Analysis of a passive ankle exoskeleton for reduction of metabolic costs during walking

Modern conflicts demand substantial physical and psychological exertion,often resulting in fatigue and diminished combat or operational readiness.Several exoskeletons have been developed recently to address these challenges,presenting various limitations that affect their operational or everyday usability.This article evaluates the performance of a dual-purpose passive ankle exoskeleton developed for the reduction of metabolic costs during walking,seeking to identify a force element that could be applied to the target population.Based on the 6-min walk test,twenty-nine subjects participated in the study using three different force elements.The results indicate that it is possible to reduce metabolic expenditure while using the developed exoskeleton.Additionally,the comfort and range of motion results verify the exoskeleton's suitability for use in uneven terrain and during extended periods.Nevertheless,the choice of the force element should be tailored to each user,and the control system should be adjustable to optimise the exoskeleton's performance.

Model Parameters Identification and Backstepping Control of Lower Limb Exoskeleton Based on Enhanced Whale Algorithm

Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.

Neural Network Robust Control Based on Computed Torque for Lower Limb Exoskeleton

The lower limb exoskeletons are used to assist wearers in various scenarios such as medical and industrial settings.Complex modeling errors of the exoskeleton in different application scenarios pose challenges to the robustness and stability of its control algorithm.The Radial Basis Function(RBF)neural network is used widely to compensate for modeling errors.In order to solve the problem that the current RBF neural network controllers cannot guarantee the asymptotic stability,a neural network robust control algorithm based on computed torque method is proposed in this paper,focusing on trajectory tracking.It innovatively incorporates the robust adaptive term while introducing the RBF neural network term,improving the compensation ability for modeling errors.The stability of the algorithm is proved by Lyapunov method,and the effectiveness of the robust adaptive term is verified by the simulation.Experiments wearing the exoskeleton under different walking speeds and scenarios were carried out,and the results show that the absolute value of tracking errors of the hip and knee joints of the exoskeleton are consistently less than 1.5°and 2.5°,respectively.The proposed control algorithm effectively compensates for modeling errors and exhibits high robustness.

A Fractional-Order Ultra-Local Model-Based Adaptive Neural Network Sliding Mode Control of n-DOF Upper-Limb Exoskeleton With Input Deadzone

This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Considering the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design.Firstly,the control gain of ultra-local model is considered as a constant.The fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance.Correspondingly,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is proposed.Secondly,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown parameter.Then,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain.Correspondingly,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is proposed.Moreover,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory.Finally,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.

Adaptive Robust Control with Leakage-Type Control Law for Trajectory Tracking of Exoskeleton Robots

This paper investigates the trajectory following problem of exoskeleton robots with numerous constraints. However, as a typical nonlinear system with variability and parameter uncertainty, it is difficult to accurately achieve the trajectory tracking control for exoskeletons. In this paper, we present a robust control of trajectory tracking control based on servo constraints. Firstly, we consider the uncertainties (e.g., modelling errors, initial condition deviations, structural vibrations, and other unknown external disturbances) in the exoskeleton system, which are time-varying and bounded. Secondly, we establish the dynamic model and formulate a close-loop connection between the dynamic model and the real world. Then, the trajectory tracking issue is regarded as a servo constraint problem, and an adaptive robust control with leakage-type adaptive law is proposed with the guaranteed Lyapunov stability. Finally, we conduct numerical simulations to verify the performance of the proposed controller.

Design and Analysis of a Novel Shoulder Exoskeleton Based on a Parallel Mechanism

Power-assisted upper-limb exoskeletons are primarily used to improve the handling efficiency and load capacity.However,kinematic mismatch between the kinematics and biological joints is a major problem in most existing exoskeletons,because it reduces the boosting effect and causes pain and long-term joint damage in humans.In this study,a shoulder augmentation exoskeleton was designed based on a parallel mechanism that solves the shoulder dislocation problem using the upper arm as a passive limb.Consequently,the human–machine synergy and wearability of the exoskeleton system were improved without increasing the volume and weight of the system.A parallel mechanism was used as the structural body of the shoulder joint exoskeleton,and its workspace,dexterity,and stiffness were analyzed.Additionally,an ergonomic model was developed using the principle of virtual work,and a case analysis was performed considering the lifting of heavy objects.The results show that the upper arm reduces the driving force requirement in coordinated motion,enhances the load capacity of the system,and achieves excellent assistance.

An Adaptive Hand Exoskeleton for Teleoperation System

Teleoperation can assist people to complete various complex tasks in inaccessible or high-risk environments,in which a wearable hand exoskeleton is one of the key devices.Adequate adaptability would be available to enable the master hand exoskeleton to capture the motion of human fingers and reproduce the contact force between the slave hand and its object.This paper presents a novel finger exoskeleton based on the cascading four-link closed-loop kinematic chain.Each finger has an independent closed-loop kinematic chain,and the angle sensors are used to obtain the finger motion including the flexion/extension and the adduction/abduction.The cable tension is changed by the servo motor to transmit the contact force to the fingers in real time.Based on the finger exoskeleton,an adaptive hand exoskeleton is consequently developed.In addition,the hand exoskeleton is tested in a master-slave system.The experiment results show that the adaptive hand exoskeleton can be worn without any mechanical constraints,and the slave hand can follow the motions of each human finger.The accuracy and the real-time capability of the force reproduction are validated.The proposed adaptive hand exoskeleton can be employed as the master hand to remotely control the humanoid five-fingered dexterous slave hand,thus,enabling the teleoperation system to complete complex dexterous manipulation tasks.

An Investigation of Exoskeleton Robotic Systems in Assisting Construction Tasks*

Whilst industrial robots have been widely used in many industrial sectors, they are predominantly used in a structured factory environment. In recent years, off-site robotics have been investigated extensively and there are some promising candidates emerging. One such category of robots is exoskeleton robots and this paper provides an in-depth assessment of their suitability in assisting human operators in undertaking manual operations typically found in the construction industry. This work aims to objectively assess the advantages and disadvantages of these two suits and provide recommendations for further improvements of similar system designs. The paper focuses on the passive exoskeleton robotic suits which are commercially available. Three types of activities are designed and a mechatronic methodology has been designed and implemented to capture visual data in order to assess these systems in comparison with normal human operations. The study suggests that these passive suits do reduce the effort required by human operators to undertake the same construction tasks as evidenced by the results from one focused study, though a number of improvements could be made to improve their performance for wider adoption.

Exploring Passive Exoskeleton-Induced Changes in Lumbar Muscle Activity

Purpose: The purpose of this study was to evaluate the effect of using a passive exoskeleton on lumbar muscle activity during lifting movements, and to determine whether muscle activity remains altered after exoskeleton removal. This study sought to identify the potential risks and benefits associated with the use of passive exoskeletons for the prevention and treatment of low back pain. Methods: Eighteen healthy adult participants lifted a 10 kg suitcase while wearing a passive exoskeleton. Muscle activity and postures were measured during lifting and before, during, and after exoskeleton use. This study examined whether the reduced muscle activity observed during exoskeleton use persisted after exoskeleton removal. Muscle activity was assessed using electromyography and postures were recorded using reflective markers and a camera. Results: The study found that Lumbar muscle activity decreased significantly (approximately 40%) during exoskeleton use compared to that without exoskeleton use. Importantly, lumbar muscle activity remained low after exoskeleton removal, at levels similar to those observed during exoskeleton use. This suggests that individuals adapted to the exoskeleton support and maintained altered muscle control, even without the exoskeleton. Conclusion: This study demonstrates that passive exoskeletons significantly reduce lumbar muscle activity during lifting tasks, and that this altered muscle control persists after exoskeleton removal. These findings contribute to the understanding of the risks and benefits of passive exoskeletons, potentially aiding their development and informing their use in the prevention and treatment of low back pain.

基于足底压力分布的下肢步态识别方法

良好的下肢步态感知性能有助于提升助力型外骨骼机器人的助力效果。本文以足底压力分布为研究对象,基于足底生物力学分析搭建一种可穿戴式足底压力分布采集装置,分别采集平地行走、平地慢跑和坡路行走3个步态的足底压力数据,并基于多元线性回归法构建的地面反作用力预测模型获取整体足压,提出了一种基于整体足压和卷积神经网络(CNN)分类算法进行下肢步态识别的方法,并与支持向量机(SVM)和反向传播(BP)神经网络进行了对比分析。试验结果表明:该方法对于3种步态的平均识别率达到98.3%,具有较高的准确性,验证了使用CNN分类算法对下肢不同步态识别的可行性与有效性。

有源刚性下肢助力外骨骼研究现状与关键技术分析

有源刚性下肢助力外骨骼系统是一种穿戴在人身体上的伴随式智能装备,单兵穿戴者通过它能提高人体负载与机动能力。从有源刚性下肢助力外骨骼的国内外研究现状出发,概述了当前助力增强型有源刚性下肢外骨骼的发展现状,并围绕国内外已有研究的进展,对影响下肢助力外骨骼发展的传感感知、机械构型与驱动、控制技术等核心关键技术进行了分析与总结,特别考虑了外骨骼在应对士兵这类特殊群体时的技术难点,为有源刚性下肢助力外骨骼在单兵装备的发展上提供一定的参考价值。

下肢外骨骼康复机器人仿真和试验分析

目的为了帮助下肢运动障碍者进行有序康复训练,设计一种下肢外骨骼康复机器人,动力源驱动下肢交叉摆动模拟人类正常步态行走,实现双下肢协调运动,帮助下肢运动障碍者完成康复训练。方法建立外骨骼机器人三维模型、下肢外骨骼机器人D-H模型,对下肢外骨骼康复机器人进行正、逆运动学分析,并将模型导入ADAMS,创建运动副与驱动函数进行运动学仿真,在此基础上制作样机并进行动力学测试试验分析。结果正、逆运动学验证了外骨骼康复机器人空间运动的合理性,ADAMS运动仿真结果与理论计算具有良好一致性,从而保证设计的下肢外骨骼结构与穿戴者下肢同步协调。仿真分析发现理论计算和仿真的误差主要来源于驱动函数误差,动作误差最大为2.15 cm。试验验证髋关节、膝关节运动与参考输入运动具有一致性。但是运动存在误差,髋关节平均误差为5.57°,膝关节平均误差为5.45°,实验发现电机扭矩不足是引起运动误差的首要因素,其次是零件加工误差和装配误差。结论通过理论计算、仿真与试验分析验证了方案的可行性,发现驱动误差、零件加工精度和装配精度可带来误差。研究结果可为进一步完善机器人性能和研究康复机器人动力学影响因素提供基础和参数依据。

下肢外骨骼机器人步行训练对痉挛型双瘫脑性瘫痪患儿平衡功能的影响

目的:探讨下肢外骨骼机器人步行训练对痉挛型双瘫脑性瘫痪患儿平衡功能的影响。方法:纳入2022年7月至2022年12月间就诊于浙江大学医学院附属儿童医院康复科的痉挛型双瘫脑性瘫痪患儿20例为治疗组,采用配对设计,年龄、性别、功能状态匹配的20例作为对照组。两组均给予常规的运动康复训练(运动疗法、悬吊训练、等速肌力训练),治疗组在此基础上增加下肢外骨骼机器人步行训练。在治疗前、治疗8周后对两组患儿进行表面肌电(sEMG)测试、动态平衡反应位移测试、静态平衡评分测试和儿童平衡量表(PBS)测试。结果:治疗前两组患儿臀大肌、臀中肌、股四头肌和胫前肌RMS数值,动态平衡反应位移、静态平衡评分及PBS评分组间差异无显著性意义(P>0.05),两组患儿治疗后臀大肌、臀中肌、股四头肌和胫前肌RMS数值,动态平衡反应位移、静态平衡评分及PBS评分均增加,治疗前后组内比较具有显著性意义(P<0.05),治疗后较治疗前有所改善,组间比较两组患儿治疗前后臀大肌、臀中肌和股四头肌RMS数值差值(臀大肌P=0.021;臀中肌P=0.016;股四头肌P=0.004)、动态平衡反应位移差值(前侧P=0.014;左侧P=0.003;右侧P=0.003)、静态平衡评分差值(P=0.005)及PBS评分差值(P=0.004)具有显著性意义,治疗组差值大于对照组,治疗组改善效果优于对照组。结论:下肢外骨骼机器人结合常规康复治疗能更加有效地改善痉挛型双瘫脑性瘫痪患儿平衡功能。

人体穿戴髋关节助力外骨骼的行走运动学分析

针对现有下肢助力外骨骼研究中缺少对关节层面的运动学分析以及缺少对髋关节外骨骼助力机制的研究这一现状,对一款髋关节助力外骨骼在多种助力模式下的运动进行采集和分析,得到被试在不穿戴外骨骼、外骨骼零助力、外骨骼低助力、外骨骼中助力、外骨骼高助力和外骨骼阻力共6种模式下的运动数据,并通过逆运动学计算和数据分析,得到关节角度曲线和步态特征。实验结果在关节层面明确了髋关节外骨骼的助力机制,可为助力外骨骼的设计和运动控制提供参考。

下肢战伤康复轮足外骨骼设计与步态试验

针对脑部和脊柱战伤士兵的术后康复训练、战时转移、生活代步需求,设计了一种下肢战伤康复轮足外骨骼,通过共用结构和驱动系统进行不同的组装来实现不同工作模式,提高装备的集成度和减轻整体质量;建立轮足外骨骼单腿摆动期的二连杆模型并推导其动力学模型;利用动力学模型和轮足外骨骼的虚拟样机模型分别获得髋、膝关节的理论计算和虚拟仿真力矩;建立基于位置阻抗的轮足外骨骼主动康复训练控制策略,搭建轮足外骨骼实物样机,获得样机的关节运动轨迹与人机交互力。理论计算、虚拟仿真及摆动腿试验测试3种方法的关节力矩曲线走势基本一致,说明理论计算和虚拟仿真模型的正确性;基于步态试验所获得的数据表明其轨迹误差和交互力均较小,满足设计要求,验证了轮足外骨骼设计的合理性与可行性。

基于改进涡流搜索算法的外骨骼迭代学习控制

为提升康复外骨骼机器人的步态跟踪性能,提出一种基于改进涡流搜索算法的迭代学习控制方法。首先针对传统迭代学习控制抗扰性差和控制信息缺失问题,引入PD控制器、自适应遗忘因子、误差过渡曲线和控制信息搜索等策略,改进迭代学习控制律;其次,基于多种策略对涡流搜索算法进行改进,提出了一种改进涡流搜索算法,改进后的算法可优化迭代学习控制的PD参数;最后进行行走实验,将提出的迭代学习控制方法与现有的同类算法进行仿真和数值比较,并测试了扰动情况下的跟踪性能。实验结果表明,所提方法的误差更小,跟踪性能更强。该算法改进了迭代学习控制的不足,具有较强的抗扰性能,保证了使用时的稳定性。

肌电和足压信息融合的外骨骼步态识别

为解决基于单一信号识别步态相位不够精准的问题,开展了动态交互力激励下的人机协同行走的步态识别研究。设计了肌电和足压信息采集的多模态传感器检测硬件平台;分别对单一信号开展滤波降噪、特征提取与降维等预处理;将表征下肢生理信息的肌电信号与运动信息的足压信号相融合,构建了支持向量机-模糊C均值(support vector machine-fuzzy C-mean algorithm,SVM-FCM)多模信息融合的外骨骼助行步态识别算法;开展了人机协同助行实验,实验结果表明:信息融合后的人机步态相位平均识别率达到82.49%,优于使用单一信号的识别效果,验证了多模信息融合算法识别人机协同步态的有效性。本研究可用于下肢外骨骼机器人运动控制,为人机运动相融奠定基础。

基于人机工程学的下肢外骨骼运动控制

为避免外骨骼机器人髋关节和膝关节尺寸影响患者正常行走功能的恢复,踝尺寸对脚落地姿态的影响,提出一种基于人机工程学的外骨骼机器人运动控制方法.首先利用人机工程学对人体的参数进行收集,并以此构建适合人体尺寸、具有舒适穿戴的外骨骼模型架构,使外骨骼机器人满足穿戴人群差异化的需求;然后所构建的下肢外骨骼架构,建立外骨骼下肢末端的位姿并构建单肢三自由度机械腿的正向运动学方程,通过对机器人的步行计划,外骨骼机器人的实际步行过程由ADAMS进行模拟,验证人体行走时两腿各关节扭矩的变化规律;最后为了验证该方法在关节速度控制中的优越性和有效性,选择模糊PID进行控制.

基于模糊PID控制的柔性下肢助力外骨骼

为给老年人在行走时提供辅助力,设计了一种对人体下肢髋关节和膝关节提供助力的柔性外骨骼,针对直流电动机进行了动力学建模,利用模糊自适应PID控制算法对柔性外骨骼进行控制。通过Simulink仿真分析得出,模糊自适应PID控制算法能够有效地跟踪电动机的期望轨迹曲线。与传统PID控制算法对比,模糊自适应PID算法能有效地抑制外界环境因素对系统的干扰,适应性较强。

基于反步法的下肢外骨骼机器人控制研究

针对下肢外骨骼预定轨迹的控制问题,在构建了二自由度机械腿的运动学和动力学模型的基础上,提出了一种非对称液压缸负载力、伸缩量与外骨骼转动扭矩、运动角度的转化关系,采用反步法控制下肢外骨骼系统的运动轨迹。首先,采用D-H法建立了二自由度下肢外骨骼系统的运动学模型,研究了外骨骼末端执行器关节速度的变化关系,并利用拉格朗日动力学方程推导了外骨骼动力学的数学模型;然后,使用了液压缸负载力控制为机械腿运动提供了相应的扭矩,进而控制了下肢外骨骼的运动姿态;其次,针对下肢外骨骼运动时的高精度要求,利用反步法控制理论,建立了阀控非对称缸系统,结合下肢外骨骼动力学系统整体的状态空间方程,利用下肢外骨骼液压伺服系统控制了下肢外骨骼进行预定轨迹运动;最后,对AMESim软件与Visual Studio软件进行了联合仿真,对比分析了PID控制与反步法控制的精度问题。研究结果表明:针对下肢外骨骼预定轨迹控制,反步法控制误差最大值为2°,对比传统PID控制,误差降低了67%;设计的反步法控制器对于下肢外骨骼具有良好的控制精度。