Abstract

The ultrasonic motor (USM) has a heavy nonlinearity and time-varying characteristics which vary with driving conditions. Because of no-exact mathematical model of USM, it is difficult to control USM. PID controller can be designed without using the expression model of plant, but it is hard to compensate the nonlinearity and characteristic changes of USM. This paper presents a self-tuning scheme using a modified particle swarm optimization (MPSO) for PID controller to overcome the dynamic characteristics of USM. A modified PSO employs the strategy that nonlinearity decreases the value of inertia weight from a large value to a small value. This strategy is to improve the performance of the standard PSO in global search and fine-tuning of the solution. The effectiveness of the proposed method is verified by numerical simulation and experimental investigation. The results demonstrate that the proposed method can improve the accuracy of USM.

Highlights

  • The ultrasonic motor (USM) is a new type motor, which is driven by the ultrasonic vibration force of piezoelectric elements

  • Since the modern controls are hard to be applied on USM, PID controller has been widely used in USM applications[4,5,6,7]

  • The results are compared with the particle swarm optimization (PSO) with linear decreasing inertia weight based PID controller and the fixed-gain PID controller by experimenting on positioning control of USM

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Summary

Introduction

The ultrasonic motor (USM) is a new type motor, which is driven by the ultrasonic vibration force of piezoelectric elements. There are limitations of the control performance using the conventional fixed-gain type PID controller because USM causes serious characteristic changes during operation and contains non-linearity. In that case, it is difficult for the conventional one to compensate such characteristic changes and non-linearity of USM. It is difficult for the conventional one to compensate such characteristic changes and non-linearity of USM To overcome those problems, the research on self tuning of PID controller using an intelligent soft computing, such as Neural Network (NN) and Genetic Algorithm (GA), are proceeding. A modified PSO which employs nonlinear decreasing of inertia weight was applied to optimize the PID parameters for a positioning control of USM.

Particle Swarm Optimization
The Proposed Modified PSO
Numerical Simulation
Application of PSO-NDW in PID Controller
Experiment Result
A Conventional Hand-Tuned PID Controller
Raise Ki to eliminate steady-state
Methods
Self-Tuning PID Controller using PSO-NDW
Comparison of the Average Steady-state Error
Findings
Conclusions

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