Yoichi Hirashima

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— In this paper, a swing-up control scheme for a serial double inverted pendulum is proposed. The control scheme is to swing up the pendulum in three steps, Step 1: to swing up the first pendulum, Step 2: to swing up the second pendulum while stabilizing the first pendulum at the upright position, and Step 3: to stabilize the two pendulums around the(More)
uses robustness of the right coprime factorization for robust stability of the closed-loop system with perturbation. Unfortunately, the robust right coprime factorization cannot easily improve the tracking performance of the control system except for simple cases. In this paper, nonlinear operator-based design method for nonlinear plant output to track a(More)
is evaluated by a total amount of costs. As a consequent, the total amount of costs reflects the number of container-movements that is required to achieve desired container-layout. After adequate autonomous learning, the optimum schedule for material handling operation can be obtained by selecting a series of container-movements that has the best(More)
This paper proposes a new generalized minimum-variance controller (GMVC) having new design parameters by using coprime factorization approach for multi-input multi-output (MIMO) case. The method is directly extended from a conventional GMVC and to construct the controller, it needs to solve only one Diophan-tine equation as in the conventional method. In(More)
The time varying human multijoint arm dynamics can be modeled by two factors, simplified musculoskeletal dynamics and the uncertainty factor consisting of measurement noises and modeling error of a rigid body dynamics. In some cases, the uncertainty factor may not be Gaussian; the Kalman filter is no longer the optimal filter. In this paper, for the(More)
—In this paper a new reinforcement learning system for generating marshaling plan of freight cars in a train is designed. In the proposed method, the total transfer distance of a locomotive is minimized to obtain the desired layout of freight cars for an outbound train. The order of movements of freight cars, the position for each removed car, the layout of(More)
—In this paper, a Q-Learning method for trasfer scheduling of freight cars in a train is proposed. In the proposed method, the number of freight-movements in order to line freights in the desired order is reflected by evaluation value for each pair of freight-layout and removal-destination at a freight yard. The best transfer scheduling can be derived by(More)