Titlebook: Uncertainty in Complex Networked Systems; In Honor of Roberto Tamer Ba?ar Book 2018 Springer Nature Switzerland AG 2018 Uncertainty in sys

耐寒

Distributed Optimization in Multi-agent Networks Using One-bit of Relative State Informationi-agent networks. The striking feature of our distributed algorithms lies in the use of only the sign of relative state information between neighbors, which substantially differentiates our algorithms from others in the existing literature. Moreover, the algorithm does not require the interaction ma

玷污

Analysis of a Distributed Consensus Based Economic Dispatch Algorithmrs independently make adjustments to their power frequency primary controller set points using three pieces of information: (a) their own marginal cost of generation, (b) the measured frequency deviation, and (c) marginal generation cost of a subset of other generators obtained using local message e

雄辯

Impact of Quantized Inter-agent Communications on Game-Theoretic and Distributed Optimization Algoriansient behavior of such algorithms. This chapter uses the information-theoretic notion of . to establish universal bounds on the maximum exponential convergence rates of primal-dual and gradient-based Nash seeking algorithms under quantized communications. These bounds depend on the inter-agent dat

Fatten

是比賽

Fatten

Robust Static Output Feedback Design with Deterministic and Probabilistic Certificates no assumption on the uncertain system, and is based on the Scenario with Certificates (SwC) method which was recently proposed to address certain static anti-windup design problems. Numerical results illustrate the effectiveness of the approach in both deterministic and stochastic cases.

REIGN

Randomization in Robustness, Estimation, and Optimization and compare known deterministic methods with their stochastic counterparts such as random descent, various versions of Monte Carlo etc., for convex and global optimization. We survey some recent results in the field and ascertain that the situation can be very different.

大洪水

Distributed Optimization in Multi-agent Networks Using One-bit of Relative State Informationhe exact relative state information, the convergence speed is essentially not affected by the loss of information. We also extend our results to the cases of deterministically and randomly time-varying graphs. Finally, we validate the theoretical results by simulations.