1,315 research outputs found

    Event-Triggered Algorithms for Leader-Follower Consensus of Networked Euler-Lagrange Agents

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    This paper proposes three different distributed event-triggered control algorithms to achieve leader-follower consensus for a network of Euler-Lagrange agents. We firstly propose two model-independent algorithms for a subclass of Euler-Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work concerning event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information and bounds on the initial conditions. When the Euler-Lagrange agents have dynamics which include the vector of gravitational potential forces, an adaptive algorithm is proposed which requires more information about the agent dynamics but can estimate uncertain agent parameters. For each algorithm, a trigger function is proposed to govern the event update times. At each event, the controller is updated, which ensures that the control input is piecewise constant and saves energy resources. We analyse each controllers and trigger function and exclude Zeno behaviour. Extensive simulations show 1) the advantages of our proposed trigger function as compared to those in existing literature, and 2) the effectiveness of our proposed controllers.Comment: Extended manuscript of journal submission, containing omitted proofs and simulation

    Novel Lagrange sense exponential stability criteria for time-delayed stochastic Cohen–Grossberg neural networks with Markovian jump parameters: A graph-theoretic approach

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    This paper concerns the issues of exponential stability in Lagrange sense for a class of stochastic Cohen–Grossberg neural networks (SCGNNs) with Markovian jump and mixed time delay effects. A systematic approach of constructing a global Lyapunov function for SCGNNs with mixed time delays and Markovian jumping is provided by applying the association of Lyapunov method and graph theory results. Moreover, by using some inequality techniques in Lyapunov-type and coefficient-type theorems we attain two kinds of sufficient conditions to ensure the global exponential stability (GES) through Lagrange sense for the addressed SCGNNs. Ultimately, some examples with numerical simulations are given to demonstrate the effectiveness of the acquired result

    Stochastic collocation on unstructured multivariate meshes

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    Collocation has become a standard tool for approximation of parameterized systems in the uncertainty quantification (UQ) community. Techniques for least-squares regularization, compressive sampling recovery, and interpolatory reconstruction are becoming standard tools used in a variety of applications. Selection of a collocation mesh is frequently a challenge, but methods that construct geometrically "unstructured" collocation meshes have shown great potential due to attractive theoretical properties and direct, simple generation and implementation. We investigate properties of these meshes, presenting stability and accuracy results that can be used as guides for generating stochastic collocation grids in multiple dimensions.Comment: 29 pages, 6 figure

    Asynchronous Gossip for Averaging and Spectral Ranking

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    We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis.Comment: 14 pages, 7 figures. Minor revisio

    Energy conservation and dissipation properties of time-integration methods for the nonsmooth elastodynamics with contact

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    This research report is devoted to the study of the conservation and the dissipation properties of the mechanical energy of several time--integration methods dedicated to the elasto--dynamics with unilateral contact. Given that the direct application of the standard schemes as the Newmark schemes or the generalized--α\alpha schemes leads to energy blow-up, we study two schemes dedicated to the time--integration of nonsmooth systems with contact: the Moreau--Jean scheme and the nonsmooth generalized--α\alpha scheme. The energy conservation and dissipation properties of the Moreau--Jean is firstly shown. In a second step, the nonsmooth generalized--α\alpha scheme is studied by adapting the previous works of Krenk and H{\o}gsberg in the context of unilateral contact. Finally, the known properties of the Newmark and the Hilber--Hughes--Taylor (HHT) scheme in the unconstrained case are extended without any further assumptions to the case with contact.Comment: 29 page

    Energy conservation and dissipation properties of time-integration methods for the nonsmooth elastodynamics with contact

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    This research report is devoted to the study of the conservation and the dissipation properties of the mechanical energy of several time-integration methods dedicated to the elasto- dynamics with unilateral contact. Given that the direct application of the standard schemes as the Newmark schemes or the generalized-α schemes leads to energy blow-up, we study two schemes dedicated to the time-integration of nonsmooth systems with contact: the Moreau-Jean scheme and the nonsmooth generalized-α scheme. The energy conservation and dissipation properties of the Moreau-Jean is firstly shown. In a second step, the nonsmooth generalized-α scheme is studied by adapting the previous works of Krenk and Høgsberg in the context of unilateral contact. Finally, the known properties of the Newmark and the Hilber-Hughes-Taylor (HHT) scheme in the unconstrained case are extended without any further assumptions to the case with contact.Ce rapport de recherche propose une étude des propriétés de conservation et de dissipation de l'énergie mécanique pour différents schémas d'intégration en temps de la dynamique élastique avec du contact unilatéral. Sachant que l'application directe des schémas standards de type Newmark et des schémas α-généralisés conduisent à des explosions de l'énergie mécanique, on étudie deux schémas dédiés à l'intégration en temps des systèmes non réguliers avec contact : le schéma de Moreau-Jean et le schéma α-généralisé non-régulier. La conservation de l'énergie et les propriétés de dissipation du schéma de Moreau-Jean sont d'abord démontrées. Dans un second temps, le schéma α-généralisé non-régulier est étudié en adaptant les travaux précurseurs de Krenk et Høgsberg dans le contexte du contact unilatéral. Finalement, les propriétés connues du schéma de Newmark et du schéma Hilber-Hughes-Taylor (HHT) dans le cas régulier sont étendues dans le cas avec contact sans hypothèses supplémentaires
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