Masasuke Shima

Analysis of a muscular control system in human movements

Abstract. In this work, we have studied a muscular control system under experimental conditions for analyzing the dynamic behavior of individual muscles and theoretical considerations for elucidating its control strategy. Movement of human limbs is achieved by joint torques and each torque is specified as the sum of torques generated by muscle forces. The behavior of individual muscles is controlled by the neural input which is estimated by means of an electromyogram (EMG). In this study, the EMGs for a flexor and an extensor are measured in elbow joint movements and the dynamic behavior of individual muscles is analyzed. As a result, it is verified that both a flexor and an extensor are activated throughout the entire movement and that the activation of muscles is controlled above a specific limit independent of the hand-held load. Subsequently, a system model for simulating elbow joint movements is developed which includes the muscle dynamic relationship between the neural input and the isometric force. The minimum limit of muscle activation that has been confirmed in experiments is provided as a constraint of the neural input and the criterion is defined by a derivative of the isometric force of individual muscles. The optimal trajectories formulated under these conditions are quantitatively compared with the experimentally observed trajectories, and the control strategy of a muscular control system is studied. Finally, a muscular control system in multi-joint arm movements is discussed with regard to the comparative analysis between observed and optimal trajectories.

An optimal control model of a neuromuscular system in human arm movements and its control characteristics

The joint torque which sets human limbs into motion is generated by a separate group of muscles provided for each joint. As the activation of each muscle is determined by a neural input, a neuromuscular system controlling all muscles has to be considered in order to understand human movements. In this study, an optimal control model of a neuromuscular system is investigated, and its control characteristics are examined. First, the dynamic and mechanical properties of a muscle are examined, and a neuromuscular system is formulated mathematically. Second, a performance criterion for the optimal control model is defined in order to characterize the dynamic behavior of the neuromuscular system, and a mathematical procedure for producing optimal trajectories is represented. Third, optimal trajectories in human arm movements are produced under various conditions of movement, and these trajectories are compared with experimentally observed ones. It is then verified that the optimal trajectories demonstrate human arm movements well. Finally, the behavior of individual muscles in various movements is examined quantitatively by means of simulation results, and the control characteristics of the human neuromuscular system are investigated.

Tracking Control for Unlinearizable Nonlinear Systems

Abstract In this paper, we consider a tracking problem for nonlinear systems of which input-output behaviors cannot be linearized. Such a system is converted to a singularly perturbed control system using a time-scale transformation and coordinate transformations under some assumptions. The exact-tracking control for the singularly perturbed control system is accomplished by a composite control law. The exact-tracking control law is approximated by truncating higher-order terms with respect to є. The proposed method is applied to a ball and beam example.

Adaptive Multi-Step Learning Control for Time-Varying Linear Systems

In this paper, the adaptive multi-step learning control for continuous-time linear system is studied. Input function uk+1 (t) is calculated from the triplets (uk, ek, ek) and (uk-1, ek-1, ek-1) where ek is tracking error of k-th trial. The weighting coefficients of the triplets are determined adaptively. The convergence of the control law is proved by means of evaluation of the norm of tracking error. Moreover, the learning control is applied to the tracking problem of two-link robot manipulator.