# Research on Control Strategy of Lower Limb Exoskeleton Robots: A Review

## TL;DR

This paper reviews control strategies for lower limb exoskeleton robots, focusing on how they can better assist human movement and rehabilitation.

## Contribution

The paper introduces a universal control framework by separating end-effector control from hardware implementation.

## Key findings

- Control strategies are analyzed based on four tasks: trajectory reproduction, motion following, Assist-As-Needed, and motion intention prediction.
- Key challenges include drive system coupling, multi-source perception, and algorithm adaptability.
- The paper emphasizes the importance of perception–decision–execution architecture in exoskeleton control.

## Abstract

With an aging population and the high incidence of neurological diseases, rehabilitative lower limb exoskeleton robots, as a wearable assistance device, present important application prospects in gait training and human function recovery. As the core of human–computer interaction, control strategy directly determines the exoskeleton’s ability to perceive and respond to human movement intentions. This paper focuses on the control strategies of rehabilitative lower limb exoskeleton robots. Based on the typical hierarchical control architecture of “perception–decision–execution,” it systematically reviews recent research progress centered around four typical control tasks: trajectory reproduction, motion following, Assist-As-Needed (AAN), and motion intention prediction. It emphasizes analyzing the core mechanisms, applicable scenarios, and technical characteristics of different control strategies. Furthermore, from the perspectives of drive system and control coupling, multi-source perception, and the universality and individual adaptability of control algorithms, it summarizes the key challenges and common technical constraints currently faced by control strategies. This article innovatively separates the end-effector control strategy from the hardware implementation to provide support for a universal control framework for exoskeletons.