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Design of a Kalman filter for rotary shape memory alloy actuators

机译:用于旋转形状记忆合金执行器的卡尔曼滤波器的设计

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Measuring the state variables of systems actuated by shape memory alloys (SMAs) is normally a difficult task because of the small diameter of the SMA wires. In such cases, as an alternative, observers are used to estimate the state vector. This paper presents an extended Kalman filter (EKF) for estimation of the state variables of a single-degree-of-freedom rotary manipulator actuated by an SMA wire. This model-based state estimator has been chosen because it works well with noisy measurements and model inaccuracies. The SMA phenomenological models, that are mostly used in engineering applications, have both model and parameter uncertainties; this makes the EKF a natural choice for SMA-actuated systems. A state space model for the SMA manipulator is presented. The model includes nonlinear dynamics of the manipulator, a thermomechanical model of the SMA, and the electrical and heat transfer behavior of the SMA wire. In an experimental set-up the angular position of the arm is the only state variable that is measured besides the voltage applied to the SMA wire. The other state variables of the system are the arm's angular velocity and the SMA wire's stress and temperature, which are not available experimentally due to difficulty in measuring them. Accurate estimation of the state variables enables design of a control system that provides better system performance. At each time step, the estimator uses the SMA wire's voltage measurement to predict the state vector which is corrected as necessary according to the measured angular position of the arm. The input and output of the model are used for the EKF simulations. The state variables collected through model simulations are also used to evaluate the performance of the EKF. Several EKF simulations presented in this paper show accurate and robust performance of the estimator, for different control inputs.
机译:由于形状记忆合金(SMA)线的直径较小,因此测量由形状记忆合金(SMA)致动的系统的状态变量通常是一项艰巨的任务。在这种情况下,可以选择使用观察者来估计状态向量。本文提出了一种扩展的卡尔曼滤波器(EKF),用于估计由SMA线驱动的单自由度旋转机械手的状态变量。选择这种基于模型的状态估计器是因为它可以很好地与嘈杂的测量结果和模型不正确性配合使用。 SMA现象学模型(主要用于工程应用)具有模型和参数的不确定性。这使EKF成为SMA驱动系统的自然选择。提出了SMA机械手的状态空间模型。该模型包括机械手的非线性动力学,SMA的热力学模型以及SMA线的电和热传递行为。在实验设置中,除了施加到SMA导线的电压外,臂的角位置是唯一被测量的状态变量。系统的其他状态变量是手臂的角速度以及SMA线的应力和温度,由于难以测量,因此无法通过实验获得。对状态变量的准确估计使设计能够提供更好系统性能的控制系统成为可能。在每个时间步长,估算器都使用SMA导线的电压测量值来预测状态向量,该状态向量将根据测得的手臂角度位置进行必要的校正。该模型的输入和输出用于EKF仿真。通过模型仿真收集的状态变量也用于评估EKF的性能。本文介绍的几种EKF仿真结果表明,针对不同的控制输入,估算器的性能准确且稳定。

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