1,721,121 research outputs found
Erratum: Consensus for networks with unknown but bounded disturbances (SIAM Journal on Control and Optimization (2009) 48 (1756-1770) DOI: 10.1137/060678786)
No full textThis erratum corrects an error in the proof of Theorem 1 in [D. Bauso, L. Giarr'e, and R. Pesenti, SIAM J. Control Optim., 48 (2009), pp. 1756-1770]
Consensus for Networks with Unknown but Bounded Disturbances
No full textWe consider stationary consensus protocols for networks of dynamic agents. The measure of the neighbors' states is affected by unknown but bounded disturbances. Here the main contribution is the formulation and solution of what we call the -consensus problem, where the states are required to converge in a target set of radius asymptotically or in finite time. We introduce as a solution a dead-zone policy that we denote as the lazy rule
Non-linear protocols for optimal distributed consensus in networks of dynamic agents.
Full text linkWe consider stationary consensus protocols for networks of dynamic agents with fixed topologies. At each time instant, each agent knows only its and its neighbors’ state, but must reach consensus on a group decision value that is function of all the agents’ initial state. We show that the agents can reach consensus if the value of such a function is time-invariant when computed over the agents’ state trajectories. We use this basic result to introduce a non-linear protocol design rule allowing consensus on a quite general set of values. Such a set includes, e.g., any generalized mean of order p of the agents’ initial states. As a second contribution we show that our protocol design is the solution of individual optimizations performed by the agents. This notion suggests a game theoretic interpretation of consensus problems as mechanism design problems. Under this perspective a supervisor entails the agents to reach a consensus by imposing individual objectives. We prove that such objectives can be chosen so that rational agents have a unique optimal protocol, and asymptotically reach consensus on a desired group decision value. We use a Lyapunov approach to prove that the asymptotical consensus can be reached when the communication links between nearby agents define a time-invariant undirected network. Finally we perform a simulation study concerning the vertical alignment maneuver of a team of unmanned air vehicles
Optimal routing of customers with general independent interarrival times in deterministic parallel queues
No full textOptimization of Long-run Average-Flow Cost in Networks with time-varying unknown demand
Full text linkWe consider continuous-time robust network flows with capacity constraints and unknown but bounded time-varying demand. The problem of interest is to design a control strategy off-line with no knowledge of the demand realization. Such a control strategy regulates the flow on-line as a function of the realized demand
Average flow constraints and stabilizability in uncertain production-distribution systems
Full text linkWe consider a multi-inventory system with controlled flows and uncertain demands (disturbances) bounded within assigned compact sets. The system is modelled as a first-order one integrating the discrepancy between controlled flows and demands at different sites/nodes. Thus, the buffer levels at the nodes represent the system state. Given a long-term average demand, we are interested in a control strategy that satisfies just one of two requirements: (i) meeting any possible demand at each time (worst case stability) or (ii) achieving a predefined flow in the average (average flow constraints). Necessary and sufficient conditions for the achievement of both goals have been proposed by the authors. In this paper, we face the case in which these conditions are not satisfied. We show that, if we ignore the requirement on worst case stability, we can find a control strategy driving the expected value of the state to zero. On the contrary, if we ignore the average flow constraints, we can find a control strategy that satisfies worst case stability while optimizing any linear cost on the average control. In the latter case, we provide a tight bound for the cost
- …
