Control Of Lower Limb Exoskeleton Research Articles

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\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{N}\\cdot\ ext{m}}$$\\end{document}. 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.

Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units.

The gait recognition of exoskeletons includes motion recognition and gait phase recognition under various road conditions. The recognition of gait phase is a prerequisite for predicting exoskeleton assistance time. The estimation of real-time assistance time is crucial for the safety and accurate control of lower-limb exoskeletons. To solve the problem of predicting exoskeleton assistance time, this paper proposes a gait recognition model based on inertial measurement units that combines the real-time motion state recognition of support vector machines and phase recognition of long short-term memory networks. A recognition validation experiment was conducted on 30 subjects to determine the reliability of the gait recognition model. The results showed that the accuracy of motion state and gait phase were 99.98% and 98.26%, respectively. Based on the proposed SVM-LSTM gait model, exoskeleton assistance time was predicted. A test was conducted on 10 subjects, and the results showed that using assistive therapy based on exercise status and gait stage can significantly improve gait movement and reduce metabolic costs by an average of more than 10%.

Feedforward Control of Lower Limb Exoskeletons: Which Torque Profile Should We Use?

Feedforward Control of Lower Limb Exoskeletons: Which Torque Profile Should We Use?

A COMPARATIVE ANALYSIS OF LOWER LIMB EXOSKELETON GAIT CONTROL SYSTEMS

The complexities associated with lower limb exoskeleton control systems stem from the dynamic and non-linear nature of human movement. Gait control, in particular, re-quires intricate coordination of joints and muscles, demanding precise adjustments to ac-commodate variations in walking speed, terrain, and user preferences. Additionally, the diverse physical characteristics among users, such as weight, height, and muscle develop-ment, introduce parametric uncertainties that amplify the challenge of designing control systems that can adapt seamlessly to individual differences. Addressing these complexities involves an advanced and multi-level control approach. In the pursuit of effective control strategies, a non-linear mathematical model for a single-leg lower limb exoskeleton is de-rived, considering mechanical and electromechanical components. Тhe gait control sys-tems of a lower limb exoskeleton were investigated, emphasizing the development of vari-ous control strategies, including proportional derivative (PD), PD with gain scheduling, hy-brid adaptive, and adaptive controllers with gain scheduling. The article ends with a com-prehensive comparative analysis of the developed controllers, presenting the integral abso-lute errors (IAE) during exoskeleton walking in both load and no-load conditions to evalu-ate their productivity, efficiency, and adaptability. Extensive testing was conducted using a physical test model representing the exoskeleton of the lower limbs. During a series of ex-periments, the functionality and operability of each control system were evaluated in vari-ous scenarios. The results of these tests give valuable information about the strengths and weaknesses of various control strategies, contributing to the development of lower limb exoskeleton technology.

MODEL PREDICTIVE CONTROL OF EXOSKELETON JOINT ANGLES

Lower limb exoskeletons are used for rehabilitation purposes, so their precise control is an important issue. Classical controllers are often unable to provide high accuracy and speed, which can lead to errors in exoskeleton operation. Complexities associated with lower limb exoskeleton control systems arise from the dynamic and nonlinear nature of human locomotion. Solving these complexities involves designing modern control systems. One of the modern approaches is Model Predictive Control, which, predicting future deviations of system state variables, performs optimization calculations and selects such input signals, in which case the error will tend to the minimum value. This controller is widely used for controlling nonlinear dynamic systems with constraints. Considering this fact, it was proposed to develop a controller with a predictive model for controlling the angles of the exoskeleton joints. Control with a predictive model assumes that the dynamic equations of the control object are known, so a nonlinear mathematical model of the lower limb exoskeleton was firstly designed, taking into account the mechanical and electromechanical components. An examination of the exoskeleton state variables and control input constraints, which were taken into account during the design of the controller, was performed, and a reduction function was defined, according to which the controller with a predictive model should perform an optimization calculation, thereby enabling the reduction of errors or inaccuracies. Simulations performed in the MATLAB/Simulink environment validate the effectiveness and accuracy of the developed controller, showing small deviations between the desired and actual joint angles during dynamic motions. Future efforts will focus on improving control quality and validating results through empirical studies, thereby strengthening the role of model predictive controllers for the design of exoskeleton control systems.

High-level control architecture of lower limb exoskeleton: A review

As a rehabilitation robot for aiding in the movement of lower limbs, the lower limb exoskeleton is a beneficial device. In order to make the most effective use of the exoskeleton, the control strategy plays a crucial role. This review paper provides a background and classification of lower limb exoskeleton control strategies, such as model-based and hierarchy-based control. Further, we presented mainly the high-level control architecture of lower limb exoskeletons, which is aimed at detecting the intention of human movement. An in-depth discussion is provided in this paper regarding manual user input (MUI), surface electromyography (sEMG), and brain-computer interface (BCI). Many people need exoskeletons, which is why this review was written. Exoskeletons, however, are expensive and cannot be mass-produced, and their control methods are immature, making them ineffective. Thus, the objective of this review is to identify research gaps and common limitations in previous research to obtain future directions for improving the usability of the control mechanism. In an alternative approach, MUI and BCI are combined to reduce the time spent switching movement modes and the amount of concentration required to do so.

Modeling and Analysis of Foot Function in Human Gait Using a Two-Degrees-of-Freedom Inverted Pendulum Model with an Arced Foot.

Gait models are important for the design and control of lower limb exoskeletons. The inverted pendulum model has advantages in simplicity and computational efficiency, but it also has the limitations of oversimplification and lack of realism. This paper proposes a two-degrees-of-freedom (DOF) inverted pendulum walking model by considering the knee joints for describing the characteristics of human gait. A new parameter, roll factor, is defined to express foot function in the model, and the relationships between the roll factor and gait parameters are investigated. Experiments were conducted to verify the model by testing seven healthy adults at different walking speeds. The results demonstrate that the roll factor has a strong relationship with other gait kinematics parameters, so it can be used as a simple parameter for expressing gait kinematics. In addition, the roll factor can be used to identify walking styles with high accuracy, including small broken step walking at 99.57%, inefficient walking at 98.14%, and normal walking at 99.43%.

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review.

Lower limb exoskeletons and exosuits ("exos") are traditionally designed with a strong focus on mechatronics and actuation, whereas the "human side" is often disregarded or minimally modeled. Muscle biomechanics principles and skeletal muscle response to robot-delivered loads should be incorporated in design/control of exos. In this narrative review, we summarize the advances in literature with respect to the fusion of muscle biomechanics and lower limb exoskeletons. We report methods to measure muscle biomechanics directly and indirectly and summarize the studies that have incorporated muscle measures for improved design and control of intuitive lower limb exos. Finally, we delve into articles that have studied how the human-exo interaction influences muscle biomechanics during locomotion. To support neurorehabilitation and facilitate everyday use of wearable assistive technologies, we believe that future studies should investigate and predict how exoskeleton assistance strategies would structurally remodel skeletal muscle over time. Real-time mapping of the neuromechanical origin and generation of muscle force resulting in joint torques should be combined with musculoskeletal models to address time-varying parameters such as adaptation to exos and fatigue. Development of smarter predictive controllers that steer rather than assist biological components could result in a synchronized human-machine system that optimizes the biological and electromechanical performance of the combined system.

Output Constrained Control of Lower Limb Exoskeleton Based on Knee Motion Probabilistic Model With Finite-Time Extended State Observer

Due to randomness and uncertainty of individual walking gait, this study uses the sparse Gaussian process (SGP) to construct a knee motion probabilistic model related to its corresponding hip motion. Since the SGP can quantify output uncertainty, a time-varying constrained boundary of knee position is presented to guarantee the operator's safety and comfort in human-exoskeleton cooperative motion. First, a low-cost gait acquisition system (called CJD-1) composed of inertial measurement unit is constructed to collect the operator's gait information. Then, a virtual interaction torque field and an output-dependent universal barrier function are designed to realize the coordinated transformation of the time-varying constrained boundary generated by the SGP. Hence, the exoskeleton joint position is constrained by the desired output constrained boundary, which is complied with the operator's gait law. Then, a backstepping controller with a finite-time extended state observer is proposed to estimate and compensate unmeasured joint velocity and lumped uncertainty. The effectiveness of the proposed control scheme is verified by both simulations and experimental results of human-exoskeleton cooperative motion.

End-to-End High-Level Control of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

End-to-End High-Level Control of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

Research on iterative learning control of lower limb exoskeleton of rehabilitation robot

It is difficult to establish an accurate mathematical model for the lower limb exoskeleton system of a rehabilitation robot, and there are unknown external disturbances. In this paper, an accurate model-independent closed-loop PD-type iterative learning control algorithm is adopted, which enables the system to track a given desired trajectory with uncertain dynamic models and parameters. The simulation results show that the algorithm can effectively improve the control accuracy of the trajectory tracking of the lower limb exoskeleton.

Gait phases recognition based on lower limb sEMG signals using LDA-PSO-LSTM algorithm

Gait phases are widely used in exoskeleton movement control. Surface electromyography (sEMG) is predictive and plays an important role in gait phase recognition. The purpose of this study is to improve the stability and accuracy of gait recognition methods based on the sEMG signals of lower limbs. First, we presented a LDA-PSO-LSTM algorithm based on feature combination selection and verified its recognition accuracy through experiments. LDA-PSO-LSTM had an average recognition rate of94.89% and a maximum accuracy of 97.02%. Second, we tested and compared the recognition accuracy of LDA-LSTM (92.17%). Experiments showed that the PSO optimization model had good recognition performance. Finally, we compared LDA-LSTM with all classifier combinations and concluded that the LDA-LSTM method has the highest recognition rate among a series of method combinations. The results indicated that LDA-PSO-LSTM as a classification model has apparent advantages in gait recognition. LDA-PSO-LSTM provides more accurate gait phase results for lower limb exoskeleton control. This method is beneficial to the development of the exoskeleton gait recognition system.

Gait Phase Detection in Walking and Stairs Using Machine Learning.

Machine learning-based activity and gait phase recognition algorithms are used in powered motion assistive devices to inform control of motorized components. The objective of this study was to develop a supervised multiclass classifier to simultaneously detect activity and gait phase (stance, swing) in real-world walking, stair ascent, and stair descent using inertial measurement data from the thigh and shank. The intended use of this algorithm was for control of a motion assistive device local to the knee. Using data from 80 participants, two decision trees and five long short-term memory (LSTM) models that each used different feature sets were initially tested and evaluated using a novel performance metric: proportion of perfectly classified strides (PPCS). Based on the PPCS of these initial models, five additional posthoc LSTM models were tested. Separate models were developed to classify (i) both activity and gait phase simultaneously (one model predicting six states), and (ii) activity-specific models (three individual binary classifiers predicting stance/swing phases). The superior activity-specific model had an accuracy of 98.0% and PPCS of 55.7%. The superior six-phase model used filtered inertial measurement data as its features and a median filter on its predictions and had an accuracy of 92.1% and PPCS of 22.9%. Pooling stance and swing phases from all activities and treating this model as a binary classifier, this model had an accuracy of 97.1%, which may be acceptable for real-world lower limb exoskeleton control if only stance and swing gait phases must be detected. Keywords: machine learning, deep learning, inertial measurement unit, activity recognition, gait.

Machine-learned Adaptive Switching in Voluntary Lower-limb Exoskeleton Control: Preliminary Results.

Lower-limb exoskeletons utilize fixed control strategies and are not adaptable to user's intention. To this end, the goal of this study was to investigate the potential of using temporal-difference learning and general value functions for predicting the next possible walking mode that will be selected by users wearing exoskeletons in order to reduce the effort and cognitive load while switching between different modes of walking. Experiments were performed with a user wearing the Indego exoskeleton and given the authority to switch between five walking modes that were different in terms of speed and turn direction. The user's switching preferences were learned and predicted from device-centric and room-centric measurements by considering similarities in the movements being performed. A switching list was updated to show the most probable future next modes to be selected by the user. In contrast to other approaches that either can only predict a single time-step or require intensive offline training, this work used a computationally inexpensive method for learning and has the potential of providing temporally extended sets of predictions in real-time. Comparing the number of required manual switches between the machine-learned switching list and the best possible static lists showed an average decrease of 42.44% in the required switches for the machine-learned adaptive strategy. These promising results will facilitate the path for real-time application of this technique.

Autonomous Locomotion Trajectory Shaping and Nonlinear Control for Lower Limb Exoskeletons

This article presents a strategy for autonomous locomotion trajectory planning for high-level control of lower limb exoskeletons by defining a novel set of adaptive central pattern generators (ACPGs) to facilitate safe and compliant interaction with the human. A time-varying bounded-gain adaptive disturbance observer is designed for estimating the human–robot interaction (HRI) needed for online central pattern generator (CPG)-based trajectory shaping and low-level nonlinear trajectory tracking control. The proposed ACPG dynamics for each exoskeleton joint updates the motion frequency and amplitude based on the observed HRI torque, which is also coupled with adjacent joints’ CPGs to regulate their phase differences in real time. An integrated Lyapunov analysis is conducted to ensure the closed-loop system’s stability and uniformly ultimately boundedness of both the tracking error and the torque estimation error in the controlled exoskeleton. Experimental studies are performed with an able-bodied human wearer by applying arbitrary torques on the exoskeleton’s joints in order to evaluate the proposed autonomous control strategy in online adjustment and personalization of the locomotion.

The role of user preference in the customized control of robotic exoskeletons.

User preference is a promising objective for the control of robotic exoskeletons because it may capture the multifactorial nature of exoskeleton use. However, to use it, we must first understand its characteristics in the context of exoskeleton control. Here, we systematically measured the control preferences of individuals wearing bilateral ankle exoskeletons during walking. We investigated users' repeatability identifying their preferences and how preference changes with walking speed, device exposure, and between individuals with different technical backgrounds. Twelve naive and 12 knowledgeable nondisabled participants identified their preferred assistance in repeated trials by simultaneously self-tuning the magnitude and timing of peak torque. They were blinded to the control parameters and relied solely on their perception of the assistance to guide their tuning. We found that participants' preferences ranged from 7.9 to 19.4 newton-meters and 54.1 to 59.2 percent of the gait cycle. Across trials, participants repeatably identified their preferences with a mean standard deviation of 1.7 newton-meters and 1.5 percent of the gait cycle. Within a trial, participants converged on their preference in 105 seconds. As the experiment progressed, naive users preferred higher torque magnitude. At faster walking speeds, these individuals were more precise at identifying the magnitude of their preferred assistance. Knowledgeable users preferred higher torque than naive users. These results highlight that although preference is a dynamic quantity, individuals can reliably identify their preferences. This work motivates strategies for the control of lower limb exoskeletons in which individuals customize assistance according to their unique preferences and provides meaningful insight into how users interact with exoskeletons.

Model Identification and Human-robot Coupling Control of Lower Limb Exoskeleton with Biogeography-based Learning Particle Swarm Optimization

Model Identification and Human-robot Coupling Control of Lower Limb Exoskeleton with Biogeography-based Learning Particle Swarm Optimization

Lower limb exoskeleton parasitic force modeling and minimizing with an adaptive trajectory controller

Parasitic force caused by joint misalignment is a common and challenging problem in the design and control of lower limb exoskeletons due to the complex human joint morphology. The force will generate high tangential force on the skin, which leads pain or at least discomfort for the wearer. This paper presents a model of the parasitic force in a lower limb exoskeleton, aiming to minimize this force with an adaptive trajectory controller (ATC). The controller uses parasitic force in the shank between the human and the exoskeleton as the control signal, and adjusts joint trajectories to minimize the parasitic force. In this paper, a lower limb exoskeleton with two-degrees of freedom (DOFs) in the knee joint is presented. Parasitic force relates to the joint misalignment is modeled and analyzed, upon which a trajectory controller is developed. Both simulations and experimental results are included, which showed that the proposed method was capable of effectively reducing the parasitic force in motion assistance.

Model identification and adaptive control of lower limb exoskeleton based on neighborhood field optimization

In lower limb exoskeletons, control performance and system stability of human–robot coordinated movement are often hampered by some model parametric uncertainties. To address this problem, Neighborhood Field Optimization (NFO) is proposed to identify the unknown model parameters of an exoskeleton for the model-based controller design. The excitation trajectory is designed by the NFO algorithm with motion constraints to improve the model identification accuracy. Meanwhile, the Huber fitness function is adopted to suppress the influence of the disturbance points in sampled dataset. Then an adaptive backstepping control scheme is constructed to improve the dynamic tracking performance of human–robot training mode in the presence of the identification error. Via Lyapunov technique and backstepping iteration, all the system state errors of the exoskeleton are bound and converge to zero neighborhood based on the assumption of bounded identified parameter error. Finally, the model identification results and comparative tracking performance of the proposed scheme are verified by an experimental platform of Two-degrees of freedom (DOF) lower limb exoskeleton with human–robot cooperative motion.

Motion Modeling and Control of Lower Limb Exoskeleton Based on Max-Plus Algebra

Max-plus algebra is a special method to describe the discrete event system. In this paper, it is introduced to describe the motion of lower limb exoskeleton. Based on the max-plus algebra and the timed event graph, the walking process of exoskeleton is modelled. The max-plus algebra approach can describe the logical sequence and safety condition in the walking process, which cannot be achieved via other conventional modelling approaches. The autonomous control of lower limb exoskeleton system is studied via the model based on max-plus algebra. In the end, an FSM (finite state machine) controller embedded with the max-plus algebra model is proposed, and the experiments show ideal speed and gait/phase period control effect, as well as the good safety and stable performance.