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A New Approach to Modeling and Controlling a Pneumatic Muscle Actuator-Driven Setup Using Back Propagation Neural Networks

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  • Jun Zhong
  • Xu Zhou
  • Minzhou Luo

Abstract

Pneumatic muscle actuators (PMAs) own excellent compliance and a high power-to-weight ratio and have been widely used in bionic robots and rehabilitated robots. However, the high nonlinear characteristics of PMAs due to inherent construction and pneumatic driving principle bring great challenges in applications acquired accurately modeling and controlling. To tackle the tricky problem, a single PMA mass setup is constructed, and a back propagation neural network (BPNN) is employed to identify the dynamics of the setup. An offline model is built up using sampled data, and online modifications are performed to further improve the quality of the model. An adaptive controller based on BPNN is designed using gradient descent information of the built-up model. Experiments of identifying the PMA setup using BPNN and position tracking by adaptive BPNN controller are performed, and results demonstrate the good capacity in accurate controlling of the PMA setup.

Suggested Citation

  • Jun Zhong & Xu Zhou & Minzhou Luo, 2018. "A New Approach to Modeling and Controlling a Pneumatic Muscle Actuator-Driven Setup Using Back Propagation Neural Networks," Complexity, Hindawi, vol. 2018, pages 1-9, October.
  • Handle: RePEc:hin:complx:4160504
    DOI: 10.1155/2018/4160504
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    Cited by:

    1. Le Chen & Ying Feng & Rui Li & Xinkai Chen & Hui Jiang, 2019. "Jiles-Atherton Based Hysteresis Identification of Shape Memory Alloy-Actuating Compliant Mechanism via Modified Particle Swarm Optimization Algorithm," Complexity, Hindawi, vol. 2019, pages 1-11, February.

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