This paper studies cooperative visual environmental monitoring based on gradient descent techniques on SO(3). We consider the scenario that vision sensors located in 3-D space control their orientations in order to optimally monitor 2-D environment. Then, the decision variables are constrained on the Lie group SO(3). Hence, we formulate the monitoring problem as an optimization problem on SO(3) and present its solution based on the gradient descent algorithm on SO(3). The proposed solution does not require density, an indicator of importance, defined on the environment but relies only on the density on the acquired image data. This allows one to apply the present method to changing unknown environment. In this paper, we also present a procedure to estimate the image density using current technology and examine real-time feasibility of the estimation process. Finally, we demonstrate the effectiveness of the proposed approach through simulation.

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Flexible rotational inverted pendulum is an extension of Furuta inverted pendulum in which pendulum is no longer a rigid body and vibrates due to the elasticity of the flexible beam. In this paper, we design a nonlinear controller based on nonlinear frequency optimal control for swinging up and stabilization of the flexible beam by a single feedback control. This controller is solved via the stable manifold approach which is recently proposed for solving the Hamilton-Jacobi equation.

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Boolean networks are network systems composed of binary-state nodes, which are known to be a promising model of gene regulatory networks. Since the Boolean networks can be specified by the combination of the network structure and the component dynamics, it is important to clarify how the whole dynamics depends on the network structure or on the component dynamics. In this paper, we disclose the relationship between the network structure and the variety of the whole dynamics. In particular, we present an upper bound of the variety of dynamics, characterized by the network topology.

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This paper provides comprehensive design method of compensator for grid-connected PWM inverters with LCL-filter to supply high quality electric power from distributed generations. The proposed compensator ensures: i) high speed tracking with low distortion and zero steady-state errors for sinusoidal current reference; ii) robust responses against frequency and voltage changes of grid power supply. The proposed design methodology is based on the utmost compensator that covers all realizable linear control systems. Various multi-objective control requirements can be considered in the design framework directly, and then, trial-and-error process is reduced. The software-in-the-loop simulations are presented to support the advantages of the design and to demonstrate the system performance compared to traditional Linear Quadratic Regulator with Internal Model controller.

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While driving, train drivers need to take into account a variety of work constraints involving safety, schedule adherence, passenger comfort, and so on. They rely largely on their own knowledge and capability to be aware of those constraints in order to comprehend the train state and carry out decision-making. In this study, we have conducted analysis of the train driving task to specify its constraint structure and requirements to information displays to support human drivers. Focusing on constraints for compliance with the regular running time, a configural display has been invented which visualizes the train speed with respect to the upper/lower speed boundaries of the running time constraint. After the classification of constraint-based and instruction-based approaches to task analysis, we empirically investigated advantages of the information display by comparing it with another display which gives more specific operational guidance in an instructive manner. Results indicate that, while performance variation among different drivers was reduced by both displays, the constraint-based display can give more opportunities to drivers to adapt themselves to variable work conditions.

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This paper proposes a suboptimal feedback controller design for nonlinear distributed parameter systems via stable manifold method. Stable manifold method provides approximate stabilizing solutions of Hamilton-Jacobi equations in nonlinear optimal control theory. We apply this method to a reduced-order system obtained from proper orthogonal decomposition and Galerkin projection. The feasibility of the design is demonstrated by a numerical example of the feedback controls for the viscous Burgers' equation.

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This paper proposes a flocking algorithm on the Special Euclidean group SE(3) for a network of rigid bodies. The main feature of the present law is to embody the three fundamental flocking rules, cohesion, alignment, and separation, presented by Reynolds. We first define rigid body networks and tackle a pose synchronization problem as a partial flocking problem. Then, based on the Reynolds rules, we define flocking motion as the goal of this paper, propose a flocking algorithm and give the convergence analysis. Here, the present scheme is based only on relative pose information to neighbors for imitating actual flocking motion or facility of implementation. We finally show the effectiveness of the present algorithm through simulation.

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This paper is concerned with construction of non-parametric PWA (Piecewise Affine) models. Though their effectiveness is demonstrated in the literature, the existing identification method suffers from heavy computation load.This load is mainly due to the large data set which is required for representing the model. This paper proposes a method to construct non-parametric PWA models represented with less data points based on estimated Hessian.

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This paper is one of the researches to countermeasure against disaster. This paper treats the short-term future prediction of gas concentration in a building for safer evacuation. Moreover, in order to treat disaster, such as fire, failure of sensor installed in a building is assumed. Therefore, a fault-tolerant system against sensor's failure is designed. First, a concept of propagation coefficient is introduced, and a gas concentration model is constructed by propagation coefficient and amount of gas generation. And, in order to estimate these values, we apply Kalman Filter to this model. As this paper should consider sensor's failure, the fault-tolerant Kalman Filter is proposed. Then, by using the estimated result, the short-term future of gas concentration is predicted. Finally, we show experimental results to analyze effectiveness of the proposed method.

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