Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled pe...Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent.However,controlling an exoskeleton for rehabilitation presents challenges due to their nonlinear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton.To remedy these problems,this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons,addressing the challenges of nonlinear dynamics and external disturbances.The proposed controller integrated a Radial Basis Function Neural Network(RBFNN)with a disturbance observer and employed a high-dimensional integral Lyapunov function to guarantee system stability and trajectory tracking performance.In the control system,the role of the RBFNN was to estimate uncertain signals in the dynamic model,while the disturbance observer tackled external disturbances during trajectory tracking.Artificially created scenarios for Human-Robot interactive experiments and periodically repeated reference trajectory experiments validated the controller’s performance,demonstrating efficient tracking.The proposed controller is found to achieve superior tracking accuracy with Root-Mean-Squared(RMS)errors of 0.022-0.026 rad for all joints,outperforming conventional Proportional-Integral-Derivative(PID)by 73%and Neural-Fuzzy Adaptive Control(NFAC)by 389.47%lower error.These results suggested that the RBFNN adaptive controller,coupled with disturbance compensation,could serve as an effective rehabilitation tool for upper-limb exoskeletons.These results demonstrate the superiority of the proposed method in enhancing rehabilitation accuracy and robustness,offering a promising solution for the control of upper-limb assistive devices.Based on the obtained results and due to their high robustness,the proposed control schemes can be extended to other motor disabilities,including lower limb exoskeletons.展开更多
Dear Editor,This letter investigates the system development of a multi-joint rehabilitation exoskeleton,and highlights the subject-adaptive control factors for efficient motor learning.In order to enable the natural m...Dear Editor,This letter investigates the system development of a multi-joint rehabilitation exoskeleton,and highlights the subject-adaptive control factors for efficient motor learning.In order to enable the natural mobility of the human upper extremity,we design the shoulder mechanism by arranging three rotational joints with acute angles,and adopt a serial chain structure for the fully constructed system.After the kinematics and dynamics of CASIA-EXO are modelled,the patient-in-the-loop control strategy is proposed for rehabilitation training,consisting of the intention-based trajectory planning and performance-based intervention adaptation.Finally,we conduct experiments to validate the efficacy of the control system,and further demonstrate the potential of CASIA-EXO in neurorehabilitation.Introduction:Neurological diseases are the leading cause of nontraumatic disability worldwide,and stroke is one of the most common encountered neurological injury,which is suffered by over 15 million individuals each year,and about 70%−80%of these individuals have varying degrees of functional impairments[1].In order to facilitate the motor relearning in central nervous system,post-stroke patients need to undergo long-term rehabilitation training to promote neural plasticity,thereby enhancing the recovery of motor function in activities of daily living(ADLs).Evidence in the clinical studies suggests that robot-assisted rehabilitation integrating neuroscience,biomechanics,and automation control can improve the patients’motivation for active participation while improving the treatment efficiency,therefore,be expected to become the most promising means for neurorehabilitation[2].展开更多
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.Co...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.展开更多
This paper presents an upper limb exoskeleton that allows cognitive(through electromyography signals)and physical user interaction(through load cells sensors)for passive and active exercises that can activate neuropla...This paper presents an upper limb exoskeleton that allows cognitive(through electromyography signals)and physical user interaction(through load cells sensors)for passive and active exercises that can activate neuroplasticity in the rehabilitation process of people who suffer from a neurological injury.For the exoskeleton to be easily accepted by patients who suffer from a neurological injury,we used the ISO9241-210:2010 as a methodology design process.As the first steps of the design process,design requirements were collected from previous usability tests and literature.Then,as a second step,a technological solution is proposed,and as a third step,the system was evaluated through performance and user testing.As part of the technological solution and to allow patient participation during the rehabilitation process,we have proposed a hybrid admittance control whose input is load cell or electromyography signals.The hybrid admittance control is intended for active therapy exercises,is easily implemented,and does not need musculoskeletal modeling to work.Furthermore,electromyography signals classification models and features were evaluated to identify the best settings for the cognitive human–robot interaction.展开更多
In this paper,a Novel Compliant Actuator(NCA)-driven Upper-Limb Exoskeleton(ULE)with force controllable,impact resistance,and back drivability is designed to ensure the safety of the subject during Human-Robot Interac...In this paper,a Novel Compliant Actuator(NCA)-driven Upper-Limb Exoskeleton(ULE)with force controllable,impact resistance,and back drivability is designed to ensure the safety of the subject during Human-Robot Interaction(HRI)processing.Based on the designed NCA-driven ULE,this paper constructs a Model Predictive Control Scheme(MPCS)for force trajectory tracking,which minimises future tracking errors by solving an optimal control problem with inequality constraints.In addition,an Error-Accumulation Improved Newton Algorithm(EAINA)is proposed to solve the MPCS for suppressing various noises and external disturbances.The proposed EAINA is theoretically proved to have small steady state for noise conditions and stability of the EAINA using Lyapunov method.Finally,experimental results verify that the proposed MPCS solved by the EAINA in the NCA-driven ULE achieves robustness,fast convergence,strong tolerance and stability for trajectory rehabilitation task.展开更多
Musculoskeletal Symptoms(MSS)often arise from prolonged maintenance of bent postures in the neck and trunk during surgical procedures.To prevent MSS,a passive exoskeleton utilizing carbon fiber beams to offer support ...Musculoskeletal Symptoms(MSS)often arise from prolonged maintenance of bent postures in the neck and trunk during surgical procedures.To prevent MSS,a passive exoskeleton utilizing carbon fiber beams to offer support to the neck and trunk was proposed.The application of support force is intended to reduce muscle forces and joint compression forces.A nonlinear mathematical model for the neck and trunk support beam is presented to estimate the support force.A validation test is subsequently conducted to assess the accuracy of the mathematical model.Finally,a preliminary functional evaluation test is performed to evaluate movement capabilities and support provided by the exoskeleton.The mathematical model demonstrates an accuracy for beam support force within a range of 0.8–1.2 N Root Mean Square Error(RMSE).The exoskeleton was shown to allow sufficient Range of Motion(ROM)for neck and trunk during open surgery training.While the exoskeleton showed potential in reducing musculoskeletal load and task difficulty during simulated surgery tasks,the observed reduction in perceived task difficulty was deemed non-significant.This prompts the recommendation for further optimization in personalized adjustments of beams to facilitate improvements in task difficulty and enhance comfort.展开更多
The study of exoskeletons has been a popular topic worldwide.However,there is still a long way to go before exoskeletons can be widely used.One of the major challenges is control,and there is no specific research tren...The study of exoskeletons has been a popular topic worldwide.However,there is still a long way to go before exoskeletons can be widely used.One of the major challenges is control,and there is no specific research trend for controlling exoskeletons.In this paper,we propose a novel exoskeleton control strategy that combines Active Disturbance Rejection Control(ADRC)and Deep Reinforcement Learning(DRL).The dynamic model of the exoskeleton is constructed,followed with the design of the ADRC.To automatically adjust the control parameters of the ADRC,the Twin-Delayed Deep Deterministic Policy Gradient(TD3)is utilized.Then a reward function is defined in terms of the joint angle,angular velocity,and their errors to the desired values,to maximize the accuracy of the joint angle.In the simulations and experiments,a conventional ADRC,and ADRC based on Genetic Algorithm(GA)and Particle Swarm Optimization(PSO)were carried out for comparison with the proposed control method.The results of the tests show that TD3-ADRC has a rapid response,small overshoot,and low Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)followed with the desired,demonstrating the superiority of the proposed control method for the self-learning control of exoskeleton.展开更多
With the acceleration of the global aging process and the increase of cardiovascular ancerebrovascular diseases,more and more patients are paralyzed due to accidents,so theexoskeleton robot began to appear in people&#...With the acceleration of the global aging process and the increase of cardiovascular ancerebrovascular diseases,more and more patients are paralyzed due to accidents,so theexoskeleton robot began to appear in people's sight,and the lower limb exoskeleton robot withrehabilitation training is also favored by more and more people.In this paper,the structural designand analysis of the lower limb exoskeleton robot are carried out in view of the patients'expectation ofnormal walking.First,gait analysis and structural design of lower limb exoskeleton robot.Based onthe analysis of the walking gait of normal people,the freedom of the three key joints of the lower limbexoskeleton robot hip joint,knee joint and ankle joint is determined.at the same time,according tothe structuralcharacteristics of each joint,the three key joints are modeled respectively,and theoverall model assembly of the lower limb exoskeleton robot is completed.Secondly,the kinematicsanalysis of the lower limb exoskeleton robot was carried out to obtain the relationship between thelinear displacement,linear speed and acceleration of each joint,so as to ensure the coordination ofthe model with the human lower limb movement.Thirdly,the static analysis of typical gait of hipjoint,knee joint and ankle joint is carried out to verify the safety of the design model under thepremise of ensuring the structural strength requirements.Finally,the parts of the model were 3Dprinted,and the rationality of the design was further verified in the process of assembling the model.展开更多
Accurate trajectory tracking in lower-limb exoskeletons is challenged by the nonlinear,time-varying dynamics of human-robot interaction,limited sensor availability,and unknown external disturbances.This study proposes...Accurate trajectory tracking in lower-limb exoskeletons is challenged by the nonlinear,time-varying dynamics of human-robot interaction,limited sensor availability,and unknown external disturbances.This study proposes a novel control strategy that combines flatness-based control with two cascaded observers:a high-gain observer to estimate unmeasured joint velocities,and a nonlinear disturbance observer to reconstruct external torque disturbances in real time.These estimates are integrated into the control law to enable robust,state-feedback-based trajectory tracking.The approach is validated through simulation scenarios involving partial state measurements and abrupt external torque perturbations,reflecting realistic rehabilitation conditions.Results confirm that the proposed method significantly enhances tracking accuracy and disturbance rejection capability,demonstrating its strong potential for reliable and adaptive rehabilitation assistance.展开更多
To overcome the limitations of traditional exoskeletons in complex outdoor terrains,this study introduces a novel lower limb exoskeleton inspired by the snow leopard’s forelimb musculoskeletal structure.It features a...To overcome the limitations of traditional exoskeletons in complex outdoor terrains,this study introduces a novel lower limb exoskeleton inspired by the snow leopard’s forelimb musculoskeletal structure.It features a non-fully anthropomorphic design,attaching only at the thigh and ankle with a backward-knee configuration to mimic natural human knee movement.The design incorporates a single elastic element at the hip for gravity compensation and dual elastic elements at the knee for terrain adaptability,which adjust based on walking context.The design’s effectiveness was assessed by measuring metabolic cost reduction and motor output torque under various walking conditions.Results showed significant metabolic cost savings of 5.8–8.8%across different speeds and a 7.9%reduction during 9°incline walking on a flat indoor surface.Additionally,the spring element decreased hip motor output torque by 7–15.9%and knee torque by 8.1–14.2%.Outdoor tests confirmed the design’s robustness and effectiveness in reducing motor torque across terrains,highlighting its potential to advance multi-terrain adaptive exoskeleton research.展开更多
The lower limb assisted exoskeleton is a prominent area of research within the field of exoskeleton technology.However,several challenges remain,including the development of flexible actuators,high battery consumption...The lower limb assisted exoskeleton is a prominent area of research within the field of exoskeleton technology.However,several challenges remain,including the development of flexible actuators,high battery consumption,the risk of joint misalignment,and limited assistive capabilities.This paper proposes a compact flexible actuator incorporating two elastic elements named Adjustable Energy Storage Series Elastic Actuator(AES-SEA),which combining an adjustable energy storage device with a series elastic actuator for application in exoskeleton hip joints.This design aims to enhance energy efficiency and improve assistive effects.Subsequently,we introduce a novel knee joint bionic structure based on a pulley-groove configuration and a four-link mechanism,designed to replicate human knee joint motion and prevent joint misalignment.Additionally,we propose an innovative controller that integrates concepts from Linear Quadratic Regulator(LQR)control and virtual tunnel for level walking assistance.This controller modulates the assisted reference trajectory using the virtual tunnel concept,enabling different levels of assistance both inside and outside the tunnel by adjusting the parameters Q and R.This approach enhances the assisting force while ensuring the safety of human-computer interaction.Finally,metabolic experiments were conducted to evaluate the effectiveness of the exoskeleton assistance.展开更多
This paper proposes virtual impedance adaptation of the lower-limb exoskeleton for human performance augmentation(LEHPA) based on deep reinforcement learning(VIADRL) to mitigate reliance on model accuracy and address ...This paper proposes virtual impedance adaptation of the lower-limb exoskeleton for human performance augmentation(LEHPA) based on deep reinforcement learning(VIADRL) to mitigate reliance on model accuracy and address the ever-changing human-exoskeleton interaction(HEI) dynamics. The classical sensitivity amplification control strategy is expanded to the virtual impedance control strategy with more learnable virtual impedance parameters. The adjustment of these virtual impedance parameters is formalized as finding the optimal policy for a Markov Decision Process and can then be effectively resolved using deep reinforcement learning algorithms. To ensure safe and efficient policy training, a multibody simulation environment is established to facilitate the training process, supplemented by the innovative hybrid inverse-forward dynamics simulation approach for executing the simulation. For comparison purposes, the SADRL strategy is introduced as a benchmark. A novel control performance evaluation method based on the HEI forces at the back, thighs, and shanks is proposed to quantitatively evaluate the performance of our proposed VIADRL strategy. The VIADRL controller is systematically compared with the SADRL controller at five selected walking speeds. The lumped ratio of HEI forces under the SADRL strategy relative to those under the SADRL strategy is as low as 0.81 in simulation and approximately 0.89 on the LEHPA prototype. The overall reduction of HEI forces demonstrates the superiority of the VIADRL strategy in comparison to the SADRL strategy.展开更多
Focusing on the rehabilitation training of hemiplegia patients,this paper proposes a gait-planning strategy based on a central pattern generator and an adaptive time-delay control scheme that utilizes recursive termin...Focusing on the rehabilitation training of hemiplegia patients,this paper proposes a gait-planning strategy based on a central pattern generator and an adaptive time-delay control scheme that utilizes recursive terminal sliding mode for lower limb rehabilitation exoskeleton robots.The central pattern generator network plans a reference gait trajectory for the affected leg,synchronized with the movement of the healthy leg.The proposed adaptive time-delay control scheme possesses a model-independent property due to the mechanism of time-delay estimation,with adaptive control gains that enhance the resilience against system perturbations and a recursive terminal sliding mode control component to achieve a fast convergence rate.According to the Lyapunov stability criterion,it is proved that the gait trajectory-tracking error is uniformly ultimately bounded.Experiments are conducted on a lower limb exoskeleton experimental platform,and the experimental results demonstrate the effectiveness and superiority of the proposed strategies.展开更多
To achieve the fast convergence and tracking precision of a robotic upper-limb exoskeleton,this paper proposes an observer-based integrated fixed-time control scheme with a backstepping method.Firstly,a typical 5 DoF(...To achieve the fast convergence and tracking precision of a robotic upper-limb exoskeleton,this paper proposes an observer-based integrated fixed-time control scheme with a backstepping method.Firstly,a typical 5 DoF(degrees of freedom)dynamics is constructed by Lagrange equations and processed for control purposes.Secondly,second-order sliding mode controllers(SOSMC)are developed and novel sliding mode surfaces are introduced to ensure the fixed-time convergence of the human-robot system.Both the reaching time and settling time are proved to be bounded with certain values independent of initial system conditions.For the purpose of rejecting the matched and unmatched disturbances,nonlinear fixed-time observers are employed to estimate the exact value of disturbances and compensate the controllers online.Ultimately,the synthesis of controllers and disturbance observers is adopted to achieve the excellent tracking performance and simulations are given to verify the effectiveness of the proposed control strategy.展开更多
基金funded by the King Salman Center For Disability Research,through Research Group No.KSRG-2024-468。
文摘Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent.However,controlling an exoskeleton for rehabilitation presents challenges due to their nonlinear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton.To remedy these problems,this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons,addressing the challenges of nonlinear dynamics and external disturbances.The proposed controller integrated a Radial Basis Function Neural Network(RBFNN)with a disturbance observer and employed a high-dimensional integral Lyapunov function to guarantee system stability and trajectory tracking performance.In the control system,the role of the RBFNN was to estimate uncertain signals in the dynamic model,while the disturbance observer tackled external disturbances during trajectory tracking.Artificially created scenarios for Human-Robot interactive experiments and periodically repeated reference trajectory experiments validated the controller’s performance,demonstrating efficient tracking.The proposed controller is found to achieve superior tracking accuracy with Root-Mean-Squared(RMS)errors of 0.022-0.026 rad for all joints,outperforming conventional Proportional-Integral-Derivative(PID)by 73%and Neural-Fuzzy Adaptive Control(NFAC)by 389.47%lower error.These results suggested that the RBFNN adaptive controller,coupled with disturbance compensation,could serve as an effective rehabilitation tool for upper-limb exoskeletons.These results demonstrate the superiority of the proposed method in enhancing rehabilitation accuracy and robustness,offering a promising solution for the control of upper-limb assistive devices.Based on the obtained results and due to their high robustness,the proposed control schemes can be extended to other motor disabilities,including lower limb exoskeletons.
基金supported in part by the National Key Research and Development Program of China(2022YFC3601200)the National Natural Science Foundation of China(62203441,U21A20479)+1 种基金the Beijing Natural Science Foundation(L232005)the Inner Mongolia Autonomous Region Science and Technology Plan(2023YFDZ0042).
文摘Dear Editor,This letter investigates the system development of a multi-joint rehabilitation exoskeleton,and highlights the subject-adaptive control factors for efficient motor learning.In order to enable the natural mobility of the human upper extremity,we design the shoulder mechanism by arranging three rotational joints with acute angles,and adopt a serial chain structure for the fully constructed system.After the kinematics and dynamics of CASIA-EXO are modelled,the patient-in-the-loop control strategy is proposed for rehabilitation training,consisting of the intention-based trajectory planning and performance-based intervention adaptation.Finally,we conduct experiments to validate the efficacy of the control system,and further demonstrate the potential of CASIA-EXO in neurorehabilitation.Introduction:Neurological diseases are the leading cause of nontraumatic disability worldwide,and stroke is one of the most common encountered neurological injury,which is suffered by over 15 million individuals each year,and about 70%−80%of these individuals have varying degrees of functional impairments[1].In order to facilitate the motor relearning in central nervous system,post-stroke patients need to undergo long-term rehabilitation training to promote neural plasticity,thereby enhancing the recovery of motor function in activities of daily living(ADLs).Evidence in the clinical studies suggests that robot-assisted rehabilitation integrating neuroscience,biomechanics,and automation control can improve the patients’motivation for active participation while improving the treatment efficiency,therefore,be expected to become the most promising means for neurorehabilitation[2].
基金supported in part by the National Natural Science Foundation of China (62173182,61773212)the Intergovernmental International Science and Technology Innovation Cooperation Key Project of Chinese National Key R&D Program (2021YFE0102700)。
文摘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.
文摘This paper presents an upper limb exoskeleton that allows cognitive(through electromyography signals)and physical user interaction(through load cells sensors)for passive and active exercises that can activate neuroplasticity in the rehabilitation process of people who suffer from a neurological injury.For the exoskeleton to be easily accepted by patients who suffer from a neurological injury,we used the ISO9241-210:2010 as a methodology design process.As the first steps of the design process,design requirements were collected from previous usability tests and literature.Then,as a second step,a technological solution is proposed,and as a third step,the system was evaluated through performance and user testing.As part of the technological solution and to allow patient participation during the rehabilitation process,we have proposed a hybrid admittance control whose input is load cell or electromyography signals.The hybrid admittance control is intended for active therapy exercises,is easily implemented,and does not need musculoskeletal modeling to work.Furthermore,electromyography signals classification models and features were evaluated to identify the best settings for the cognitive human–robot interaction.
基金supported by the National Natural Science Foundation of China(Nos.62373065,61873304,62173048,and 62106023)the Key Science and Technology Projects of Jilin Province,China(No.20230204081YY).
文摘In this paper,a Novel Compliant Actuator(NCA)-driven Upper-Limb Exoskeleton(ULE)with force controllable,impact resistance,and back drivability is designed to ensure the safety of the subject during Human-Robot Interaction(HRI)processing.Based on the designed NCA-driven ULE,this paper constructs a Model Predictive Control Scheme(MPCS)for force trajectory tracking,which minimises future tracking errors by solving an optimal control problem with inequality constraints.In addition,an Error-Accumulation Improved Newton Algorithm(EAINA)is proposed to solve the MPCS for suppressing various noises and external disturbances.The proposed EAINA is theoretically proved to have small steady state for noise conditions and stability of the EAINA using Lyapunov method.Finally,experimental results verify that the proposed MPCS solved by the EAINA in the NCA-driven ULE achieves robustness,fast convergence,strong tolerance and stability for trajectory rehabilitation task.
基金funded by China Scholarship Council,Grant Number 201906840121department of rehabilitation medicine,University Medical Center Groningen,University of Groningen,grant number:O/085350.
文摘Musculoskeletal Symptoms(MSS)often arise from prolonged maintenance of bent postures in the neck and trunk during surgical procedures.To prevent MSS,a passive exoskeleton utilizing carbon fiber beams to offer support to the neck and trunk was proposed.The application of support force is intended to reduce muscle forces and joint compression forces.A nonlinear mathematical model for the neck and trunk support beam is presented to estimate the support force.A validation test is subsequently conducted to assess the accuracy of the mathematical model.Finally,a preliminary functional evaluation test is performed to evaluate movement capabilities and support provided by the exoskeleton.The mathematical model demonstrates an accuracy for beam support force within a range of 0.8–1.2 N Root Mean Square Error(RMSE).The exoskeleton was shown to allow sufficient Range of Motion(ROM)for neck and trunk during open surgery training.While the exoskeleton showed potential in reducing musculoskeletal load and task difficulty during simulated surgery tasks,the observed reduction in perceived task difficulty was deemed non-significant.This prompts the recommendation for further optimization in personalized adjustments of beams to facilitate improvements in task difficulty and enhance comfort.
基金funded by Zhiyuan Laboratory(Grant NO.ZYL2024017a).
文摘The study of exoskeletons has been a popular topic worldwide.However,there is still a long way to go before exoskeletons can be widely used.One of the major challenges is control,and there is no specific research trend for controlling exoskeletons.In this paper,we propose a novel exoskeleton control strategy that combines Active Disturbance Rejection Control(ADRC)and Deep Reinforcement Learning(DRL).The dynamic model of the exoskeleton is constructed,followed with the design of the ADRC.To automatically adjust the control parameters of the ADRC,the Twin-Delayed Deep Deterministic Policy Gradient(TD3)is utilized.Then a reward function is defined in terms of the joint angle,angular velocity,and their errors to the desired values,to maximize the accuracy of the joint angle.In the simulations and experiments,a conventional ADRC,and ADRC based on Genetic Algorithm(GA)and Particle Swarm Optimization(PSO)were carried out for comparison with the proposed control method.The results of the tests show that TD3-ADRC has a rapid response,small overshoot,and low Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)followed with the desired,demonstrating the superiority of the proposed control method for the self-learning control of exoskeleton.
基金College Student Innovation andEntrepreneurship Project(Grant No.:S202414435026)ingkou Institute of Technology campus level research project——Development of food additive supercritical extraction equipment and fluid transmission systemresearch(Grant No.HX202427).
文摘With the acceleration of the global aging process and the increase of cardiovascular ancerebrovascular diseases,more and more patients are paralyzed due to accidents,so theexoskeleton robot began to appear in people's sight,and the lower limb exoskeleton robot withrehabilitation training is also favored by more and more people.In this paper,the structural designand analysis of the lower limb exoskeleton robot are carried out in view of the patients'expectation ofnormal walking.First,gait analysis and structural design of lower limb exoskeleton robot.Based onthe analysis of the walking gait of normal people,the freedom of the three key joints of the lower limbexoskeleton robot hip joint,knee joint and ankle joint is determined.at the same time,according tothe structuralcharacteristics of each joint,the three key joints are modeled respectively,and theoverall model assembly of the lower limb exoskeleton robot is completed.Secondly,the kinematicsanalysis of the lower limb exoskeleton robot was carried out to obtain the relationship between thelinear displacement,linear speed and acceleration of each joint,so as to ensure the coordination ofthe model with the human lower limb movement.Thirdly,the static analysis of typical gait of hipjoint,knee joint and ankle joint is carried out to verify the safety of the design model under thepremise of ensuring the structural strength requirements.Finally,the parts of the model were 3Dprinted,and the rationality of the design was further verified in the process of assembling the model.
基金funded by the King Salman Center for Disability Research,through Research Group No.KSRG-2024-468.
文摘Accurate trajectory tracking in lower-limb exoskeletons is challenged by the nonlinear,time-varying dynamics of human-robot interaction,limited sensor availability,and unknown external disturbances.This study proposes a novel control strategy that combines flatness-based control with two cascaded observers:a high-gain observer to estimate unmeasured joint velocities,and a nonlinear disturbance observer to reconstruct external torque disturbances in real time.These estimates are integrated into the control law to enable robust,state-feedback-based trajectory tracking.The approach is validated through simulation scenarios involving partial state measurements and abrupt external torque perturbations,reflecting realistic rehabilitation conditions.Results confirm that the proposed method significantly enhances tracking accuracy and disturbance rejection capability,demonstrating its strong potential for reliable and adaptive rehabilitation assistance.
基金sponsored by the Fundamental Research Funds for the Central Universities[N2329001].
文摘To overcome the limitations of traditional exoskeletons in complex outdoor terrains,this study introduces a novel lower limb exoskeleton inspired by the snow leopard’s forelimb musculoskeletal structure.It features a non-fully anthropomorphic design,attaching only at the thigh and ankle with a backward-knee configuration to mimic natural human knee movement.The design incorporates a single elastic element at the hip for gravity compensation and dual elastic elements at the knee for terrain adaptability,which adjust based on walking context.The design’s effectiveness was assessed by measuring metabolic cost reduction and motor output torque under various walking conditions.Results showed significant metabolic cost savings of 5.8–8.8%across different speeds and a 7.9%reduction during 9°incline walking on a flat indoor surface.Additionally,the spring element decreased hip motor output torque by 7–15.9%and knee torque by 8.1–14.2%.Outdoor tests confirmed the design’s robustness and effectiveness in reducing motor torque across terrains,highlighting its potential to advance multi-terrain adaptive exoskeleton research.
基金supported by Guangdong Provincial Key Laboratory of Minimally Invasive Surgical Instruments and Manufacturing Technology(MISIMT-2021-4)the Fundamental Research Funds for the Central Universities(N2329001).
文摘The lower limb assisted exoskeleton is a prominent area of research within the field of exoskeleton technology.However,several challenges remain,including the development of flexible actuators,high battery consumption,the risk of joint misalignment,and limited assistive capabilities.This paper proposes a compact flexible actuator incorporating two elastic elements named Adjustable Energy Storage Series Elastic Actuator(AES-SEA),which combining an adjustable energy storage device with a series elastic actuator for application in exoskeleton hip joints.This design aims to enhance energy efficiency and improve assistive effects.Subsequently,we introduce a novel knee joint bionic structure based on a pulley-groove configuration and a four-link mechanism,designed to replicate human knee joint motion and prevent joint misalignment.Additionally,we propose an innovative controller that integrates concepts from Linear Quadratic Regulator(LQR)control and virtual tunnel for level walking assistance.This controller modulates the assisted reference trajectory using the virtual tunnel concept,enabling different levels of assistance both inside and outside the tunnel by adjusting the parameters Q and R.This approach enhances the assisting force while ensuring the safety of human-computer interaction.Finally,metabolic experiments were conducted to evaluate the effectiveness of the exoskeleton assistance.
文摘This paper proposes virtual impedance adaptation of the lower-limb exoskeleton for human performance augmentation(LEHPA) based on deep reinforcement learning(VIADRL) to mitigate reliance on model accuracy and address the ever-changing human-exoskeleton interaction(HEI) dynamics. The classical sensitivity amplification control strategy is expanded to the virtual impedance control strategy with more learnable virtual impedance parameters. The adjustment of these virtual impedance parameters is formalized as finding the optimal policy for a Markov Decision Process and can then be effectively resolved using deep reinforcement learning algorithms. To ensure safe and efficient policy training, a multibody simulation environment is established to facilitate the training process, supplemented by the innovative hybrid inverse-forward dynamics simulation approach for executing the simulation. For comparison purposes, the SADRL strategy is introduced as a benchmark. A novel control performance evaluation method based on the HEI forces at the back, thighs, and shanks is proposed to quantitatively evaluate the performance of our proposed VIADRL strategy. The VIADRL controller is systematically compared with the SADRL controller at five selected walking speeds. The lumped ratio of HEI forces under the SADRL strategy relative to those under the SADRL strategy is as low as 0.81 in simulation and approximately 0.89 on the LEHPA prototype. The overall reduction of HEI forces demonstrates the superiority of the VIADRL strategy in comparison to the SADRL strategy.
基金supported by the National Natural Science Foundation of China(62473337,62003305)the Key Research and Development Program of Zhejiang Province(2024C03040,2022C03029)the funding of Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang Province(2023R01006).
文摘Focusing on the rehabilitation training of hemiplegia patients,this paper proposes a gait-planning strategy based on a central pattern generator and an adaptive time-delay control scheme that utilizes recursive terminal sliding mode for lower limb rehabilitation exoskeleton robots.The central pattern generator network plans a reference gait trajectory for the affected leg,synchronized with the movement of the healthy leg.The proposed adaptive time-delay control scheme possesses a model-independent property due to the mechanism of time-delay estimation,with adaptive control gains that enhance the resilience against system perturbations and a recursive terminal sliding mode control component to achieve a fast convergence rate.According to the Lyapunov stability criterion,it is proved that the gait trajectory-tracking error is uniformly ultimately bounded.Experiments are conducted on a lower limb exoskeleton experimental platform,and the experimental results demonstrate the effectiveness and superiority of the proposed strategies.
基金supported by National Natural Science Foundation of China (Nos. 61703134, 61703135, 61773151, 61503118 and 61871173)Natural Science Foundation of Hebei Province (Nos. F2015202150, F2016202327 and F2018202279)+3 种基金Natural Science Foundation of Tianjin (No. 17JCQNJC04400)the Foundation of Hebei Educational Committee (Nos. QN2015068 and ZD2016071)the Colleges and Universities in Hebei Province Science and Technology Research Youth Fund (No. ZC2016020)the Graduate Innovation Funding Project of Hebei Province (No. CXZZBS2017038)
文摘To achieve the fast convergence and tracking precision of a robotic upper-limb exoskeleton,this paper proposes an observer-based integrated fixed-time control scheme with a backstepping method.Firstly,a typical 5 DoF(degrees of freedom)dynamics is constructed by Lagrange equations and processed for control purposes.Secondly,second-order sliding mode controllers(SOSMC)are developed and novel sliding mode surfaces are introduced to ensure the fixed-time convergence of the human-robot system.Both the reaching time and settling time are proved to be bounded with certain values independent of initial system conditions.For the purpose of rejecting the matched and unmatched disturbances,nonlinear fixed-time observers are employed to estimate the exact value of disturbances and compensate the controllers online.Ultimately,the synthesis of controllers and disturbance observers is adopted to achieve the excellent tracking performance and simulations are given to verify the effectiveness of the proposed control strategy.
