In this paper we develop an elasto-dynamic model of the human arm that includes effects of neuro-muscular control upon elastic deformation in the limb.The elasto-dynamic model of the arm is based on hybrid parameter m...In this paper we develop an elasto-dynamic model of the human arm that includes effects of neuro-muscular control upon elastic deformation in the limb.The elasto-dynamic model of the arm is based on hybrid parameter multiple body system variational projection principles presented in the companion paper.Though the technique is suitable for detailed bone and joint modeling,we present simulations for simplified geometry of the bones,discretized as Rayleigh beams with elongation,while allowing for large deflections.Motion of the upper extremity is simulated by incorporating muscle forces derived from a Hill-type model of musculotendon dynamics.The effects of muscle force are modeled in two ways.In one approach,an effective joint torque is calculated by multiplying the muscle force by a joint moment ann.A second approach models the muscle as acting along a straight line between the origin and insertion sites of the tendon.Simple arm motion is simulated by utilizing neural feedback and feedforward control.Simulations illustrate the combined effects of neural control strategies, models of muscle force inclusion,and elastic assumptions on joint trajectories and stress and strain development in the bone and tendon.展开更多
In this paper we develop an elasto-dynamic model of the human arm for use in neuro-muscular control and dynamic interaction studies.The motivation for this work is to present a case for developing and using non-quasis...In this paper we develop an elasto-dynamic model of the human arm for use in neuro-muscular control and dynamic interaction studies.The motivation for this work is to present a case for developing and using non-quasistatic models of human musculo-skeletal biomechanics.The model is based on hybrid parameter multiple body system(HPMBS)variational projection principles.In this paper,we present an overview of the HPMBS variational principle applied to the full elasto-dynamic model of the arm.The generality of the model allows one to incorporate muscle effects as either loads transmitted through the tendon at points of origin and insertion or as an effective torque at a joint.Though the technique is suitable for detailed bone and joint modeling,we present in this initial effort only simple geometry with the bones discretized as Rayleigh beams with elongation, while allowing for large deflections.Simulations demonstrate the viability of the mcthod for use in the companion paper and in future studies.展开更多
An inverted pendulum is a sensitive system of highly coupled parameters, in laboratories, it is popular for modelling nonlinear systems such as mechanisms and control systems, and also for optimizing programmes before...An inverted pendulum is a sensitive system of highly coupled parameters, in laboratories, it is popular for modelling nonlinear systems such as mechanisms and control systems, and also for optimizing programmes before those programmes are applied in real situations. This study aims to find the optimum input setting for a double inverted pendulum（DIP）, which requires an appropriate input to be able to stand and to achieve robust stability even when the system model is unknown. Such a DIP input could be widely applied in engineering fields for optimizing unknown systems with a limited budget. Previous studies have used various mathematical approaches to optimize settings for DIP, then have designed control algorithms or physical mathematical models.This study did not adopt a mathematical approach for the DIP controller because our DIP has five input parameters within its nondeterministic system model. This paper proposes a novel algorithm, named Uni Neuro, that integrates neural networks（NNs） and a uniform design（UD） in a model formed by input and response to the experimental data（metamodel）. We employed a hybrid UD multiobjective genetic algorithm（HUDMOGA） for obtaining the optimized setting input parameters. The UD was also embedded in the HUDMOGA for enriching the solution set, whereas each chromosome used for crossover, mutation, and generation of the UD was determined through a selection procedure and derived individually. Subsequently, we combined the Euclidean distance and Pareto front to improve the performance of the algorithm. Finally, DIP equipment was used to confirm the settings. The proposed algorithm can produce 9 alternative configured input parameter values to swing-up then standing in robust stability of the DIP from only 25 training data items and 20 optimized simulation results. In comparison to the full factorial design, this design can save considerable experiment time because the metamodel can be formed by only 25 experiments using the UD. Furthermore, the proposed algorithm can be applied to nonlinear systems with multiple constraints.展开更多
文摘In this paper we develop an elasto-dynamic model of the human arm that includes effects of neuro-muscular control upon elastic deformation in the limb.The elasto-dynamic model of the arm is based on hybrid parameter multiple body system variational projection principles presented in the companion paper.Though the technique is suitable for detailed bone and joint modeling,we present simulations for simplified geometry of the bones,discretized as Rayleigh beams with elongation,while allowing for large deflections.Motion of the upper extremity is simulated by incorporating muscle forces derived from a Hill-type model of musculotendon dynamics.The effects of muscle force are modeled in two ways.In one approach,an effective joint torque is calculated by multiplying the muscle force by a joint moment ann.A second approach models the muscle as acting along a straight line between the origin and insertion sites of the tendon.Simple arm motion is simulated by utilizing neural feedback and feedforward control.Simulations illustrate the combined effects of neural control strategies, models of muscle force inclusion,and elastic assumptions on joint trajectories and stress and strain development in the bone and tendon.
文摘In this paper we develop an elasto-dynamic model of the human arm for use in neuro-muscular control and dynamic interaction studies.The motivation for this work is to present a case for developing and using non-quasistatic models of human musculo-skeletal biomechanics.The model is based on hybrid parameter multiple body system(HPMBS)variational projection principles.In this paper,we present an overview of the HPMBS variational principle applied to the full elasto-dynamic model of the arm.The generality of the model allows one to incorporate muscle effects as either loads transmitted through the tendon at points of origin and insertion or as an effective torque at a joint.Though the technique is suitable for detailed bone and joint modeling,we present in this initial effort only simple geometry with the bones discretized as Rayleigh beams with elongation, while allowing for large deflections.Simulations demonstrate the viability of the mcthod for use in the companion paper and in future studies.
基金supported by Indonesian Government(No.BPPLN DIKTI 3+1)
文摘An inverted pendulum is a sensitive system of highly coupled parameters, in laboratories, it is popular for modelling nonlinear systems such as mechanisms and control systems, and also for optimizing programmes before those programmes are applied in real situations. This study aims to find the optimum input setting for a double inverted pendulum（DIP）, which requires an appropriate input to be able to stand and to achieve robust stability even when the system model is unknown. Such a DIP input could be widely applied in engineering fields for optimizing unknown systems with a limited budget. Previous studies have used various mathematical approaches to optimize settings for DIP, then have designed control algorithms or physical mathematical models.This study did not adopt a mathematical approach for the DIP controller because our DIP has five input parameters within its nondeterministic system model. This paper proposes a novel algorithm, named Uni Neuro, that integrates neural networks（NNs） and a uniform design（UD） in a model formed by input and response to the experimental data（metamodel）. We employed a hybrid UD multiobjective genetic algorithm（HUDMOGA） for obtaining the optimized setting input parameters. The UD was also embedded in the HUDMOGA for enriching the solution set, whereas each chromosome used for crossover, mutation, and generation of the UD was determined through a selection procedure and derived individually. Subsequently, we combined the Euclidean distance and Pareto front to improve the performance of the algorithm. Finally, DIP equipment was used to confirm the settings. The proposed algorithm can produce 9 alternative configured input parameter values to swing-up then standing in robust stability of the DIP from only 25 training data items and 20 optimized simulation results. In comparison to the full factorial design, this design can save considerable experiment time because the metamodel can be formed by only 25 experiments using the UD. Furthermore, the proposed algorithm can be applied to nonlinear systems with multiple constraints.
