Consensus analysis of multiagent networks via aggregated and pinning approaches

This is the post-print version of of the Article - Copyright @ 2011 IEEEIn this paper, the consensus problem of multiagent nonlinear directed networks (MNDNs) is discussed in the case that a MNDN does not have a spanning tree to reach the consensus of all nodes. By using the Lie algebra theory, a linear node-and-node pinning method is proposed to achieve a consensus of a MNDN for all nonlinear functions satisfying a given set of conditions. Based on some optimal algorithms, large-size networks are aggregated to small-size ones. Then, by applying the principle minor theory to the small-size networks, a sufficient condition is given to reduce the number of controlled nodes. Finally, simulation results are given to illustrate the effectiveness of the developed criteria.This work was jointly supported by CityU under a research grant (7002355) and GRF funding (CityU 101109)

Distributed Cooperative Regulation for Multiagent Systems and Its Applications to Power Systems: A Survey

Cooperative regulation of multiagent systems has become an active research area in the past decade. This paper reviews some recent progress in distributed coordination control for leader-following multiagent systems and its applications in power system and mainly focuses on the cooperative tracking control in terms of consensus tracking control and containment tracking control. Next, methods on how to rank the network nodes are summarized for undirected/directed network, based on which one can determine which follower should be connected to leaders such that partial followers can perceive leaders’ information. Furthermore, we present a survey of the most relevant scientific studies investigating the regulation and optimization problems in power systems based on distributed strategies. Finally, some potential applications in the frequency tracking regulation of smart grids are discussed at the end of the paper

Active-passive dynamic consensus filters: Theory and applications

”This dissertation presents a new method for distributively sensing dynamic environments utilizing integral action based system theoretic distributed information fusion methods. Specifically, the main contribution is a new class of dynamic consensus filters, termed active-passive dynamic consensus filters, in which agents are considered to be active, if they are able to sense an exogenous quantity of interest and are considered to be passive, otherwise, where the objective is to drive the states of all agents to the convex hull spanned by the exogenous inputs sensed by active agents. Additionally, we generalize these results to allow agents to locally set their value-of-information, characterizing an agents ability to sense a local quantity of interest, which may change with respect to time. The presented active-passive dynamic consensus filters utilize equations of motion in order to diffuse information across the network, requiring continuous information exchange and requiring agents to exchange their measurement and integral action states. Additionally, agents are assumed to be modeled as having single integrator dynamics. Motivated from this standpoint, we utilize the ideas and results from event-triggering control theory to develop a network of agents which only share their measurement state information as required based on errors exceeding a user-defined threshold. We also develop a static output-feedback controller which drives the outputs of a network of agents with general linear time-invariant dynamics to the average of a set of applied exogenous inputs. Finally, we also present a system state emulator based adaptive controller to guarantee that agents will reach a consensus even in the presence of input disturbances. For each proposed active-passive dynamic consensus filter, a rigorous analysis of the closed-loop system dynamics is performed to demonstrate stability. Finally, numerical examples and experimental studies are included to demonstrate the efficacy of the proposed information fusion filters”--Abstract, page iv

Eigenvalue Based Approach for Global Consensus in Multiagent Systems with Nonlinear Dynamics

This paper addresses the global consensus of nonlinear multiagent systems with asymmetrically coupled identical agents. By employing a Lyapunov function and graph theory, a sufficient condition is presented for the global exponential consensus of the multiagent system. The analytical result shows that, for a weakly connected communication graph, the algebraic connectivity of a redefined symmetric matrix associated with the directed graph is used to evaluate the global consensus of the multiagent system with nonlinear dynamics under the common linear consensus protocol. The presented condition is quite simple and easily verified, which can be effectively used to design consensus protocols of various weighted and directed communications. A numerical simulation is also given to show the effectiveness of the analytical result

Robust Distributed Stabilization of Interconnected Multiagent Systems

Many large-scale systems can be modeled as groups of individual dynamics, e.g., multi-vehicle systems, as well as interconnected multiagent systems, power systems and biological networks as a few examples. Due to the high-dimension and complexity in configuration of these infrastructures, only a few internal variables of each agent might be measurable and the exact knowledge of the model might be unavailable for the control design purpose. The collective objectives may range from consensus to decoupling, stabilization, reference tracking, and global performance guarantees. Depending on the objectives, the designer may choose agent-level low-dimension or multiagent system-level high-dimension approaches to develop distributed algorithms. With an inappropriately designed algorithm, the effect of modeling uncertainty may propagate over the communication and coupling topologies and degrade the overall performance of the system. We address this problem by proposing single- and multi-layer structures. The former is used for both individual and interconnected multiagent systems. The latter, inspired by cyber-physical systems, is devoted to the interconnected multiagent systems. We focus on developing a single control-theoretic tool to be used for the relative information-based distributed control design purpose for any combinations of the aforementioned configuration, objective, and approach. This systematic framework guarantees robust stability and performance of the closed-loop multiagent systems. We validate these theoretical results through various simulation studies

Behaviour monitoring: investigation of local and distributed approaches

Nowadays, the widespread availability of cheap and efficient unmanned systems (either aerial, ground or surface) has led to significant opportunities in the field of remote sensing and automated monitoring. On the one hand, the definition of efficient approaches to information collection, filtering and fusion has been the focus of extremely relevant research streams over the last decades. On the other hand, far less attention has been given to the problem of ‘interpreting’ the data, thus implementing inference processes able to, e.g., spot anomalies and possible threats in the monitored scenario. It is easy to understand how the automation of the ‘target assessment’ process could bring a great impact on monitoring applications since it would allow sensibly alleviating the analysis burden for human operators. To this end, the research project proposed in this thesis addresses the problem of behaviour assessment leading to the identification of targets that exhibit features “of interest”. Firstly, this thesis has addressed the problem of distributed target assessment based on behavioural and contextual features. The assessment problem is analysed making reference to a layered structure and a possible implementation approach for the middle-layer has been proposed. An extensive analysis of the ‘feature’ concept is provided, together with considerations about the target assessment process. A case study considering a road-traffic monitoring application is then introduced, suggesting a possible implementation for a set of features related to this particular scenario. The distributed approach has been implemented employing a consensus protocol, which allows achieving agreement about high-level, non-measurable, characteristics of the monitored vehicles. Two different techniques, ‘Belief’ and ‘Average’ consensus, for distributed target assessment based on features are finally presented, enabling the comparison of consensus effects when implemented at different level of the considered conceptual hierarchy. Then, the problem of identifying targets concerning features is tackled using a different approach: a probabilistic description is adopted for the target characteristics of interest and a hypothesis testing technique is applied to the feature probability density functions. Such approach is expected to allow discerning whether a given vehicle is a target of interest or not. The assessment process introduced is also able to account for information about the context of the vehicle, i.e. the environment where it moves or is operated. In so doing the target assessment process can be effectively adapted to the contour conditions. Results from simulations involving a road monitoring scenario are presented, considering both synthetic and real-world data. Lastly, the thesis addresses the problem of manoeuvre recognition and behaviour anomalies detection for generic targets through pattern matching techniques. This problem is analysed considering motor vehicles in a multi-lane road scenario. The proposed approach, however, can be easily extended to significantly different monitoring contexts. The overall proposed solution consists in a trajectory analysis tool, which classifies the target position over time into a sequence of ‘driving modes’, and a string-matching technique. This classification allows, as result of two different approaches, detecting both a priori defined patterns of interest and general behaviours standing out from those regularly exhibited from the monitored targets. Regarding the pattern matching process, two techniques are introduced and compared: a basic approach based on simple strings and a newly proposed method based on ‘regular expressions’. About reference patterns, a technique for the automatic definition of a dictionary of regular expressions matching the commonly observed target manoeuvres is presented. Its assessment results are then compared to those of a classic multi-layered neural network. In conclusion, this thesis proposes some novel approaches, both local and distributed, for the identification of the ‘targets of interest’ within a multi-target scenario. Such assessment is solely based on the behaviour actually exhibited by a target and does not involve any specific knowledge about the targets (analytic dynamic models, previous data, signatures of any type, etc.), being thus easily applicable to different scenarios and target types. For all the novel approaches described in the thesis, numerical results from simulations are reported: these results, in all the cases, confirm the effectiveness of the proposed techniques, even if they appear to be open to interpretation because of the inherent subjectivity of the assessment process

DATA REPLICATION IN DISTRIBUTED SYSTEMS USING OLYMPIAD OPTIMIZATION ALGORITHM

Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments