# Research on motion control of a novel stroke rehabilitation robot based on the dual parallel washout algorithm

## TL;DR

This paper introduces a new stroke rehabilitation robot using a dual parallel washout algorithm to improve motion control and training effectiveness.

## Contribution

A novel motion control strategy using dual parallel washout algorithm and MOGA optimization for stroke rehabilitation robots.

## Key findings

- The MOGA optimization reduces motion perception error in the rehabilitation robot.
- Motion decoupling and dual parallel filtering improve the accuracy of motion reproduction.
- The system aims to provide a more realistic motion experience for users.

## Abstract

A motion control strategy based on multi-source heterogeneous motion information fusion and motion decoupling parallel washout algorithm (WA) is proposed for the control of a rehabilitation robot designed for stroke-related balance disorders. The robot features a serial-parallel hybrid structure and humanoid gait functionality, with its output being the pre-defined trajectory motion of the guiding pedals. The WA algorithm is widely applied in motion simulation and control. In this study, the filter parameters of the WA are optimized using Multi-Objective Genetic Algorithm (MOGA), aiming to minimize the motion perception error introduced by the robot, thereby optimizing the robot’s motion trajectory to better align with the human perception threshold and the dynamic response characteristics of the device. A custom-built multi-source heterogeneous sensing system is employed to capture human gait features, enabling the WA to generate specific motion trajectories pre-defined for the rehabilitation robot. To ensure that the optimization search space for each WA channel remains independent and to more accurately reproduce motion details, motion decoupling and dual parallel filtering control strategies are introduced. Through the optimization of the WA filter parameters, the system aims to minimize the theoretical motion perception error experienced by the user during robot-assisted motion training, with the potential to provide a more realistic motion experience and enhanced training outcomes. In the future, long-term follow-up and monitoring of the effectiveness will also be conducted.