基于改进模型的气动肌肉神经网络串级控制

Neural Network Cascade Control of Pneumatic Artificial Muscles Based on Modified Model

气动肌肉的理论模型是其控制器设计的基础。为了得到较完整的气动肌肉的理论模型,作者在Chou理想模型的基础上,综合考虑影响气动肌肉特性的主要因素,推导了气动肌肉收缩力-位移-气压三者之间的解析表达式,建立了相对简单而又较为完整的气动肌肉的改进静态数学模型。在此基础上,利用神经网络控制器的自适应与鲁棒性等优点,设计了一种针对气动肌肉驱动关节的神经网络PID串级控制器,该串级控制器采用内环气压控制器与外环位置控制器分别对气压与位置进行闭环控制,实验结果验证了该控制器的有效性。

The theoretical model is the foundation for the control of pneumatic artificial muscles. The static characteristics of pneumatic artificial muscles are affected by many factors. By considering the main factors,the relationship between the force,pressure and contraction of pneumatic artificial muscles are derived based on the Chou ideal model,and then a modified perfect model is built.On the basis,a neural network cascade Proportion-Integration-Differential（ PID） controller for a joint driven by pneumatic artificial muscles was designed,by taking the advantages of neural network control scheme such as self-adaptability and robustness. The cascade closed-loop controller was adopted of an inner pressure controller and an outer position controller to control the pressure and position respectively. Performance effect of the cascade controller is verified by experiments.