Abstract

The Ultrasonic Motor (USM) is a new type motor that get driven by frictional force. At present, since USM has excellent features, it is used in various fields such as in autofocus of cameras, actuators of devices in MRI environment, and micro robots. However, because the dynamic characteristics of USM vary according to different temperature, humidity and load conditions, mathematical modeling of USM is difficult. Thus, PID control has been used for control of USM conventionally. However, conventional PID control with fixed gains cannot the non-linear characteristic variation of USM. In this research, we propose a Hybrid Improved PSO (HI-PSO) for USM control. This proposed method applies Particle Swarm Optimization (PSO) method to tune the optimized PID gains automatically. The optimized gains in USM control can be obtained in real time by using the proposed method. The control of USM with high accuracy can be achieved by applying the proposed method compensating the characteristics.

Highlights

  • The Ultrasonic Motor (USM) is a new type of motor which is driven by frictional force

  • Since USM is driven by frictional force, dynamic characteristics vary according to different conditions

  • PID control with fixed gains cannot compensate the non-linear characteristics of USM, which is caused by a temperature or humidity change or a load change in USM driving

Read more

Summary

Introduction

The Ultrasonic Motor (USM) is a new type of motor which is driven by frictional force. The frictional force between the rotor and the stator, which is caused by the ultrasonic wave generated from the vibration, will drive the rotor to move in certain direction. Since USM is driven by frictional force, dynamic characteristics vary according to different conditions. For this reason, there are difficulties in mathematical modeling based on physical analysis(5). In this research, we use Particle Swarm Optimization (PSO) as a method to tune the PID gains for obtaining high control performance in USM control(12). Since PSO is effective in non-linear optimization, we apply it in the proposed method corresponding to the nonlinear change of characteristics. The effectiveness of the proposed method is verified based on experiments

Particle Swarm Optimization
Novel Hybrid PSO
Experimental Conditions
Experimental Results
Conclusions

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.