The constant development of sensing applications using innovative and affordable measurement devices has increased the amount of data transmitted through networks, carrying in many cases, redundant information that requires more time to be analyzed or larger storage centers. This redundancy is mainly present because the network nodes do not recognize environmental variations requiring exploration, which causes a repetitive data collection in a set of limited locations. In this work, we propose a multiagent learning framework that uses the Gaussian process regression (GPR) to allow the agents to predict the environmental behavior by means of the neighborhood measurements, and the rate distortion function to establish a border in which the environmental information is neither misunderstood nor redundant. We apply this framework to a mobile sensor network and demonstrate that the nodes can tune the parameter s of the Blahut-Arimoto algorithm in order to adjust the gathered environment information and to become more or less exploratory within a sensing area.

We tackle the problem of multiple quadrotors transporting a cable-suspended point-mass load. The quadrotors are treated as a virtual leader-follower algorithm, where a multi-layer graph encapsulates the communication and physical interaction. On the one hand, the communication stands for the approach of following the reference trajectory of a virtual leader. On the other hand, the load exerts a distributed tension force on each cable which is modeled as the well-known spring-damping system to each quadrotor establishing an interconnected dynamic. We assume cables are stretchable and have neglectable mass. Both objectives are accomplished through a Model Reference Adaptive Control approach with a robust modification that treats uncertainties and perturbations given by error in parameters, noise in the signal, and the wind drag forces. We prove stability based on Lyapunov approach and the results are shown through simulation.

We develop an event-triggered control strategy for a weighted-unbalanced directed homogeneous robot network to reach a dynamic consensus in this work. We present some guarantees for synchronizing a robot network when all robots have access to the reference and when a limited number of robots have access. The proposed event-triggered control can reduce and avoid the periodic updating of the signals. Unlike some current control methods, we prove stability by making use of a logarithmic norm, which extends the possibilities of the control law to be applied to a wide range of directed graphs, in contrast to other works where the event-triggered control can be only implemented over strongly connected and weight-balanced digraphs. We test the performance of our algorithm by carrying out experiments both in simulation and in a real team of robots.

Demand response systems assume an electricity retail-market with strategic electricity consuming agents. The goal in these systems is to design load shaping mechanisms to achieve efficiency of resources and customer satisfaction. Recent research efforts have studied the impact of integrity attacks in simplified versions of the demand response problem, where neither the load consuming agents nor the adversary are strategic.

In the path to the future implementation of the smart grid, microgrids are presented as a cornerstone. An efficient and optimal microgrid operation is paramount. In this paper, we present a hierarchical microgrid management system using task sharing and an evolutionary game theory based dispatch strategy as a coordination algorithm to integrate the three main control levels needed for microgrid operation, which can be considered as a distributed intelligent system. The proposed algorithm shares the total load demanded into the distributed generators efficiently, while the microgrid utility is maximized dynamically, increasing the system reliability. Some simulation results are presented to illustrate the effectiveness of the dynamic performance of the proposed approach compared with a traditional hierarchical algorithm.

A power electronics benchmark problem involving several practical scenarios for a fixed-frequency synchronous step-down DC-DC converter is investigated. A new weighted control technique that uses the replicator dynamics concepts to weight the operation of different controllers tuned to operate in different modes, and with different control objectives is presented. Simulations for four critical process conditions are shown to illustrate the performance of this novel technique.