Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
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Intelligent Switching Control of a Pneumatic Artificial Muscle Robot using Learning Vector Quantization Neural Network

학습벡터양자화 뉴럴네트워크를 이용한 공압 인공 근육 로봇의 지능 스위칭 제어

  • Yoon, Hong-Soo (Department of Mechanical and Automotive Engineering, Univ. of Ulsan) ;
  • Ahn, Kyoung-Kwan (School of Mechanical and Automotive Engineering, Univ of Ulsan)
  • 윤홍수 (울산대학교 기계자동차공학과) ;
  • 안경관 (울산대학교 기계자동차공학부)
  • Published : 2009.04.01
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Abstract

Pneumatic cylinder is one of the low cost actuation sources which have been applied in industrial and prosthetic application since it has a high power/weight ratio, a high-tension force and a long durability However, the control problems of pneumatic systems, oscillatory motion and compliance, have prevented their widespread use in advanced robotics. To overcome these shortcomings, a number of newer pneumatic actuators have been developed such as McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle (PAM) Manipulators. In this paper, one solution for position control of a robot arm, which is driven by two pneumatic artificial muscles, is presented. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external load of the robot arm. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is proposed in this paper. This estimates the external load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external working loads.

Keywords

References

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