In this paper, we propose a supervisory control system for motion planning of humanoid robots. The proposed system is hierarchically structured into two levels. The lower level controls and monitors the robots using modular state nets. The upper level generates an optimal sequence of motion for user's requirements using timed Petri nets.

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This paper deals with an autonomous distributed conveyance system under a dynamic environment. In order for each agent to implement strategy determination autonomously, it needs to acquire environmental information. In this system, information is exchanged among other agents. By using Bayesian inference for gathered local information, the overall situation is predicted and strategy is determined. Moreover, when current environmental information differs from information acquired from environment by environmental big change etc., cancellation of the current strategy and re-decision are implemented.

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This paper proposes two algorithms (Algorithm I and Algorithm II) for solving a stochastic discrete algebraic Riccati equation, which arises in stochastic optimal control for the discrete-time system. Our algorithms are generalized versions of Hewer's algorithm. Algorithm I has the quadratic convergence but requires to solve a sequence of non-standard Lyapunov equations. On the other hand, Algorithm II needs the solutions of standard discrete Lyapunov equations, which can be solved easily, but it has a linear convergent term. By a numerical example, it is shown that Algorithm I is superior to Algorithm II in the case of large dimension.

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Kagoshima Prefecture has been suffering from natural disasters by typhoons repeatedly. They hit power systems very badly and sometimes cut off electricity. To ensure the rapid restoration of electricity supply, one needs to predict the amount of damage by typhoon accurately. This paper proposes its prediction method by using the GA (Genetic Algorithm), a polynomial regression model, and NN (Neural Networks). The track of typhoon is evaluated from Gaussian function made by the GA. A predictor consists of the second-order polynomial regressor at the first stage and the NN at the second stage. This method enables us to predict the number of damaged distribution poles and lines from weather forecasts of typhoon. Effectiveness of the method is assured by applying it to the actual data.

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The present paper proposes a four wheel steering (4WS) controller for path tracking by using sliding mode control theory. It is based on the advantage that front and rear wheel steering can be decoupled at the reference points, where are defined as vehicle centers of percussion with respect to front and rear wheel. Simulations demonstrate the following characteristics : 1) the proposed 4WS vehicle follows the path more stablely and precisely than 2WS vehicle, 2) it has robust stability against the perturbation of cornering power, path radius and the cross-wind disturbance, and 3) steady state is not affected by the cross-wind disturbance.

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This paper considers local stability analysis of the systems with input saturation based on the LPV (linear parameter varying) descriptor representation which enables us to treat a wider class of systems and were originally proposed by Takaba. First, using the ordinary quadratic Lyapunov functions, we derive a less conservative stability condition compared to the former one by adding a new descriptor variable which is related to constancy of saturated inputs. Moreover, it is shown that this method can be easily extended to the case where piecewise quadratic Lyapunov functions are adopted. Finally we demonstrate its effectiveness through numerical examples.

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It is important to detect gas leakage sounds from pipes in petroleum refining plants and chemical plants, as often the gas used in these plants are flammable or poisonous. In order to detect the leakage accurately, we should select a feature extraction method for sounds properly. The purpose of this paper is to examine whether independent component analysis (ICA) is useful as a feature extraction method. Several experiments are performed in a plant using an artificial gas leakage device under various experimental conditions. A separating matrix that separates the independent components from collected leakage sounds and background noises is trained by an ICA algorithm. Through several simulations, we find that most basis functions acquired from this training are localized in frequency. Furthermore, there are remarkable differences in amplitude of some independent components between leakage sounds and background noises. From these results, we confirm that the feature extraction using the ICA algorithm is very useful for detecting gas leakage sounds.

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