基于无导数卡尔曼滤波的假肢控制

S. M. Moosavi, S. Fakoorian, V. Azimi, H. Richter, D. Simon
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引用次数: 6

摘要

提出了一种基于状态估计的机器人/假肢系统无导数控制方法。该系统是一个模拟人类髋关节和大腿运动的测试机器人和一个动力跨股假肢的组合。机器人/假体组合建模为三自由度(DOF)机器人:垂直髋关节位移、大腿角度和膝盖角度。针对n-DOF机器人系统,提出了一种基于状态估计的无导数卡尔曼滤波器(DKF)。然后,我们提出了一种在机器人动力学中包含干扰时使DKF具有鲁棒性的方法。在鲁棒DKF中,我们使用两种不同的方法来抑制干扰:PD和PI。这些干扰补偿器用于监视控制,使DKF在存在干扰时具有鲁棒性。仿真结果表明了DKF和鲁棒DKF在三自由度机器人/假体系统状态估计控制中的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Derivative-free Kalman filtering-based control of prosthetic legs
A derivative-free method for state estimation-based control of a robot/prosthesis system is presented. The system is the combination of a test robot that emulates human hip and thigh motion, and a powered transfemoral prosthetic leg. The robot/prosthesis combination is modeled as a three degree-of-freedom (DOF) robot: vertical hip displacement, thigh angle, and knee angle. We develop a derivative-free Kalman filter (DKF) for state estimation-based control for an n-DOF robotic system. We then propose a method to make the DKF robust when the robot dynamics include disturbances. In the robust DKF, we use two different methods for disturbance rejection: PD and PI. These disturbance compensators are used for supervisory control to make the DKF robust in the presence of disturbances. The simulation results show the advantages of the DKF and the robust DKF for the three-DOF robot/prosthesis system for state estimation-based control.
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