Consensusability of discrete-time multi-agent systems

The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact within an environment. The agents are considered to be autonomous entities. MAS can be used to solve problems that are difficult or impossible for an individual agent to solve. The main feature which is achieved when developing MAS, if they work, is flexibility, since MAS can be added to, modified and reconstructed, without the need for detailed rewriting of the application. MAS can manifest self-organization as well as self-steering related complex behaviors even when the individual strategies of all their agents are simple. The goal of MAS research is to find methods that allow us to build complex systems composed of autonomous agents who, while operating on local knowledge and possessing only limited abilities, are nonetheless capable of enacting the desired global behaviors. We want to know how to take a description of what a system of agents should do and break it down into individual agent behaviors. This thesis investigates the problem when discrete-time MAS are consensusable under undirected graph. A discussion is provided to show how the problem differs from continuous time system. Then a consensusability condition is derived in terms of the Mahler measure of the agent system for single input single out systems (SISO) and result shows that there is an improved consensusability by a power of two. An algorithm is proposed for distributed consensus feedback control law when the consensusability holds. Also the case of output feedback is considered in which the consensusability problem becomes more complicated. To solve this we decompose the problem into two parts i.e. state feedback and state estimation. Simulation results demonstrate the effectiveness of the established results

Cooperative Least Square Parameter Identification by Consensus within the Network of Autonomous Vehicles

authors' post-printIn this paper, a consensus framework for cooperative parameter estimation within the vehicular network is presented. It is assumed that each vehicle is equipped with a dedicated short range communication (DSRC) device and connected to other vehicles. The improvement achieved by the consensus for parameter estimation in presence of sensor’s noise is studied, and the effects of network nodes and edges on the consensus performance is discussed. Finally, the simulation results of the introduced cooperative estimation algorithm for estimation of the unknown parameter of road condition is presented. It is shown that due to the faster dynamic of network communication, single agents’ estimation converges to the least square approximation of the unknown parameter properly.Natural Sciences and Engineering Research Council || CRDPJ/431233-12 Nuvation Engineering, Waterloo, ON, Canad

Distributed Control Strategies for Microgrids: An Overview

There is an increasing interest and research effort focused on the analysis, design and implementation of distributed control systems for AC, DC and hybrid AC/DC microgrids. It is claimed that distributed controllers have several advantages over centralised control schemes, e.g., improved reliability, flexibility, controllability, black start operation, robustness to failure in the communication links, etc. In this work, an overview of the state-of-the-art of distributed cooperative control systems for isolated microgrids is presented. Protocols for cooperative control such as linear consensus, heterogeneous consensus and finite-time consensus are discussed and reviewed in this paper. Distributed cooperative algorithms for primary and secondary control systems, including (among others issues) virtual impedance, synthetic inertia, droop-free control, stability analysis, imbalance sharing, total harmonic distortion regulation, are also reviewed and discussed in this survey. Tertiary control systems, e.g., for economic dispatch of electric energy, based on cooperative control approaches, are also addressed in this work. This review also highlights existing issues, research challenges and future trends in distributed cooperative control of microgrids and their future applications