METAHEURISTIC METHODS OPTIMIZED HSMC CONTROL OF A MULTI DOF HAND AND WRIST REHABILITATION ROBOT DESIGN

Abstract

Rehabilitation is a process that aims to restore individuals to full functionality following an injury or illness that has compromised their ability to perform daily activities. A significant challenge in addressing these needs is the selection of robotic devices that can adequately respond to the complex requirements of rehabilitation. This study presents a simulation study for the position control of the wrist and fingers using Hierarchical Sliding Mode Control (HSMC) that is optimized via metaheuristic algorithms. A novel hierarchical approach is applied that is optimized with Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms. The model was simulated under disturbances to track the desired trajectory. Simulation results are compared with Hierarchical PID controller results. The findings demonstrate that HSMC-based control effectively improve trajectory tracking, reducing mean absolute and normalized root mean square (NRMS) errors compared to HPID controllers. The proposed approach shows promising potential for real implementation, enhancing the efficiency of rehabilitation devices.

Keywords

• Rehabilitation Robot
• Hierarchical Sliding Mode Control (HSMC)
• Hand Rehabilitation
• Particle Swarm Optimization (PSO)
• Differential Evolution (DE)

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