Optimal adaptive control of a knee joint exoskeleton for lower limb functional rehabilitation

TL;DR

This paper presents an optimal adaptive control scheme for knee joint exoskeletons using Particle Swarm Optimization to enhance rehabilitation effectiveness by minimizing tracking errors and respecting system constraints.

Contribution

It introduces a novel PSO-based adaptive control method tailored for knee exoskeletons, addressing patient variability and improving tracking performance.

Findings

High tracking accuracy demonstrated in simulations

Effective parameter tuning respecting nonlinear constraints

Potential for improved personalized rehabilitation

Abstract

Lower limb exoskeleton robots hold great potential for rehabilitation, movement assistance, and strength augmentation. Design control to guarantee optimal needed assistance is still a challenge considering the pathological variances between patients. In this paper, we proposed an optimal adaptive control scheme based on Particle Swarm Optimization (PSO) Algorithm. The proposed controller is based on a well-known dynamic model of the knee joint exoskeleton, and the optimization algorithm is used to minimize a square error fitness function, which quantifies tracking performances. Control parameters are tuned respecting some nonlinear constraints for step response of the system and boundaries constraints. Numerical simulation results are presented to show the validity and the high performances of the proposed approach.