Optimal Position Control of Nonlinear Muscle Based on Sliding Mode and Particle Swarm Optimization Algorithm

Document Type : Original Article

Authors

1 Department of Biomedical Engineering, Electronic Branch, Islamic Azad University, Tehran, Iran

2 Department of Mechanical Engineering, University of Guilan, Rasht, Iran

3 Department of Biomedical Engineering, Sciences and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

Rehabilitation is a way to care muscle and skeleton disorders and disease, using mechanical equipment. The aim of rehabilitation is that help disable persons who they reach to minimum physical activities. Since pneumatic actuators have high respect of power to weight, are cleanliness, reachable fluid, are to repair and low cost that can use to implement in robot which interact with human body parts. The other features that use on pneumatic actuators that this is comprisable fluid and variable stiffness traits. Due to importance and performance of these actuators in medical science and also they have high security and exact control which can preserve bodies. Therefore in this project, we search pneumatic actuators with high precision controller that can manufacture various mechanisms for rehabilitation process. Pneumatic actuators mathematical modeling is presented in this thesis. According to modeling of pneumatic actuator, differential equations of actuators are presented. In this research, we used solenoid valves, because proportional valves are expensive. We used sliding mode theory for position control of actuator. There are many importance on these systems and the main aim of this project, optimal path of 2 links mechanism for rehabilitation problems. So optimal tracking robot, we use particle swarm optimization for finding optimal gain controller in sliding mode control because we want to achieve minimum tracking errors in path planning of mechanism.

Keywords

References

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Transactions on Machine Intelligence
Volume 5, Issue 1
October 2022
Pages 37-45

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History

  • Receive Date: 29 May 2022
  • Revise Date: 15 July 2022
  • Accept Date: 22 September 2022

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