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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

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FPGA-based Elman neural network control system for linear ultrasonic motor.

Faa-Jeng Lin¹, Ying-Chih Hung

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

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

|February 14, 2009

Summary

This study introduces a field-programmable gate array (FPGA)-based Elman neural network (ENN) for precise control of linear ultrasonic motors (LUSMs). The system demonstrates effective position control for nonlinear and time-varying motor dynamics.

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Area of Science:

Robotics and Control Systems
Artificial Intelligence
Motor Control

Background:

Linear ultrasonic motors (LUSMs) exhibit complex nonlinear and time-varying dynamics, posing challenges for precise position control.
Traditional control methods struggle to adapt to the inherent complexities of LUSM operation.
Accurate control is crucial for LUSM applications requiring high-precision movement.

Purpose of the Study:

To develop and implement an Elman neural network (ENN) control system for precise mover position control of LUSMs.
To adapt the ENN for hardware implementation on a field-programmable gate array (FPGA).
To validate the proposed control scheme through experimental verification.

Main Methods:

Designed an Elman neural network (ENN) with a detailed description of its structure and online learning algorithm (delta adaptation law).
Replaced the standard sigmoid function in the ENN's hidden layer with a piecewise continuous function for efficient hardware implementation.
Implemented the ENN control algorithm on a field-programmable gate array (FPGA) chip.

Main Results:

The proposed FPGA-based ENN control system effectively managed the nonlinear and time-varying characteristics of the LUSM.
Experimental results confirmed the system's capability for precision position control.
The hardware implementation facilitated potential low-cost and high-performance industrial applications.

Conclusions:

The FPGA-based ENN control system offers a viable solution for precise LUSM position control.
The use of a piecewise continuous function and FPGA implementation enhances practical applicability.
The study validates the effectiveness of the proposed intelligent control approach for LUSMs.