Adaptive motion control of arm rehabilitation robot based on impedance identification

Abstract

There is increasing interest in using rehabilitation robots to assist post-stroke patients during rehabilitation therapy. The motion control of the robot plays an important role in the process of functional recovery training. Due to the change of the arm impedance of the post-stroke patient in the passive recovery training, the conventional motion control based on a proportional-integral (PI) controller is difficult to produce smooth movement of the robot to track the designed trajectory set by the rehabilitation therapist. In this paper, we model the dynamics of post-stroke patient arm as an impedance model, and propose an adaptive control scheme, which consists of an adaptive PI control algorithm and an adaptive damping control algorithm, to control the rehabilitation robot moving along predefined trajectories stably and smoothly. An equivalent two-port circuit of the rehabilitation robot and human arm is built, and the passivity theory of circuits is used to analyze the stability and smoothness performance of the robot. A slide Least Mean Square with adaptive window (SLMS-AW) method is presented for on-line estimation of the parameters of the arm impedance model, which is used for adjusting the gains of the PI-damping controller. In this paper, the Barrett WAM Arm manipulator is used as the main hardware platform for the functional recovery training of the post-stroke patient. Passive recovery training has been implemented on the WAM Arm, and the experimental results demonstrate the effectiveness and potential of the proposed adaptive control strategies.

Dispositivo Robótico Multifuncional para la Rehabilitación de las Extremidades Superiores

“…Con este propósito, observando loséxitos obtenidos en otros dispositivos robóticos de rehabilitación (Otten et al, 2015;Song et al, 2014) gracias al uso del algoritmo de impedancia (Hogan, 1985), en este trabajo se ha diseñado un controlador basado en este fundamento (Figura 5).…”

A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data

“…Obviously, there is need to partition the movement data collected from patients in a way that is intuitive and commensurate with how the task both performed (by the patient) and observed (by the clinician); a substantial portion of the robot's utility is in providing accurate quantitative support to guiding therapy in real-time and supporting offline analysis. Moreover, considering that in adaptive robotics, early-session data are used to train the robot's force-impedance settings for optimized re-training (Patton and Mussa-Ivaldi, 2004;Xu et al, 2011;Song et al, 2015), the ongoing inquiry into movement decomposition (modeling the speed profile as a series of log-normal curves) (Rohrer et al, 2002;Dipietro et al, 2009;Balasubramanian et al, 2012Balasubramanian et al, , 2015, or the robotic manipulandums prevalent in industrial applications (Gao and Zhang, 2015;Chen et al, 2016;He et al, 2017). These arenas are just a few examples of where boundary conditions are critically important parameters.…”

Fuzzy learning variable admittance control for human-robot cooperation

“…Fuzzy logic is a very effective tool to represent expert knowledge with linguistic rules and create a human-like inference mechanism. Examples of fuzzy logic and impedance control can be seen in rehabilitation robotics [13] where a fuzzy inference system was implemented to adjust the controller parameters according to the patient's arm impedance.…”