Guaranteed Cost Tracking for Uncertain Coupled Multi-agent Systems Using Consensus over a Directed Graph

This paper considers the leader-follower control problem for a linear multi-agent system with directed communication topology and linear nonidentical uncertain coupling subject to integral quadratic constraints (IQCs). A consensus-type control protocol is proposed based on each agent's states relative to its neighbors and leader's state relative to agents which observe the leader. A sufficient condition is obtained by overbounding the cost function. Based on this sufficient condition, a computational algorithm is introduced to minimize the proposed guaranteed bound on tracking performance, which yields a suboptimal bound on the system consensus control and tracking performance. The effectiveness of the proposed method is demonstrated using a simulation example.Comment: Accepted for presentation at the 2013 Australian Control conferenc

A Distributed Asynchronous Method of Multipliers for Constrained Nonconvex Optimization

This paper presents a fully asynchronous and distributed approach for tackling optimization problems in which both the objective function and the constraints may be nonconvex. In the considered network setting each node is active upon triggering of a local timer and has access only to a portion of the objective function and to a subset of the constraints. In the proposed technique, based on the method of multipliers, each node performs, when it wakes up, either a descent step on a local augmented Lagrangian or an ascent step on the local multiplier vector. Nodes realize when to switch from the descent step to the ascent one through an asynchronous distributed logic-AND, which detects when all the nodes have reached a predefined tolerance in the minimization of the augmented Lagrangian. It is shown that the resulting distributed algorithm is equivalent to a block coordinate descent for the minimization of the global augmented Lagrangian. This allows one to extend the properties of the centralized method of multipliers to the considered distributed framework. Two application examples are presented to validate the proposed approach: a distributed source localization problem and the parameter estimation of a neural network.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0648

Fuzzy Distributed Cooperative Tracking For A Swarm Of Unmanned Aerial Vehicles With Heterogeneous Goals

Copyright © 2015 Taylor & Francis This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Systems Science on 29 December 2015, available online: http://www.tandfonline.com/10.1080/00207721.2015.1126380This article proposes a systematic analysis for a tracking problem which ensures cooperation amongst a swarm of UAVs, modelled as nonlinear systems with linear and angular velocity constraints, in order to achieve different goals. A distributed Takagi-Sugeno (TS) framework design is adopted for the representation of the nonlinear model of the dynamics of the UAVs. The distributed control law which is introduced is composed of both node and network level information. Firstly feedback gains are synthesised using a Parallel Distributed Compensation (PDC) control law structure, for a collection of isolated UAVs; ignoring communications among the swarm. Then secondly, based on an alternation-like procedure, the resulting feedback gains are used to determine Lyapunov matrices which are utilised at network level to incorporate into the control law the relative differences in the states of the vehicles, and to induce cooperative behaviour. Eventually stability is guaranteed for the entire swarm. The control synthesis is performed using tools from linear control theory: in particular the design criteria are posed as Linear Matrix Inequalities (LMIs). An example based on a UAV tracking scenario is included to outline the efficacy of the approach.Engineering and Physical Sciences Research Council (EPSRC

Asynchronous ADMM via a data exchange server

With advances in inter-processing-unit communication technology, distributed algorithms are becoming increasingly advantageous. This paper focuses on solving convex distributed optimisation problems with local consensus coupling constraints via the alternating direction method of multipliers (ADMM), by means of an asynchronous methodology allowing for communication delays. We use a bipartite undirected graph to denote the update structure of the processing agents that cooperatively perform the distributed algorithm without a centralised aggregator. We introduce a data server to exchange the asynchronous consensus data among the processing agents. Under certain technical assumptions that involve bounded delays, bounded step sizes, and strong convexities in parts of the local objectives, the running average of the local iterates generated by the proposed asynchronous algorithm converge to an optimal solution