基于Prandtl-Ishlinskii模型的气动肌肉迟滞特性动态建模与控制方法

现有迟滞模型由于采用离线参数辨识方法，难以表征气动肌肉迟滞的时变性和负载相关性，极易产生较大的建模误差。为了精确表征气动肌肉的迟滞特性，利用Prandtl-Ishlinskii（PI）模型描述气动肌肉的位移-气压迟滞特性，并采用带遗忘因子的递推最小二乘法在线辨识PI模型参数。在此基础上，结合PI逆模型设计了一种带有前馈在线补偿的复合控制方法用于气动肌肉的运动控制。同时搭建相应的实验装置进行了气动肌肉迟滞建模和运动控制实验。实验结果表明，采用在线参数辨识方法后的PI模型能有效描述气动肌肉迟滞的负载相关性，且极大地降低了负载变化带来的控制误差。

Dynamic Hysteresis Modeling and Control of Pneumatic Muscle Based on Prandtl-Ishlinskii Model

Due to the use of offline parameter identification methods, the existing hysteresis models are difficult to characterize the time-varying and load dependent properties from the hysteresis of pneumatic muscle (PM), which was easy to generate significant modeling errors. In order to accurately characterize the hysteresis characteristics of PM, the Prandtl-Ishlinskii (PI) model was used to describe the lengt h-pressure hysteresis characteristics of PM, and the forgetting factor recursive least squares (FFRLS) was used to identify parameters of the PI model online. Compared with offline identification, the online identification method can effectively improve the modeling accuracy of PI models. Then the feedforward online compensation controller was designed based on the PI inverse model, and a composite controller was established by combining with feedback control. This composite control approach was used to realize the motion control of PM. At the same time, corresponding experimental equipment was built and hysteresis modeling and motion control experiments of PM were conducted to compare and analyze the trajectory tracking effects of offline identification and online identification under different loads. The experimental results showed that the PI model using online parameter identification method can effectively describe the load-dependence of hysteresis and greatly reduce control errors caused by load variation.