligent complementary sliding-mode control (ICSMC) system enhances contour tracking for linear ultrasonic motors. This novel approach significantly improves tracking accuracy and speed by utilizing a recurrent wavelet-based Elman neural network estimator.

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Area of Science:

Robotics and Control Systems
Artificial Intelligence
Neural Networks

Background:

Linear ultrasonic motors (LUSMs) are crucial for precise motion control stages.
Contour tracking requires sophisticated control systems to manage complex movements.
Existing sliding-mode control (SMC) methods have limitations in tracking accuracy and error bounds.

Purpose of the Study:

To propose an intelligent complementary sliding-mode control (ICSMC) system for LUSM-based X-Y-theta motion control stages.
To improve contour tracking performance by reducing tracking errors.
To develop an on-line uncertainty estimation method using a novel neural network.

Main Methods:

Development of an ICSMC system incorporating a recurrent wavelet-based Elman neural network (RWENN) estimator.
Utilizing a complementary generalized error transformation to reduce tracking error bounds.
Employing wavelet functions as activation functions in the RWENN for enhanced convergence.
Deriving RWENN estimation laws via Lyapunov stability theorem for on-line training.
Introducing a robust compensator to handle system uncertainties.

Main Results:

The proposed ICSMC system demonstrated significantly improved tracking performance compared to conventional SMC and CSMC.
The RWENN estimator effectively estimated lumped uncertainties on-line.
The use of wavelet functions in RWENN improved convergent precision and time.
The complementary generalized error transformation halved the guaranteed ultimate bound of the tracking error.

Conclusions:

The ICSMC system offers superior contour tracking capabilities for LUSM-based motion stages.
The RWENN estimator provides an effective solution for on-line uncertainty estimation in control systems.
The integration of wavelet neural networks enhances the precision and speed of control systems.