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In this article, the adaptive finite-time consensus problem is discussed for uncertain nonlinear multiagent systems. In contrast with the correlative literature, the systems permit multiple uncertainties (not only in control coefficients but also in inherent nonlinearities), and typically the consensus protocol is pursued in a fully distributed fashion (independent from any global information of network topology). This essentially challenges the realization of the finite-time consensus. To overcome the challenge, a new continuous fully distributed protocol is proposed by combining adaptive techniques such that the finite-time consensus of the systems under investigation is achieved. Remarkably, a dynamic high gain, rather than multiple ones in the related literature, is adequate to resist two kinds of uncertainties and to guarantee the fully distributed fashion of the consensus protocol. Moreover, the adaptive finite-time consensus protocol is specified on the scenario of leader-following multiagent systems. Simulation results of three interaction topologies are acquired to illustrate the validity and the wider applicability of the proposed protocol. [ABSTRACT FROM AUTHOR]

This article is devoted to explore the periodic event-triggered stabilization for continuous-time stochastic systems, and to develop analysis tools/methods for stochastic periodic event-triggered control. Notably, without real-time monitoring of system behavior as in the scenario with continuous event evaluation, it is critical to delicately estimate and govern the execution/sampling error to achieve the desired system performance. This, under stochastic effects, would be substantially challenging, since the behavior of the stochastic system is hard to predict and is disparate between trials even with the same initial conditions. In this article, a framework of global stabilization via periodic event-triggered output-feedback is established: 1) a criterion condition based on the ISS-Lyapunov function is presented for the feasibility of the desired event-triggered stabilization; 2) both the asymptotic stabilization and exponential stabilization are achieved for the systems, with delicately specifying the periodic event-triggering mechanism; and 3) the involved analysis, without applying the well-known Lyapunov theorems, can serve as a pattern from estimating sampling and execution errors to assess the closed-loop stability for stochastic periodic event-triggered control. Moreover, based on the established framework, we contribute the stabilizing controller design via periodic event-triggered output-feedback for a class of stochastic nonlinear systems. [ABSTRACT FROM AUTHOR]

This article is devoted to the optimal protocol for the leader-following consensus of second-order multiagent systems. Remarkably, the global consensus cost functional and the directed topology are pregiven, and the optimal protocol to be sought is distributed. This makes the Riccati-based strategies inapplicable, by which merely centralized protocol can be derived. As the main contribution of this article, an effective strategy of seeking a distributed optimal protocol is proposed for second-order agents over the digraph of the directed tree. Detailedly, the feasibility of distributed optimal protocol is first affirmed, that is, the existence of optimal gain parameters can be guaranteed. Then, by recursively deriving the completely explicit formulas of the consensus errors of relative position and velocity, an online implementable algorithm is developed to achieve the parameterization of the cost functional, that is, to obtain the explicit formula of the cost functional with respect to gain parameters of all agents. Lastly, the desired optimal gain parameters are obtained by minimizing the explicit formula. [ABSTRACT FROM AUTHOR]

This paper addresses the linear quadratic regulator optimal leader-following consensus for multiagent systems in a single-integrator form. Substantially different from the existing related works, the cost function, a global one, and the topology structure are both pregiven, and the optimal protocol to be sought is distributed (which merely depends on relative state information). This violates the optimal protocol design based on the algebraic Riccati equation, although a centralized protocol can be derived. To solve the problem, a novel design strategy of distributed optimal protocol is proposed for the multiagent systems over the digraph of a directed tree. Specifically, the dynamics of the consensus error is explicitly obtained, by which an online-implementable algorithm is given to achieve the parameterization of the cost function. Namely, the completely explicit formula with respect to the gain parameters of all agents is derived for the cost function. Based on this, the existence of optimal gain parameters is rigorously proven, which means the existence of the desired distributed optimal protocol. Furthermore, the optimal gain parameters are derived by minimizing the explicit formula. Two simulation examples are provided to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper focuses on the almost sure consensus via time-varying feedback for a class of uncertain stochastic nonlinear multiagent systems. A remarkable feature of the systems is that the nonlinearities allow serious unknowns and time-variations. Moreover, there exists no known bound for the unknown control coefficients. To handle the unknowns and time-variations, a time-varying consensus scheme is developed. The main idea of the scheme is to incorporate a delicate function of time into consensus protocols such that the unknowns and time-variations can be compensated as time increases. Based on the proposed protocols, the almost sure leaderless consensus and almost sure leader-following consensus are achieved. Finally, two simulation examples are provided to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

In the near future, plug-in electric vehicles (PEVs) will dominate the car market. The peak-valley difference would increase greatly due to the random nature of PEVs charging behaviors. In this paper, a new problem on studying the dynamic economic/emission dispatch (DEED) including PEVs for peak shaving and valley filling is proposed, and furthermore, the effect caused by different vehicle-to-grid (V2G) and grid-to-vehicle (G2V) loads on DEED is analyzed. The optimization model of the problem is constructed including several practical constraints, such as power flow constraints and ramp rate limits. The battery degradation cost is also included, which reshapes the objective function of DEED. Based on the model, a strategy for DEED including PEVs for peak shaving and valley filling is given, and a multiobjective optimization algorithm is adopted to solve the proposed problem. To demonstrate the feasibility and effectiveness of the proposed strategy, three DEED cases are considered under different V2G power. The analysis of the effect on fuel cost and emission caused by different V2G power shows that the proposed strategy can effectively reduce the emission and cut down the investment in peak load plants. [ABSTRACT FROM AUTHOR]

We present a hybrid metaheuristic optimization algorithm for solving economic dispatch problems in power systems. The proposed algorithm, based on bat algorithm, combines chaotic map and random black hole model together. Chaotic map is used to prevent premature convergence, and the random black hole model is helpful not only in avoiding premature convergence, but also in increasing the global search ability, enlarging exploitation area and accelerating convergence speed. The pseudocode and related parameters of the proposed algorithm are also given in this paper. Different from other related works, the costs of conventional thermal generators and random wind power are both included in the cost function because of the increasing penetration of wind power. The proposed algorithm has no requirement on the convexity or continuous differentiability of the cost function, although the effect on fuel cost, caused by the underestimation and overestimation of wind power, is included. This makes it feasible to take more practical nonlinear constraints into account, such as prohibited operating zones and ramp rate limits. Three test cases are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]