The properties of pneumatic artificial muscle (PAM) with excellent power-to-weight ratio and natural compliance made it useful for healthcare engineering applications. However, it has undesirable hysteresis effect in controlling a robotic manipulator. This behavior is quasistatic and quasirate dependent which changed with excitation frequency and external force. Apart from this, it also inherits frictional presliding behavior with nonlocal memory effect. These nonlinearities need to be compensated to achieve optimal performance of the control system. Even though an inverse modeling of PAM has limited application, it is important on certain control system implementation that requires the solution to the inverse problem. In this paper, the inverse modeling of PAM in the form of activation pressure was proposed. This activation pressure model was derived according to static pressure and extracted hysteresis components from pressure/length hysteresis. The derivation of the static pressure model follows the phenomenological-based model of third-order polynomial. It is capable of characterizing the nonlinear region of PAM at low and high pressure. The derivation of extracted hysteresis model follows the mechanism of dynamic friction. In this principle, the activation pressure model was dependent on regression coefficient of the static pressure model and dynamic friction coefficients of the extracted hysteresis model. The regression constants of these coefficients were characterized from the hysteresis dataset by using model parameter identification and the particle swarm optimization (PSO) method. The result from model simulation shows the root mean square error (RMSE) value of less than 10% error was evaluated at various excitation frequencies and external forces. This inverse modeling of PAM implemented a simple approach, but it should be useful in control design applications such as rehabilitation robotics, biomedical system, and humanoid robots.

中文翻译：

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基于多项式参数化和粒子群算法的非线性人工肌肉逆模型

气动人造肌肉（PAM）的特性具有出色的功率重量比和自然顺应性，使其可用于医疗保健工程应用。然而，它在控制机器人操纵器中具有不期望的磁滞效应。这种行为是准静态和准相关的，随激励频率和外力而变化。除此之外，它还继承了具有非局部记忆效应的摩擦预滑动行为。这些非线性需要补偿，以实现控制系统的最佳性能。即使PAM的逆模型应用受到限制，对于某些需要解决逆问题的控制系统实现，这也很重要。本文以活化压力的形式提出了PAM的逆模型。该激活压力模型是根据静态压力导出的，并从压力/长度滞后中提取了滞后分量。静压模型的推导遵循基于现象学的三阶多项式模型。它能够表征低压和高压下PAM的非线性区域。提取的磁滞模型的推导遵循动态摩擦的机理。按照这个原理，激活压力模型取决于静压力模型的回归系数和提取的磁滞模型的动摩擦系数。通过使用模型参数识别和粒子群优化（PSO）方法，从磁滞数据集中表征这些系数的回归常数。模型仿真的结果表明，在各种激励频率和外力作用下，均方根误差（RMSE）值均小于10％。PAM的这种逆模型实现了一种简单的方法，但在控制设计应用程序（例如康复机器人，生物医学系统和类人机器人）中应该很有用。