This article introduces a cable-driven lower limb rehabilitation robot with movable distal anchor points(M-CDLR).The traditional cable-driven parallel robots(CDPRs)control the moving platform by changing the length of...This article introduces a cable-driven lower limb rehabilitation robot with movable distal anchor points(M-CDLR).The traditional cable-driven parallel robots(CDPRs)control the moving platform by changing the length of cables,M-CDLR can also adjust the position of the distal anchor point when the moving platform moves.The M-CDLR this article proposed has gait and single-leg training modes,which correspond to the plane and space motion of the moving platform,respectively.After introducing the system structure configuration,the generalized kinematics and dynamics of M-CDLR are established.The fully constrained CDPRs can provide more stable rehabilitation training than the under-constrained one but requires more cables.Therefore,a motion planning method for the movable distal anchor point of M-CDLR is proposed to realize the theoretically fully constrained with fewer cables.Then the expected trajectory of the moving platform is obtained from the motion capture experiment,and the motion planning of M-CDLR under two training modes is simulated.The simulation results verify the effectiveness of the proposed motion planning method.This study serves as a basic theoretical study of the structure optimization and control strategy of M-CDLR.展开更多
As an innovative concept,an optimal predictive impedance controlle is introduced here to control a lower limb rehabilitation robo in the presence of uncertainty.The desired impedance law is considered to propose a con...As an innovative concept,an optimal predictive impedance controlle is introduced here to control a lower limb rehabilitation robo in the presence of uncertainty.The desired impedance law is considered to propose a conventional model-based impedance controller for the LLRR.However,external disturbances,model imperfection,and parameters uncertainties reduce the performance of the controller in practice.In order to cope with these uncertainties,an optimal predictive compensator is introduced as a solution for a proposed convex optimization problem,which is performed on a forward finite-length horizon.As a result,the LLRR has the desired behavior even in an uncertain environment.The performance and efficiency of the proposed controller are verified by the simulation results.展开更多
基金funded by the National Natural Science Foundation of China,Grant Number:52175006.
文摘This article introduces a cable-driven lower limb rehabilitation robot with movable distal anchor points(M-CDLR).The traditional cable-driven parallel robots(CDPRs)control the moving platform by changing the length of cables,M-CDLR can also adjust the position of the distal anchor point when the moving platform moves.The M-CDLR this article proposed has gait and single-leg training modes,which correspond to the plane and space motion of the moving platform,respectively.After introducing the system structure configuration,the generalized kinematics and dynamics of M-CDLR are established.The fully constrained CDPRs can provide more stable rehabilitation training than the under-constrained one but requires more cables.Therefore,a motion planning method for the movable distal anchor point of M-CDLR is proposed to realize the theoretically fully constrained with fewer cables.Then the expected trajectory of the moving platform is obtained from the motion capture experiment,and the motion planning of M-CDLR under two training modes is simulated.The simulation results verify the effectiveness of the proposed motion planning method.This study serves as a basic theoretical study of the structure optimization and control strategy of M-CDLR.
文摘As an innovative concept,an optimal predictive impedance controlle is introduced here to control a lower limb rehabilitation robo in the presence of uncertainty.The desired impedance law is considered to propose a conventional model-based impedance controller for the LLRR.However,external disturbances,model imperfection,and parameters uncertainties reduce the performance of the controller in practice.In order to cope with these uncertainties,an optimal predictive compensator is introduced as a solution for a proposed convex optimization problem,which is performed on a forward finite-length horizon.As a result,the LLRR has the desired behavior even in an uncertain environment.The performance and efficiency of the proposed controller are verified by the simulation results.
