Fan Jizhuang, Zhong Jun, Zhao Jie, Zhu Yanhe
Technol Health Care. 2015;23 Suppl 2:S231-8. doi: 10.3233/THC-150958.
Although Pneumatic Artificial Muscle (PAM) has a promising future in rehabilitation robots, it's difficult to realize accurate position control due to its highly nonlinear properties.
This paper deals with position control of PAM.
To describe the hysteresis inside PAM, a polynomial based phenomenological function is developed. Based on the phenomenological model for PAM and analysis of pressure dynamics within PAM, an adaptive cascade controller is proposed. Both outer loop and inner loop employ BP Neural Network tuned PID algorithm. The outer loop is to handle high nonlinearities and unmodeled dynamics of PAM, while the inner loop is responsible for nonlinearities caused by pressure dynamics.
Experimental results show high tracking accuracy as compared with a convention PID controller.
The proposed controller is effective in improving performance of PAM and will be implemented in a rehabilitation robot.
虽然气动人工肌肉（PAM）在康复机器人领域有着广阔的前景，但由于其高度非线性特性，难以实现精确的位置控制。
本文研究PAM的位置控制。
为描述PAM内部的滞后现象，开发了一种基于多项式的唯象函数。基于PAM的唯象模型和对PAM内部压力动态的分析，提出了一种自适应串级控制器。外环和内环均采用BP神经网络整定PID算法。外环用于处理PAM的高度非线性和未建模动态，而内环负责由压力动态引起的非线性。
实验结果表明，与传统PID控制器相比，跟踪精度更高。
所提出的控制器在提高PAM性能方面是有效的，并将在康复机器人中实现。
