This paper addresses the problem of modelling, control, and simulation of a mechanical system actuated by an agonist-antagonist musculotendon subsystem. Contraction dynamics is given by case 1 of Zajac＇s model. Satur...This paper addresses the problem of modelling, control, and simulation of a mechanical system actuated by an agonist-antagonist musculotendon subsystem. Contraction dynamics is given by case 1 of Zajac＇s model. Saturated semi positive proportional-derivative-type controllers with switching as neural excitation inputs are proposed. Stability theory of switched system and SOSTOOLS, which is a sum of squares optimization toolbox of Matlab, are used to determine the stability of the obtained closed-loop system. To corroborate the obtained theoretical results numerical simulations are carried out. As additional contribution, the discussed ideas are applied to the biomimetic control of a DC motor, i.e., the position control is addressed assuming the presence of musculotendon actuators. Real-experiments corroborate the expected results.展开更多
Multibody musculoskeletal modeling of human gait has been proved helpful in investigating the pathology of musculoskeletal disorders.However,conventional inverse dynamics methods rely on external force sensors and can...Multibody musculoskeletal modeling of human gait has been proved helpful in investigating the pathology of musculoskeletal disorders.However,conventional inverse dynamics methods rely on external force sensors and cannot capture the nonlinear muscle behaviors.Meanwhile,the forward dynamics approach is computationally demanding and only suited for relatively simple tasks.This study proposed an integrated simulation methodology to fulfill the requirements of estimating foot-ground reaction force,tendon elasticity,and muscle recruitment optimization.A hybrid motion capture system,which combines the marker-based infrared device and markerless tracking through deep convolutional neural networks,was developed to track lower limb movements.The foot-ground reaction forces were determined by a contact model for soft materials,and its parameters were estimated using a two-step optimization method.The muscle recruitment problem was first resolved via a static optimization algorithm,and the obtained muscle activations were used as initial values for further simulation.A torque tracking procedure was then performed by minimizing the errors of joint torques calculated by musculotendon equilibrium equations and inverse dynamics.The proposed approach was validated against the electromyography measurements of a healthy subject during gait.The simulation framework provides a robust way of predicting joint torques,musculotendon forces,and muscle activations,which can be beneficial for understanding the biomechanics of normal and pathological gait.展开更多
文摘This paper addresses the problem of modelling, control, and simulation of a mechanical system actuated by an agonist-antagonist musculotendon subsystem. Contraction dynamics is given by case 1 of Zajac＇s model. Saturated semi positive proportional-derivative-type controllers with switching as neural excitation inputs are proposed. Stability theory of switched system and SOSTOOLS, which is a sum of squares optimization toolbox of Matlab, are used to determine the stability of the obtained closed-loop system. To corroborate the obtained theoretical results numerical simulations are carried out. As additional contribution, the discussed ideas are applied to the biomimetic control of a DC motor, i.e., the position control is addressed assuming the presence of musculotendon actuators. Real-experiments corroborate the expected results.
基金the National Natural Science Foundations of China(Grant Nos.12102035 and 12125201)the China Postdoctoral Science Foundation(Grant No.2020TQ0042)the Beijing Natural Science Foundation(Grant No.L212008).
文摘Multibody musculoskeletal modeling of human gait has been proved helpful in investigating the pathology of musculoskeletal disorders.However,conventional inverse dynamics methods rely on external force sensors and cannot capture the nonlinear muscle behaviors.Meanwhile,the forward dynamics approach is computationally demanding and only suited for relatively simple tasks.This study proposed an integrated simulation methodology to fulfill the requirements of estimating foot-ground reaction force,tendon elasticity,and muscle recruitment optimization.A hybrid motion capture system,which combines the marker-based infrared device and markerless tracking through deep convolutional neural networks,was developed to track lower limb movements.The foot-ground reaction forces were determined by a contact model for soft materials,and its parameters were estimated using a two-step optimization method.The muscle recruitment problem was first resolved via a static optimization algorithm,and the obtained muscle activations were used as initial values for further simulation.A torque tracking procedure was then performed by minimizing the errors of joint torques calculated by musculotendon equilibrium equations and inverse dynamics.The proposed approach was validated against the electromyography measurements of a healthy subject during gait.The simulation framework provides a robust way of predicting joint torques,musculotendon forces,and muscle activations,which can be beneficial for understanding the biomechanics of normal and pathological gait.
