Order Multi-agent Systems Research Articles

A Distributed Explicit Reference Governor for Constrained Control of Multiple UAVs

This paper develops a distributed strategy to control multiple UAVs in a confined environment. By pre-stabilizing each agent and using the applied reference as an auxiliary state variable, it is shown that the transient dynamics can be limited to a suitable invariant level-set. Coordination between agents is then addressed using existing tools for first order multi-agent system. The resulting control law is simple, computationally inexpensive and scalable. Moreover, it can easily take into account saturations on the available thrust as well as constraints on the agents’ velocity. The effectiveness of the proposed algorithm is presented with several simulation results showing its robustness with respect to external disturbances.

Projection-Based Model Reduction of Multi-Agent Systems Using Graph Partitions

In this paper, we establish a projection-based model reduction method for multiagent systems defined on a graph. Reduced order models are obtained by clustering the vertices (agents) of the underlying communication graph by means of suitable graph partitions. In the reduction process, the spatial structure of the network is preserved and the reduced order models can again be realized as multiagent systems defined on a graph. The agents are assumed to have single-integrator dynamics and the communication graph of the original system is weighted and undirected. The proposed model reduction technique reduces the number of vertices of the graph (which is equal to the dynamic order of the original multi-agent system) and yields a reduced order multiagent system defined on a new graph with a reduced number of vertices. This new graph is a weighted symmetric directed graph. It is shown that if the original multiagent system reaches consensus, then so does the reduced order model. For the case that the clusters are chosen using an almost equitable partition (AEP) of the graph, we obtain an explicit formula for the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm of the error system obtained by comparing the input-output behaviors of the original model and the reduced order model. We also prove that the error obtained by taking an arbitrary partition of the graph is bounded from below by the error obtained using the largest AEP finer than the given partition. The proposed results are illustrated by means of a running example.

Distributed ${\cal H}_{\infty}$ Consensus of Higher Order Multiagent Systems With Switching Topologies

This brief addresses the distributed ${\cal H}_{\infty}$ consensus problem of multiagent systems with higher order linear dynamics and switching directed topologies. Without assuming that the directed communication topology is fixed or balanced, a class of distributed protocols is constructed and employed to achieve state consensus while guaranteeing a prescribed disturbance rejection objective, with both admissible exogenous disturbances and unknown initial states. By using tools from algebraic graph theory and switched systems theory, it is proven that the distributed ${\cal H}_{\infty}$ consensus in closed-loop multiagent systems with switching directed topologies can be achieved if the feedback gain matrix of the protocol is appropriately designed and the coupling strength among neighboring agents is larger than a derived positive value. Moreover, the consensus rate for the closed-loop nominal multi-agent systems is discussed.

Multi-Agent Recognition System based on Object Based Image Analysis Using WorldView-2

In this paper, using spatial and spectral characteristics of the WorldView-2 satellite imagery, capabilities of multi-agent systems are used for solving multiple object recognition difficulties in complex urban areas. The methodology has two main steps: object based image analysis (OBIA) and multiagent object recognition. In the first step, segmentation and multi-process object classification based on spectral, textural, and structural features are performed. Classified regions are used as an input dataset in the multi-agent system in order to modify object recognition results. According to the results from the object based image analysis process, using contextual relations and structural features, the overall accuracy and Kappa improved by 17.79 percent and 0.253, respectively. Using knowledge-based reasoning and cooperative capabilities of agents in the multi-agent system in this paper, most of the remaining difficulties are decreased and values 90.95 percent and 0.876 are obtained for the overall accuracy and Kappa, respectively, of the object recognition results.

Distributed coordination of fractional order multi-agent systems with communication delays

Abstract Because of the complexity of the practical environments, many distributed multi-agent systems can not be illustrated with the integer-order dynamics and can only be described with the fractional-order dynamics. Under the connected network with directed weighted topologies, the dynamical characteristics of agents with fractional-order derivative operator is analyzed in this paper. Applying the Laplace transform and frequency domain theory of the fractional-order operator, the distributed coordination of fractional-order multi-agent systems (FOMAS) with communication delays is studied, and a critical value of time delay is obtained to ensure the consensus of FOMAS. Since the integer-order model is a special case of fractional-order model, the extended results in this paper are in accordance with that of the integer-order model. Finally, numerical examples are provided to verify our results.

Multiconsensus of second order multiagent systems with directed topologies

A novel multiconsensus problem is introduced in multiagent systems. The states of multiple agents in each subnetwork asymptotically converge to a consistent value with information exchanges among subnetworks. The multiconsensus problem of second order multiagent systems with directed topologies is studied. Three linear protocols are proposed to solve the multiconsensus problem. Necessary and sufficient conditions are derived based on matrix theory. Simulations are provided to demonstrate the effectiveness of the theoretical results.

Finite-time consensus protocols for networks of dynamic agents by terminal iterative learning

This paper aims to address finite-time consensus problems for multi-agent systems under the iterative learning control framework. Distributed iterative learning protocols are presented, which adopt the terminal laws to update the control input and are offline feedforward design approaches. It is shown that iterative learning protocols can guarantee all agents in a directed graph to reach the finite-time consensus. Furthermore, the multi-agent systems can be enabled to achieve a finite-time consensus at any desired terminal state/output if iterative learning protocols can be improved by introducing the desired terminal state/output to a portion of agents. Simulation results show that iterative learning protocols can effectively accomplish finite-time consensus objectives for both first-order and higher order multi-agent systems.

Object oriented image analysis based on multi-agent recognition system

In this paper, the capabilities of multi-agent systems are used in order to solve object recognition difficulties in complex urban areas based on the characteristics of WorldView-2 satellite imagery and digital surface model (DSM). The proposed methodology has three main steps: pre-processing of dataset, object based image analysis and multi-agent object recognition. Classified regions obtained from object based image analysis are used as input datasets in the proposed multi-agent system in order to modify/improve results. In the first operational level of the proposed multi-agent system, various kinds of object recognition agents modify initial classified regions based on their spectral, textural and 3D structural knowledge. Then, in the second operational level, 2D structural knowledge and contextual relations are used by agents for reasoning and modification. Evaluation of the capabilities of the proposed object recognition methodology is performed on WorldView-2 imagery over Rio de Janeiro (Brazil) which has been collected in January 2010. According to the obtained results of the object based image analysis process, contextual relations and structural descriptors have high potential to modify general difficulties of object recognition. Using knowledge based reasoning and cooperative capabilities of agents in the proposed multi-agent system in this paper, most of the remaining difficulties are decreased and the accuracy of object based image analysis results is improved for about three percent.

Distributed output regulation of switching multi‐agent systems subject to input saturation

In this study, we consider the distributed output regulation (DOR) problem of linear multi-agent systems subject to input saturation with switching topology. Owing to the input saturation elements, the considered systems is non-linear. It is natural to take the semiglobal frame for it which allows one to use distributed linear feedback controller. The basic problem is to design distributed feedback controller for the considered multi-agent systems in order to have all agents to track an active leader and/or distributed rejection with disturbance signals. Both the leader and the disturbance signal are modelled as the exogenous system with different dynamics and unmeasurable variables. A systematic distributed linear design approach based on the solvability condition is proposed for the considered DOR problem.

Adaptive Consensus of High Order Multi-Agent Systems with Unknown Nonlinear Dynamics

In this paper, high order consensus problem of multi-agent systems with non-identical unknown nonlinear dynamics following an unknown and nonlinear leader is investigated in networks with fixed and switching topologies. By parameterizations of unknown nonlinear dynamics of agents in the system, neighbor-based adaptive consensus algorithms are proposed by incorporating consensus errors in addition to relative position feedback. Base on algebraic graph theory, Lyapunov theory, Riccati inequalities and PE condition, analysis of stability and parameter convergence of the proposed algorithm are conducted. Finally, a simulation in switching networks is worked out to illustrate the effectiveness of the theoretical results.

Leader-Following Consensus of Linear Multiagent Systems with State Observer under Switching Topologies

The leader-following consensus problem of higher order multiagent systems is considered. The dynamics of each agent are given in general form of linear system, and the communication topology among the agents is assumed to be directed and switching. To track the leader, two kinds of distributed observer-based consensus protocols are proposed for each following agent, whose distributed observers are used to estimate the leader’s state and tracking error based on the relative outputs of the neighboring agents, respectively. Some sufficient consensus conditions are established by using parameter-dependent Lyapunov function method under a class of directed interaction topologies. As special cases, the consensus conditions for balanced and undirected interconnection topology cases can be obtained directly. The protocol design technique is based on algebraic graph theory, Riccati equation, and Sylvester equation. Finally, a simulation example is given to illustrate our obtained result.

Consensus of Multi-agent Systems with Time Delays and Measurement Noises

In this paper, the leader-following consensus problem of high order multi-agent systems with time-varying communication delays and noises is considered. The interconnection topology among the agents is assumed to be switching and undirected. The dynamics of each agent and the leader is a linear system. To track the active leader, a neighbor-based control of each following agent is designed in using local information. The control design technique is based on algebraic graph theory, Riccati inequality and Lyapunov inequality. By using Lyapunov-Razumikhim and stochastic analysis methods, we prove that the closed loop tracking control system is stochastically stable in mean square and the estimation errors converge to zero in mean square as well. Finally, a numerical example is given to illustrate the obtained result. DOI: http://dx.doi.org/10.11591/telkomnika.v10i6.1429 Full Text: PDF

GENERALISATION OF SUBMARINE FEATURES ON NAUTICAL CHARTS

Abstract. On most large scale and middle scale maps, relief is represented by contours and spot heights. In order to adapt the representation to the scale, the terrain is generalised either by smoothing or filtering the terrain model or by simplifying the contours. However this approach is not applicable to nautical chart construction where terrain features are selected according to their importance for navigation. This paper presents an approach for the consideration of feature attributes in the generalisation of a set of contours with respect to nautical chart constraints. Features are defined by sets of contours and a set of generalisation operators applied to features is presented. The definitions are introduced in a multi-agent system in order to perform automatic generalisation of a contour set. Results are discussed on a case study and directions for future work are presented.

Mean Square Consensus Control for Second Order Multi-Agent Systems under Fixed Topologies and Measurement Noises

This paper considers the mean square consensus problems for second order multi-agent systems with fixed topologies and measurement noises. Two cases were analyzed: 1) undirected networks with fixed topologies; 2) undirected networks with fixed topologies and measurement noises. In order to attenuate the measurement noises, a time-varying consensus gain was introduced in the consensus protocol. Sufficient conditions were derived for all agents to reach consensus in mean square via algebraic graph theory, matrix theory and stochastic analysis approach, by constructing an appropriate Lyapunov function. A numerical example was given to verify our theoretical analysis.

Une logique modale pour raisonner sur la cohérence et la complétude de réglementations

In this paper, we deal with regulations that may exist in multiagent systems in order to regulate agent behaviour. More precisely, we discuss two properties of regulations, consistency and completeness. After defining what consistency and completeness mean, we propose a way to consistently complete incomplete regulations. This contribution considers that regulations are expressed in a first order deontic logic.

Leader-following consensus of multi-agent systems under fixed and switching topologies

The leader-following consensus problem of higher order multi-agent systems is considered. In the system, the dynamics of each agent and the leader is a linear system. The control of each agent using local information is designed and detailed analysis of the leader-following consensus is presented for both fixed and switching interaction topologies, which describe the information exchange between the multi-agent systems. The design technique is based on algebraic graph theory, Riccati inequality and Lyapunov inequality. Simulations indicate the capabilities of the algorithms.

Formal approaches to multi-agent sysems

In recent years, multi-agent systems have come to form one of the key technologies for software development. The Formal Approaches to Multiagent Systems (FAMAS) workshop series brings together researchers from the fields of logic, theoretical computer science and multi-agent systems in order to discuss formal techniques for specifying and verifying multiagent systems, including many subtle and not easy to formalize aspects of agency. FAMAS addresses the issues of logics for multiagent systems, formal methods for verification, e.g. model checking, and formal approaches to cooperation, multi-agent planning, communication, coordination, negotiation, games, and reasoning under uncertainty in a distributed environment. The first workshop in the FAMAS series, FAMAS’03, took place in Warsaw in April 2003 as a satellite event of the European Conference on Theory and Practice of Software (ETAPS’03). A selection of contributed and invited papers was published in Fundamenta Informaticae as volume 63, issue 2,3 of 2004. The second FAMAS workshop, FAMAS’06, took place in August 2006 at the Riva del Garda, in conjunction with the European Conference on Artificial Intelligence (ECAI’06). The best contributions resulted in the current special issue of the Journal of Autonomous Agents and Multiagent Systems. FAMAS’07 was one of the agent workshops gathered together under the umbrella of Multiagent Logics, Languages and Organizations—FederatedWorkshops (MALLOW’007), taking place in September 2007 in Durham. Finally, FAMAS’09 will take place in Turin as a part of MALLOW’09.

Design considerations for the implementation of multi-agent systems in the dairy industry

The objectives of this research were: (a) to perform a survey of current research in the area of multi-agent systems in order to learn more about how they could be designed and implemented; (b) to investigate the feasibility of such an approach for agriculture, based on an integration of currently existing technologies; and, more specifically (c) to assess the potential of a multi-agent approach in the context of decision support for dairy production. The results of this work highlighted a number of key concepts in multi-agent system design, including the importance of selecting an appropriate system architecture for agent coordination (e.g., peer-to-peer, federated, or blackboard-based) and the need for well-defined agent communication methods (language and ontology). Alternative technologies, used in the implementation of multi-agent systems (e.g., communication protocols and distributed computing methods), were also explored. Lastly, a case study was carried out, in which some of the discussed technologies were tested and implemented to create a multi-agent heifer management system. The system consists of two different types of agents and several databases, and was implemented on a PC-based network. The agents work together to synthesize data about heifer development from different sources and to present this to the user in a graphical format. The system demonstrates the feasibility of applying an agent-based approach, using currently available technology, to problems such as dairy herd management in which a distributed decision-support solution is often required. It is concluded that the constraints for the implementation of multi-agent systems do not appear to be of a technological nature; the challenge seems to be more one of defining and accepting a common ontology and communication language by members of a given industry. In addition, large-scale distributed systems will require sophisticated agent-coordination methods to ensure robust and efficient operation.