Reference number : GC-2017-Q1-03574, amendment number : , agreement type : G, recipient legal name : Aghdam, Amir (Concordia University)|Aghdam, Amir (Université Concordia), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : There is an increasing interest in the deployment of multi-agent networks for emerging applications such as surveillance and target tracking. This proposal focuses on two research problems in the area of multi-agent networks. The first problem concerns network reconfiguration, which aims at improving the performance of a network by properly positioning its nodes and/or changing the weights of its links (e.g., by adjusting the communication/sensing power of the nodes). This is a problem which requires a strong practical background, and while the results will be developed for a general class of asymmetric networks, the applicant's prior experience in the design of experimentally validated distributed control schemes for underwater sensor networks puts him in a unique position to address some of the practical shortcomings of existing results for this type of systems. One of the main characteristics of asymmetric networks is that the graph representing them is directed, and in the case of underwater sensor networks, particularly, it is random too. However, there are not many results in the literature for the analysis of this type of graphs, due to their complexity compared to (deterministic) undirected graphs. This limits the extent of which certain observations in such networks can be justified analytically. For example, it is known that the relative positions of the acoustic nodes in an underwater sensor network can have a significant impact on data aggregation performance. In fact, similar observations have been reported in other types of asymmetric networks with no convincing theoretical justification. The applicant and his team have recently developed some theoretical results, validated by simulations, that relate the important properties of general weighted directed graphs (such as connectivity) to the configuration of the network, enabling the research community for the first time to justify these observations theoretically, and more importantly, use the results to further improve the performance of the network. These results will be used in the proposed research to find the optimal configuration for asymmetric networks. The results can also be used in other applications such as traffic network control systems, to justify some counter-intuitive observations reported in this type of systems (e.g., negative impact of the addition of some roads on the overall traffic flow in the network). The other problem investigated in this proposal is concerned with cooperative decision making for heading control of multiple vehicles, where it is desired to coordinate a group of vehicles in order to detect, localize, track and intercept a group of objects that arrive in a protected area (mission space) at random time instants. The proposed dynamic decision making and control design is based on a reward allocation strategy which directs the vehicles toward the objects in an optimal cooperative fashion., program purpose french : There is an increasing interest in the deployment of multi-agent networks for emerging applications such as surveillance and target tracking. This proposal focuses on two research problems in the area of multi-agent networks. The first problem concerns network reconfiguration, which aims at improving the performance of a network by properly positioning its nodes and/or changing the weights of its links (e.g., by adjusting the communication/sensing power of the nodes). This is a problem which requires a strong practical background, and while the results will be developed for a general class of asymmetric networks, the applicant's prior experience in the design of experimentally validated distributed control schemes for underwater sensor networks puts him in a unique position to address some of the practical shortcomings of existing results for this type of systems. One of the main characteristics of asymmetric networks is that the graph representing them is directed, and in the case of underwater sensor networks, particularly, it is random too. However, there are not many results in the literature for the analysis of this type of graphs, due to their complexity compared to (deterministic) undirected graphs. This limits the extent of which certain observations in such networks can be justified analytically. For example, it is known that the relative positions of the acoustic nodes in an underwater sensor network can have a significant impact on data aggregation performance. In fact, similar observations have been reported in other types of asymmetric networks with no convincing theoretical justification. The applicant and his team have recently developed some theoretical results, validated by simulations, that relate the important properties of general weighted directed graphs (such as connectivity) to the configuration of the network, enabling the research community for the first time to justify these observations theoretically, and more importantly, use the results to further improve the performance of the network. These results will be used in the proposed research to find the optimal configuration for asymmetric networks. The results can also be used in other applications such as traffic network control systems, to justify some counter-intuitive observations reported in this type of systems (e.g., negative impact of the addition of some roads on the overall traffic flow in the network). The other problem investigated in this proposal is concerned with cooperative decision making for heading control of multiple vehicles, where it is desired to coordinate a group of vehicles in order to detect, localize, track and intercept a group of objects that arrive in a protected area (mission space) at random time instants. The proposed dynamic decision making and control design is based on a reward allocation strategy which directs the vehicles toward the objects in an optimal cooperative fashion., agreement title english : Reconfiguration and Cooperative Control for Multi-Agent Networks, agreement title french : Reconfiguration and Cooperative Control for Multi-Agent Networks, agreement number : RGPIN, agreement value : 290000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada