Coordination Control For Multi-Agent Systems And Its Applications In Social Network And Privacy Preserving

Multi-agent systems have a wide range of applications in areas such as intelligent transportation,smart grid,aerospace,robotics and wireless sensor networks,etc.Coordination control enables agent states to aggregate a common value at the collective level,thereby completing tasks that cannot be handled by a single agent.This thesis,incorporating with the state-of-the-art results on coordination of multi-agent systems,is dedicated to investigating the cooperative control problems of multi-agent systems in the presence of both antagonistic and cooperative information.The deduced results are further extended to quantify the opinion dynamics in social network and the privacy preserving within the framework of multi-agent systems.The main contents of this thesis are listed below:First,a coordination algorithm for the first-order multi-agent systems in the presence of antagonistic information is proposed,where the scaling parameter quantifies whether the agent is antagonistic or not,and the weighted gain assures the convergence of the underlying system.The existence of the weighted gain is proved,and a criterion is derived,which guaranteeing that all eigenvalues of system matrix are contained in the unit disk,except for a simple 1 eigenvalue.To avoid the global information,a linear transformation is employed to establish the relationship between the error system and the original system.A coordination condition of the agents subject to antagonistic information is obtained.The derived results are further extended to the scenarios of the leader-follower and the changing interaction topology.Furthermore,the relationship between the above algorithm and Altafini model is elaborated in details.The coordination problem for second-order multi-agent systems without velocity information is then discussed.For general directed graph,it is shown that the underlying coordination problem is tightly linked to communication topology and the weighted gains.To further reduce the communication burden,the coordination protocols using event-triggered and quantization-triggered mechanisms are designed.To circumvent the global information,node-based and edge-based coordination algorithms are developed with the corresponding coordination criteria.Therefore,it generalizes the collaborative control for second-order multi-agent systems under the fixed communication topology.For second-order multi-agent systems,we study the coordination issue with antagonistic interaction.Since the scaling parameters for speed and position are usually not correlated,the cooperative control for second-order multi-agent systems under hostile information is more challenging comparing with the first-order case.We prove the existence of the weighted gain,and give the criterion judging whether there are merely two zero eigenvalues and the nonzero eigenvalues have negative real parts.The proposed setup is further extended to two cases,and the corresponding coordination criteria are presented.For general linear multi-agent systems,the coordination problem in the presence of antagonistic information is suggested to give the coordination condition for the interacting agents.Additionally,the explicit expression of the final aggregated value for each interacting agent is formulated.By using output information of the agents,the distributed observer for the coordination of the participating agents is devised,while guaranteeing the observer error approaches to zero eventually.The underlying coordination region is analyzed in details.Considering the constraint of input,we focus on the semi-global coordination problem via low gain feedback for multi-agent systems subject to both antagonistic information and input saturation.The opinion dynamics of social networks is studied which naturally expands the setup of multi-agent systems with antagonistic interactions.The primary idea is to bring in an appraisal network that quantifies the level of cognitive orientation on the opinions of remaining individuals(i.e.,antagonistic or trust),while the interacting network characterizes the interaction mechanism of the participating individuals in social networks.It is found that cooperative appraisal network leads to consensus in opinions,while antagonistic appraisal network results in clusters in opinions.With the help of random convex optimization,the lower bound on the number of the samples used to estimate the underlying appraisal network with priori constraints is explicitly given.The obtained result is extended to the case of multi-issue interdependence,and the criteria on consensus,clusters and stability of the interacting individuals are presented.Finally,the privacy preserving problem of multi-agent systems is addressed.With the aid of minimum observable subspace,it is proved that adding appropriate amount of noise can achieve privacy preserving of system's initial value.Based on this argument,the node-based privacy preserving mechanism is put forward and it is pointed out that privacy preserving problem is solvable provided that no less than half of the sensor nodes are blurred by random noise.Furthermore,an edge-based privacy preserving mechanism is given to indicate that privacy preserving is ensured as long as certain conditions are fulfilled,even if less than half of the sensor nodes are disturbed by random noise.This reveals the compromise between the system privacy protection level and the system complexity to a certain extent.