Cooperative learning in multi-agent systems from intermittent measurements

Motivated by the problem of tracking a direction in a decentralized way, we consider the general problem of cooperative learning in multi-agent systems with time-varying connectivity and intermittent measurements. We propose a distributed learning protocol capable of learning an unknown vector $\mu$ from noisy measurements made independently by autonomous nodes. Our protocol is completely distributed and able to cope with the time-varying, unpredictable, and noisy nature of inter-agent communication, and intermittent noisy measurements of $\mu$. Our main result bounds the learning speed of our protocol in terms of the size and combinatorial features of the (time-varying) networks connecting the nodes

Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms

In this paper, we present an asynchronous algorithm to estimate the unknown parameter under an unreliable network which allows new sensors to join and old sensors to leave, and can tolerate link failures. Each sensor has access to partially informative measurements when it is awakened. In addition, the proposed algorithm can avoid the interference among messages and effectively reduce the accumulated measurement and quantization errors. Based on the theory of stochastic approximation, we prove that our proposed algorithm almost surely converges to the unknown parameter. Finally, we present a numerical example to assess the performance and the communication cost of the algorithm.This work was supported in part by the National Natural Science Foundation of China under Grant 61503308 and Grant 61472331, in part by the Natural Science Foundation Project of Chongqing CSTC 2015jcyjA40043, and in part by Fundamental Research Funds for the Central Universities under Grant SWU114036. This publication was made possible by NPRP grant #4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation)

Adaptive Network Dynamics and Evolution of Leadership in Collective Migration

The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcations with respect to investment cost explains the observed hysteretic effect associated with recovery of migration in fragmented environments. Further, we show a minimum connectivity threshold above which there is evolutionary branching into leader and follower populations. For small populations, we show how the topology of the underlying social interaction network influences the emergence and location of leaders in the adaptive system. Our model and analysis can describe other adaptive network dynamics involving collective tracking or collective learning of a noisy, unknown signal, and likewise can inform the design of robotic networks where agents use decentralized strategies that balance direct environmental measurements with agent interactions.Comment: Submitted to Physica D: Nonlinear Phenomen

Modeling, Simulation and Decentralized Control of Islanded Microgrids

Modeling, Simulation and Decentralized Control of Islanded Microgrids by Farideh Doost Mohammadi This thesis develops a comprehensive modular state-space model of microgrids containing inverter-based Distributed Energy Resources (DERs). The model is validated and then used for small signal stability enhancement and voltage and frequency control. State space models of various microgrid elements are first derived, which allow for the inclusion of any possible elements such as current controlled inverters that are missing in the literature. Then a complete state space model is obtained to complement the models that are available in the literature and whose objectives are system analysis only as compared to the purpose of this work which is stability enhancement and control design. Specifically,;1. Small signal stability is enhanced by adding current controlled inverters to the microgrid. 2. Decentralized secondary frequency and voltage control techniques are proposed.;For secondary frequency control purposes, at first, the control strategies of different kinds of inverters and storage devices are described. Then, a novel solution is introduced for islanded microgrids by decomposing the system into virtual control areas.;For the secondary voltage control an Average Consensus Algorithm (ACA) is used and applied on a network of agents which has been chosen optimally based on the required connectivity. The main purpose of the ACA is to keep the average voltage of all the buses at a desired level during islanding. Then another control strategy is proposed to improve the voltage profile. While the average voltage is kept fixed by the voltage controlled inverters, this voltage profile smoothness is obtained by dedicating zones to current controlled inverters and defining their responsibilities based on the location of the loads

Cooperative learning in multi-agent systems from intermittent measurements

Motivated by the problem of decentralized direction-tracking, we consider the general problem of cooperative learning in multi-agent systems with time-varying connectivity and intermittent measurements. We propose a distributed learning protocol capable of learning an unknown vector μ from noisy measurements made independently by autonomous nodes. Our protocol is completely distributed and able to cope with the time-varying, unpredictable, and noisy nature of inter-agent communication, and intermittent noisy measurements of μ. Our main result bounds the learning speed of our protocol in terms of the size and combinatorial features of the (time-varying) network connecting the nodes. © 2013 IEEE