Earticle

초록

영어

Magnetic shape memory alloy actuator is a new type of actuator that can offer big travel and high resolution of output displacement, which makes it suitable for driving task. However, its output displacement represents the hysteresis applied to input magnetic field. Hysteresis restricts its application in the high precision positioning. In order to eliminate the hysteresis of magnetic shape memory alloy actuator, a reinforcement learning fuzzy neural network controller is proposed. Network structure and special learning algorithm of reinforcement learning fuzzy neural network controller are introduced in detail. The proposed control system adopts the generalized approximate reasoning-based intelligent control architecture, which is mainly consisted of three parts: Action Selection Network, Action Evaluation Network and Stochastic Action Modifier. Finally, in order to verify the effectiveness of the proposed control method, the simulation experiment is researched. The experimental results show that the proposed control method can obtain the smaller tracking error, and the controller’s maximum tracking error is less than 0.95%, hysteresis loop is less than 2.66%.

목차

Abstract
 1. Introduction
 2. Design of Reinforcement Learning Fuzzy Neural Network Controller
  2.1 Structure of AEN
  2.2 Algorithm of Internal Reinforcement Signal
  2.3 Structure of ASN Fuzzy Neural Network
  2.4 Stochastic Action Modifier (SAM)
 3. Learning Algorithm of AEN and ASN
  3.1 Learning of AEN
  3.2 Learning of ASN
 4. Simulation Research
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Miaolei Zhou [ College of Communication Engineering, Jilin University, Changchun 130022, China ]
• Bing Hu [ College of Communication Engineering, Jilin University, Changchun 130022, China ]
• Wei Gao [ College of Communication Engineering, Jilin University, Changchun 130022, China ]
• Jingyuan Wang [ College of Communication Engineering, Jilin University, Changchun 130022, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605