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
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
Robot-assisted technology has been increasingly employed in the therapy of post stroke patients to deliver high-quality treatment and alleviate therapists5 burden.This paper introduces a novel parallel end traction ap...Robot-assisted technology has been increasingly employed in the therapy of post stroke patients to deliver high-quality treatment and alleviate therapists5 burden.This paper introduces a novel parallel end traction apparatus(PETA)to supplement equipment selection.Considering the appearance and performance of the PETA,two types of special five-bar linkage mechanisms are selected as the potential configurations of the actuation execution unit because of their compact arrangement and parallel structure.Kinematic analysis of each mechanism,i.e.,position solutions and Jacobian matrix,is carried out.Subsequently,a comparative study between the two mechanisms is conducted.In the established source of nondimensional parameter synthesis,the singularity,maximum continuous workspace,and performance variation trends are analyzed.Based on the evaluation results,the final scheme with determined configuration and corresponding near-optimized non-dimensional parameters is obtained.Then,a prototype is constructed.By adding a lockable translational degree of freedom in the vertical direction,the PETA can provide 2D planar exercise and 3D spatial exercise.Finally,a control system is developed for passive exercise mode based on the derived inverse position solution,and preliminary experiments are performed to verify the applicability of the PETA.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 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The authors thank Henan Huibo Medical Co.,Ltd.for several useful suggestions on this apparatus.Additionally,this research is funded in part by Beijing Natural Science Foundation(Grant No.3204036)in part by the National Key R&D Program of China(Grant Nos.2018YFB1307004 and 2020YFC2004200)+1 种基金in part by the National Natural Science Foundation of China(Grant No.61903011)in part by the General Program of Science and Technology Development Project of the Beijing Municipal Education Commission(Grant No.KM202010005021).
文摘Robot-assisted technology has been increasingly employed in the therapy of post stroke patients to deliver high-quality treatment and alleviate therapists5 burden.This paper introduces a novel parallel end traction apparatus(PETA)to supplement equipment selection.Considering the appearance and performance of the PETA,two types of special five-bar linkage mechanisms are selected as the potential configurations of the actuation execution unit because of their compact arrangement and parallel structure.Kinematic analysis of each mechanism,i.e.,position solutions and Jacobian matrix,is carried out.Subsequently,a comparative study between the two mechanisms is conducted.In the established source of nondimensional parameter synthesis,the singularity,maximum continuous workspace,and performance variation trends are analyzed.Based on the evaluation results,the final scheme with determined configuration and corresponding near-optimized non-dimensional parameters is obtained.Then,a prototype is constructed.By adding a lockable translational degree of freedom in the vertical direction,the PETA can provide 2D planar exercise and 3D spatial exercise.Finally,a control system is developed for passive exercise mode based on the derived inverse position solution,and preliminary experiments are performed to verify the applicability of the PETA.
