气动肌肉的最小二乘支持向量机迟滞模型

针对传统迟滞模型存在的待辨识参数多、参数辨识过程复杂和辨识精度低等问题,采用最小二乘支持向量机对气动肌肉的位移/气压迟滞开展建模研究。通过非线性映射将原始数据空间映射到高维空间,将原系统的非线性问题变成高维空间中的线性问题,借助于最小二乘法求解该线性方程组,从而提高其求解速度及收敛精度。在气动肌肉迟滞特性实验的基础上,采用所建数学模型,与经典的PI模型进行对比。结果表明,采用最小二乘支持向量机建立的数学模型具有更高的建模精度,均方差和平均误差相比PI模型分别减小了99.21%和99.1%,该方法可为后续气动肌肉的迟滞补偿控制提供有效的手段。

The Hysteresis Modeling of Pneumatic Muscle Based on Least Squares Support Vector Machine Approach

The traditional hysteresis modeling methods have series problems such as many parameters to be identified, complex parameter identification process and low identification accuracy, thus the least squares support vector machine (LS-SVM) approach is proposed to characterize the hysteresis phenomenon of pneumatic muscle(PM). The method maps the original data space to the high-dimensional space by non-linear mapping, thus the non-linear problem of the original system is transformed into a linear problem in the high-dimensional space, the least square method is used to solve the system of linear equations, which improves the speed of solution and convergence accuracy. Based on the experiments, the mathematical model of displacement/pressure hysteresis on PM was established by using LS-SVM method, the calculation results showed that the mathematical model established by LS-SVM has higher modeling accuracy and various error indices such as mean variance and mean error are significantly reduced, which reduce 99.21% and 99.1% respectively compared with the classical PI model. The method providing an effective means for subsequent hysteresis compensation control of PM.