Consensus for switched networks with unknown but bounded disturbances

We consider stationary consensus protocols for networks of dynamic agents with switching topologies. The measure of the neighbors' state is affected by Unknown But Bounded disturbances. Here the main contribution is the formulation and solution of what we call the $\epsilon$-consensus problem, where the states are required to converge in a tube of ray $\epsilon$ asymptotically or in finite time.Comment: 18 pages, 3 figures. The manuscript has been submitted for the Special issue on Control and optimization in Cooperative Networks. Submitted to SIAM SICO

Teletraffic Engineering for Direct Load Control in Smart Grids

International audienceThe traditional paradigm for power grid operation is to continuously adapt energy production to demand. This paradigm is challenged by the increasing penetration of renewable sources, that are more variable and less predictable. An alternative approach is the direct load control of some inherently flexible electric loads to shape the demand. Direct control of deferrable loads presents analogies with flow admission control in telecommunication networks: a request for network resources (bandwidth or energy) can be delayed on the basis of the current network status in order to guarantee some performance metrics. In this paper we go beyond such an analogy, showing that usual teletraffic tools can be effectively used to control energy loads. In particular, we propose a family of control schemes which can be easily tuned to achieve the desired trade-off among resource usage, control overhead and privacy leakage

Large Scale Control of Deferrable Domestic Loads in Smart Grids

International audienceIn this paper, we investigate a realistic and low-cost deployment of large scale direct control of inelastic home appliances whose energy demand cannot be shaped, but simply deferred. The idea is to exploit 1) some simple actuators to be placed on the electric plugs for connecting or disconnecting appliances with heterogeneous control interfaces, including non-smart appliances, and 2) the Internet connections of customers for transporting the activation requests from the actuators to a centralized controller. Our solution requires no interaction with home users: in particular, it does not require them to express their energy demand in advance. A queuing theory model is derived to quantify how many users should adopt this solution in order to control a significant aggregated power load without significantly impairing their quality of service

Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

Facilitating navigation in pedestrian environments is critical for enabling people who are blind and visually impaired (BVI) to achieve independent mobility. A deep reinforcement learning (DRL)–based assistive guiding robot with ultrawide-bandwidth (UWB) beacons that can navigate through routes with designated waypoints was designed in this study. Typically, a simultaneous localization and mapping (SLAM) framework is used to estimate the robot pose and navigational goal; however, SLAM frameworks are vulnerable in certain dynamic environments. The proposed navigation method is a learning approach based on state-of-the-art DRL and can effectively avoid obstacles. When used with UWB beacons, the proposed strategy is suitable for environments with dynamic pedestrians. We also designed a handle device with an audio interface that enables BVI users to interact with the guiding robot through intuitive feedback. The UWB beacons were installed with an audio interface to obtain environmental information. The on-handle and on-beacon verbal feedback provides points of interests and turn-by-turn information to BVI users. BVI users were recruited in this study to conduct navigation tasks in different scenarios. A route was designed in a simulated ward to represent daily activities. In real-world situations, SLAM-based state estimation might be affected by dynamic obstacles, and the visual-based trail may suffer from occlusions from pedestrians or other obstacles. The proposed system successfully navigated through environments with dynamic pedestrians, in which systems based on existing SLAM algorithms have failed

Cooperative Inventory Control

A two-echelon one-warehouse multi-retailer inventory system is considered. Everyday, each retailer faces a stochastic demand and must choose whether to replenish or not from the warehouse. Decisions are made locally with the objective of minimizing individual ordering, storage/shortage and fixed transportation costs. The last are shared among all retailers who choose to replenish, thus motivating a certain coordination of replenishment strategies. Critical to coordination is communication. Hence, retailers are modeled as dynamic agents of a communication network. We show that each i retailer's optimal strategy is to order only in conjunction with at least other l i - 1 retailers, where the threshold l i is computed locally by the i -agent. In particular, we propose i) a decentralized Neuro-Dynamic Programming algorithm for the search of the sub-optimal thresholds; ii) a distributed consensus protocol for minimal information interchange between retailers thus to guarantee the best coordination of orders. We point out some analogies with the iterated dominance method to find noncooperative equilibria in the class of externality games

About the Stability of Active Queue Management Mechanism

Abstract — In this paper, we discuss the influence of multiple bottlenecks on the stability of Active Queue Management (AQM) controllers, usually configured on a single bottleneck basis. To see this, we consider a network scenario where RED is configured at each router according to previously developed control theoretic techniques. These configuration rules assure stability in a single bottleneck scenario. Yet, we show that instability may arise when two links become congested. We justify this result through a multiple bottleneck model. I