This paper proposes a game-theoretic approach to design a distributed real-time electricity pricing mechanism. Our approach is novel in that it guarantees individual rationality, meaning that it provides suppliers and consumers with a guaranteed incentive to participate in the real-time pricing market. Such an incentive is devised by offering a time-varying, situation-dependent subsidy that guarantees that a supplier/consumer profits by switching from a fixed-price contract to the real-time pricing contract. Although we assume that suppliers and consumers decide supply and demand quantities in a fully distributed manner to maximize their own profit, the proposed mechanism guarantees under moderate conditions that the market converges through an iterative process to a Nash equilibrium that maximizes social welfare. Furthermore, in order to guarantee safe operation of an electrical grid, our pricing mechanism increases stability of load frequency control, and at the same time, achieves supply-demand equilibrium by explicitly taking into account an equality constraint through dual decomposition method. We empirically demonstrate by simulations the individual rationality of the proposed mechanism as well as the convergence to supply-demand equilibrium.

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In this paper, a theoretical study on extended Kalman filter (EKF)-based mobile robot localization with intermittent measurements is examined by analysing the measurement innovation characteristics. Even if measurement data are unavailable and existence of uncertainties during mobile robot observations, it is suggested that the mobile robot can effectively estimate its location in an environment. This paper presents the uncertainties bounds of estimation by analysing the measurement innovation to preserve good estimations although some measurements data are sometimes missing. Theoretical analysis of the EKF is proposed to demonstrate the conditions when the problem occurred. From the analysis of measurement innovation, Jacobian transformation has been found as one of the main factors that affects the estimation performance. Besides that, the initial state covariance, process and measurement noises must be kept smaller to achieve better estimation results. The simulation and experimental results obtained are showing consistent behaviour as proposed in this paper.

Abstract-In this paper, we discuss a target-enclosing problem for a group of multiple nonholonomic agents in a plane. The proposed strategies guarantee that multiple agents' coordination finally results in a circular formation enclosing the targetobject which moves in the plane. Firstly, virtual agents for the feedback linearization of the real nonholonomic agents are introduced. Secondly, we propose the target-enclosing control laws based on the consensus algorithm to the virtual agents. Algebraic graph theory and consensus algorithm are employed to prove convergence and stability of the enclosing problem. Finally, experiments are provided to demonstrate the effectiveness of the proposed control laws.

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