Joint Frequency Regulation and Economic Dispatch Using Limited Communication

We study the performance of a decentralized integral control scheme for joint power grid frequency regulation and economic dispatch. We show that by properly designing the controller gains, after a power flow perturbation, the control achieves near-optimal economic dispatch while recovering the nominal frequency, without requiring any communication. We quantify the gap between the controllable power generation cost under the decentralized control scheme and the optimal cost, based on the DC power flow model. Moreover, we study the tradeoff between the cost and the convergence time, by adjusting parameters of the control scheme. Communication between generators reduces the convergence time. We identify key communication links whose failures have more significant impacts on the performance of a distributed power grid control scheme that requires information exchange between neighbors

Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning

Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. While it is often difficult to directly define the behavior of the agents, simple communication protocols can be defined more easily using prior knowledge about the given task. In this paper, we propose a number of simple communication protocols that can be exploited by deep reinforcement learning to find decentralized control policies in a multi-robot swarm environment. The protocols are based on histograms that encode the local neighborhood relations of the agents and can also transmit task-specific information, such as the shortest distance and direction to a desired target. In our framework, we use an adaptation of Trust Region Policy Optimization to learn complex collaborative tasks, such as formation building and building a communication link. We evaluate our findings in a simulated 2D-physics environment, and compare the implications of different communication protocols.Comment: 13 pages, 4 figures, version 2, accepted at ANTS 201

Decentralized control with communication between controllers

The paper presents the problem of decentralized control with communication between controllers. It differs from the regularly considered decentralized control problem in that the controllers can communicate information. Major questions are then: What? When? and To Whom? to communicate information. The problem instances of decentralized control with communication between controllers for discrete-event systems and for finite-dimensional linear systems are described in detail

A cross layer multi hop network architecture for wireless Ad Hoc networks

In this paper, a novel decentralized cross-layer multi-hop cooperative network architecture is presented. Our architecture involves the design of a simple yet efficient cooperative flooding scheme,two decentralized opportunistic cooperative forwarding mechanisms as well as the design of Routing Enabled Cooperative Medium Access Control (RECOMAC) protocol that spans and incorporates the physical, medium access control (MAC) and routing layers for improving the performance of multihop communication. The proposed architecture exploits randomized coding at the physical layer to realize cooperative diversity. Randomized coding alleviates relay selection and actuation mechanisms,and therefore reduces the coordination among the relays. The coded packets are forwarded via opportunistically formed cooperative sets within a region, without communication among the relays and without establishing a prior route. In our architecture, routing layer functionality is submerged into the MAC layer to provide seamless cooperative communication while the messaging overhead to set up routes, select and actuate relays is minimized. RECOMAC is shown to provide dramatic performance improvements, such as eight times higher throughput and ten times lower end-to-end delay as well as reduced overhead, as compared to networks based on well-known IEEE 802.11 and Ad hoc On Demand Distance Vector (AODV) protocols