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 Review on Lower Limb Rehabilitation Exoskeleton Robots

Lower limb rehabilitation exoskeleton robots integrate sensing, control, and other technologies and exhibit the characteristics of bionics, robotics, information and control science, medicine, and other interdisciplinary areas. In this review, the typical products and prototypes of lower limb exoskeleton rehabilitation robots are introduced and stateof-the-art techniques are analyzed and summarized. Because the goal of rehabilitation training is to recover patients’ sporting ability to the normal level, studying the human gait is the foundation of lower limb exoskeleton rehabilitation robot research. Therefore, this review critically evaluates research progress in human gait analysis and systematically summarizes developments in the mechanical design and control of lower limb rehabilitation exoskeleton robots. From the performance of typical prototypes, it can be deduced that these robots can be connected to human limbs as wearable forms;further, it is possible to control robot movement at each joint to simulate normal gait and drive the patient’s limb to realize robot-assisted rehabilitation training. Therefore human–robot integration is one of the most important research directions, and in this context, rigid-flexible-soft hybrid structure design, customized personalized gait generation, and multimodal information fusion are three key technologies.

Structure Design of Lower Limb Exoskeletons for Gait Training

Due to the close physical interaction between human and machine in process of gait training, lower limb exoskeletons should be safe, comfortable and able to smoothly transfer desired driving force/moments to the patients. Correlatively, in kinematics the exoskeletons are required to be compatible with human lower limbs and thereby to avoid the uncontrollable interactional loads at the human-machine interfaces. Such requirement makes the structure design of exoskeletons very difficult because the human-machine closed chains are complicated. In addition, both the axis misalignments and the kinematic character difference between the exoskeleton and human joints should be taken into account. By analyzing the DOF（degree of freedom） of the whole human-machine closed chain, the human-machine kinematic incompatibility of lower limb exoskeletons is studied. An effective method for the structure design of lower limb exoskeletons, which are kinematically compatible with human lower limb, is proposed. Applying this method, the structure synthesis of the lower limb exoskeletons containing only one-DOF revolute and prismatic joints is investigated; the feasible basic structures of exoskeletons are developed and classified into three different categories. With the consideration of quasi-anthropopathic feature, structural simplicity and wearable comfort of lower limb exoskeletons, a joint replacement and structure comparison based approach to select the ideal structures of lower limb exoskeletons is proposed, by which three optimal exoskeleton structures are obtained. This paper indicates that the human-machine closed chain formed by the exoskeleton and human lower limb should be an even-constrained kinematic system in order to avoid the uncontrollable human-machine interactional loads. The presented method for the structure design of lower limb exoskeletons is universal and simple, and hence can be applied to other kinds of wearable exoskeletons.

Control and Implementation of 2-DOF Lower Limb Exoskeleton Experiment Platform

In this study,a humanoid prototype of 2-DOF(degrees of freedom)lower limb exoskeleton is introduced to evaluate the wearable comfortable effect between person and exoskeleton.To improve the detection accuracy of the humanrobot interaction torque,a BPNN(backpropagation neural networks)is proposed to estimate this interaction force and to compensate for the measurement error of the 3D-force/torque sensor.Meanwhile,the backstepping controller is designed to realize the exoskeleton's passive position control,which means that the person passively adapts to the exoskeleton.On the other hand,a variable admittance controller is used to implement the exoskeleton's active followup control,which means that the person's motion is motivated by his/her intention and the exoskeleton control tries best to improve the human-robot wearable comfortable performance.To improve the wearable comfortable effect,serval regular gait tasks with different admittance parameters and step frequencies are statistically performed to obtain the optimal admittance control parameters.Finally,the BPNN compensation algorithm and two controllers are verified by the experimental exoskeleton prototype with human-robot cooperative motion.

Robust Sliding Mode Control for a 2-DOF Lower Limb Exoskeleton Base on Linear Extended State Observer

For the 2-Degree of Freedom(DOF)lower limb exoskeleton,to ensure the system robustness and dynamic performance,a linearextended-state-observer-based(LESO)robust sliding mode control is proposed to not only reduce the influence of parametric uncertainties,unmodeled dynamics,and external disturbance but also estimate the unmeasurable real-time joint angular velocity directly.Then,via Lyapunov technology,the stability of the corresponding LESO and controller is proven.The appropriate and reasonable simulation was carried out to verify the effectiveness of the proposed LESO and exoskeleton controller.

Unknown system dynamics estimator-based impedance control for lower limb exoskeleton with enhanced performance

In this article, an unknown system dynamics estimator-based impedance control method is proposed for the lower limb exoskeleton to stimulate the tracking flexibility with the terminal target position when suffering parametric inaccuracies and unexpected disturbances. To reinforce the robust performance, via constructing the filtering operation-based dynamic relation, i.e., invariant manifold, the unknown system dynamics estimators are employed to maintain the accurate perturbation identification in both the hip and knee subsystem. Besides, a funnel control technique is designed to govern the convergence process within a minor overshoot and a higher steady-state precision. Meanwhile, an interactive complaint result can be obtained with the aid of the impedance control, where the prescribed terminal trajectory can be adjusted into the interaction variable-based target position by the force–position mapping, revealing the dynamic influence between the impedance coefficient (stiffness and damping) and the adjusted position magnitude. A sufficient stability analysis verifies the ultimately uniformly bounded results of all the error signals, and even the angle errors can be regulated within the predefined funnel boundary in the whole convergence. Finally, some simulations are provided to demonstrate the validity and superiority including the enhanced interaction flexibility and robustness.

A Comparison of Four Neural Networks Algorithms on Locomotion Intention Recognition of Lower Limb Exoskeleton Based on Multi-source Information

Lower Limb Exoskeletons(LLEs)are receiving increasing attention for supporting activities of daily living.In such active systems,an intelligent controller may be indispensable.In this paper,we proposed a locomotion intention recognition system based on time series data sets derived from human motion signals.Composed of input data and Deep Learning(DL)algorithms,this framework enables the detection and prediction of users’movement patterns.This makes it possible to predict the detection of locomotion modes,allowing the LLEs to provide smooth and seamless assistance.The pre-processed eight subjects were used as input to classify four scenes:Standing/Walking on Level Ground(S/WOLG),Up the Stairs(US),Down the Stairs(DS),and Walking on Grass(WOG).The result showed that the ResNet performed optimally compared to four algorithms(CNN,CNN-LSTM,ResNet,and ResNet-Att)with an approximate evaluation indicator of 100%.It is expected that the proposed locomotion intention system will significantly improve the safety and the effectiveness of LLE due to its high accuracy and predictive performance.

Review of Key Technologies for Developing Personalized Lower Limb Rehabilitative Exoskeleton Robots

Rehabilitative training and assistance to daily living activities play critical roles in improving the life quality of lower limb dyskinesia patients and older people with motor function degeneration.Lower limb reha-bilitative exoskeleton has a promising application prospect in support of the above population.In this paper,critical technologies for developing lower limb rehabilitative exoskeleton for individualized user needs are identi-fied and reviewed,including exoskeleton hardware modularization,bionic compliant driving,individualized gait planning and individual-oriented motion intention recognition.Inspired by the idea of servitization,potentials in exoskeleton product-service system design and its enabling technologies are then discussed.It is suggested that future research will focus on exoskeleton technology and exoskeleton-based service development oriented to an individual's physical features and personalized requirements to realize better human-exoskeleton coordination in terms of technology,as well as accessible and high-quality rehabilitation and living assistance in terms of utility.

Review of Power-Assisted Lower Limb Exoskeleton Robot

Power-assisted lower limb exoskeleton robot is a wearable intelligent robot system involving mechanics,materials,electronics,control,robotics,and many other fields.The system can use external energy to provide additional power to humans,enhance the function of the human body,and help the wearer to bear weight that is previously unbearable.At the same time,employing reasonable structure design and passive energy storage can also assist in specific actions.First,this paper introduces the research status of power-assisted lower limb exoskeleton robots at home and abroad,and analyzes several typical prototypes in detail.Then,the key technologies such as structure design,driving mode,sensing technology,control method,energy management,and human-machine coupling are summarized,and some common design methods of the exoskeleton robot are summarized and compared.Finally,the existing problems and possible solutions in the research of power-assisted lower limb exoskeleton robots are summarized,and the prospect of future development trend has been analyzed.

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。

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。

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.

下肢外骨骼柔性踝关节设计及助力研究

针对市面上的外骨骼产品普遍存在柔顺性欠佳、能耗高等问题,提出了一种由气弹簧被动助力的柔性踝关节方案。为研究气弹簧对关节力矩以及系统能耗的改善效果,根据踝关节在步态中的不同阶段,建立了气弹簧的力学模型,并通过ADAMS进行动力学仿真验证,选取了弹簧的最优参数。仿真结果表明,气弹簧在最优参数下能有效减小各关节所受的冲击力矩,降低主动关节驱动电动机的能耗,在一定程度上改善了外骨骼的动态性能。

下肢康复外骨骼机器人设计与性能分析

为了更好地辅助偏瘫患者的康复训练,设计了一种基于盘式电机驱动的下肢康复外骨骼机器人,并通过助力效果可视化研究与性能分析来验证其不同康复训练模式的有效性。首先,对下肢康复外骨骼机器人的结构进行了详细设计,并利用OpenSim软件进行了人机耦合的生物力学分析。然后,开展了基于位置跟踪控制的被动康复训练实验以及抗阻康复训练实验并采集表面肌电信号,验证了所设计下肢康复外骨骼机器人在不同模式下辅助患者康复训练的有效性。结果表明,穿戴下肢康复外骨骼机器人能使人体膝关节的力矩减小50%左右;在被动康复训练实验中,跟随误差为-4°~8°,且人体下肢目标肌群的肌肉激活度呈明显的周期性变化;在抗阻康复训练实验中,人体下肢目标肌群的肌肉激活度随负重的增加而提高。所设计的下肢康复外骨骼机器人具有良好的灵敏性和跟随性,其被动及抗阻康复训练模式均有助于偏瘫患者下肢的康复,具有广阔的应用前景。

下肢助行外骨骼结构设计与仿真分析

外骨骼机器人作为当下国内外的研究热点之一,其研究过程融合了机械设计、仿真分析、传感、控制等各项技术。针对当今社会对助行外骨骼的需求,文中从人体下肢结构与运动特性研究角度出发,采用模块化的方法仿人体髋关节设计了一款下肢助行外骨骼机器人。文中使用Solid Works三维软件设计了下肢助行外骨骼的三维模型,对所建立的模型进行了数学建模并通过MATLAB和ADAMS软件对外骨骼进行了仿真分析与验证。结果表明:外骨骼结构设计安全合理,能够满足实际需求。

穿戴式外骨骼康复机器人结合Bobath技术对脑卒中偏瘫患者的影响

目的探讨穿戴式外骨骼康复机器人结合Bobath技术对脑卒中偏瘫患者步态、平衡能力、下肢运动Fugl-Meyer功能量表(FMA)评分的影响。方法纳入2020年11月—2022年11月我院收治的78例脑卒中偏瘫患者,按照数字列表法随机分为观察组与对照组,每组39例,对照组予以Bobath技术训练,观察组在对照组基础上予以穿戴式外骨骼康复机器人训练,干预8周。比较两组干预前后的步态参数(步频、步速、跨步长比率)、平衡能力[Berg平衡量表(BBS)]、下肢运动功能FMA评分、日常生活活动能力[改良Barthel指数(MBI)]、下肢肌力恢复情况(屈髋肌力、伸膝肌力)。结果干预前,两组各指标差异无统计学意义(P>0.05);干预后,两组步频、步速、跨步长比率、BBS评分、下肢FMA评分、MBI评分、屈髋肌力、伸膝肌力较干预前显著升高(均P<0.05),观察组步频、步速、跨步长比率、BBS评分、下肢FMA评分、MBI评分、屈髋肌力、伸膝肌力高于对照组(均P<0.05)。结论穿戴式外骨骼康复机器人结合Bobath技术可改善脑卒中偏瘫患者步态和平衡能力,促进下肢运动功能恢复,提高日常生活活动能力,增强患者下肢肌力。

适应人体重心起伏的悬吊减重康复系统设计

减重系统对下肢疾病患者的行走康复训练有重要影响。现有的下肢康复外骨骼减重装置大多只考虑如何减去患者体重的百分比,而忽略了患者身体重心的起伏。由于外骨骼的骨盆支架在竖直方向运动轨迹固定,患者步态的微小变化就会导致重心高度与骨盆支架运动轨迹不符,这一差异会作用到患者的骨盆位置,影响下肢关节的活动,并产生额外的风险。针对这一问题,提出通过采集足底压力来预测重心位置变化,并利用获得的重心轨迹解算出应施加的减重力方法,为患者训练提供安全有效的减重系统。所提方法的可行性已获仿真证实,并在研发的外骨骼减重系统上获得实际验证,模糊控制器跟踪重心轨迹的误差相比PID控制减少了21.2%,稳态误差维持在±1 mm范围内,髋膝关节的活动范围相比常规减重分别增加了14.36%和13.77%。

下肢外骨骼机器人动力学及自抗扰控制研究

为增强下肢障碍人群在被动训练阶段,使用下肢康复外骨骼时的轨迹跟踪控制精度及系统的抗干扰性,设计了自抗扰控制(Active disturbance rejection control,ADRC)策略,可将未建模部分和外部不确定性干扰视作总扰动,并设计了补偿环节进行补偿以抑制干扰的影响。根据下肢外骨骼机器人的结构推导出了动力学方程,并根据方程设计了自抗扰控制器的各个环节。最后通过MATLAB/Simulink仿真来观察所设计控制器的效果,并与传统PID方法进行对比,验证了所设计的ADRC控制方案具有更高的控制精度和更好的抗干扰性能。

基于OpenSim的柔性下肢助力外骨骼设计及仿真分析

针对目前刚性外骨骼存在穿戴舒适性差、外骨骼关节难以与人体关节匹配、对人体自由度限制较多等问题,设计了一种柔性下肢助力外骨骼,该外骨骼采用柔性弹力带储能装置,可实现行走时对人体下肢肌肉进行助力。使用OpenSim软件建立了柔性外骨骼人机交互的模拟,以实现其最佳助力方案为目标,建立了多关节助力、单关节助力人机耦合模型;研究了不同关节助力组合对人体助力的影响以及不同弹簧刚度外骨骼人机耦合模型对膝关节、髋关节和踝关节的影响。结果表明,单关节助力方案优于多关节助力方案,随着弹簧刚度的增加,当达到20 N/mm时,单踝关节助力方案助力效果最好。模拟仿真结果表明,穿戴踝关节外骨骼后与不穿戴的情况相比,在行走过程中,人体总体能量消耗、小腿主要运动肌肉受力均有所减小,减小了穿戴者的肌肉负担,达到了助力目的。