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A decentralized estimation-decision strategy is derived for a team of electronic combat air vehicles (ECAVs) deceiving a network of radars. For the deception, a phantom target track is cooperatively generated by each ECAV applying range delay on the individual radar pulses. To continuously obtain a feasible phantom track, the team must tightly coordinate the phantom’s trajectory so as not to violate any of the system constraints. The coordination is performed by a decentralized decision process for which each ECAV periodically transmits its constraints on feasible tracks. To perform the decision process with minimal communication between the ECAVs, a decentralized estimation algorithm is proposed where each ECAV continuously estimates the states of its teammates and their respective radar position based on their individual transmitted constraints. Thus, all the information obtained in the individual messages is extracted and group coordination is obtained. Moreover, if there are gaps in communication the team coordination can be maintained. Simulation results confirm the viability of the proposed decentralized estimation-decision team strategy.

It is known in the literature on Automated Highway Systems that information flow can significantly affect the propagation of errors in spacing in a collection of vehicles. This chapter investigates this issue further for a homogeneous collection of vehicles, where in the motion of each vehicle is modeled as a point mass and is digitally controlled. The structure of the controller employed by the vehicles is as follows: , where () is the (z- transformation of) control action for the vehicle, is the position of the vehicle, is the desired distance between the and the vehicles in the collection, () is the discrete transfer function of the controller and is the set of vehicles that the vehicle can communicate with directly. This chapter further assumes that the information flow is undirected, i.e., ∈ ⇔ ∈ and the information flow graph is . We consider information flow in the collection, where each vehicle can communicate with a maximum of () vehicles. We allow () to vary with the size of the collection. We first show that () cannot have any zeroes at = 1 to ensure that relative spacing is maintained in response to a reference vehicle making a maneuver where its velocity experiences a steady state offset. We then show that if the control transfer function () has one or more poles located at = 1, then the motion of the collection of vehicles will become unstable if the size of the collection is sufficiently large. These two results imply that (1) ≠ 0 and (1) must be well defined. We further show that if ()/ → 0 as → ∞ then there is a low frequency sinusoidal disturbance of at most unit amplitude acting on each vehicle such that the maximum error in spacing response increase at least as . A consequence of the results presented in this chapter is that the maximum of the error in spacing and velocity of any vehicle can be made insensitive to the size of the collection only if there is at least one vehicle in the collection that communicates with at least () other vehicles in the collection. We also show that there can be at most one vehicle that communicates with () vehicles and that any other vehicle in the collection can only communicate with at most vehicles, where depends only on the chosen controller and the its sampling time.

In this chapter, formation control of mobile robots with nonlinear models is considered. Two controllers are proposed with the aid of the dynamic feedback linearization technique, the time-scaling technique and properties of Laplacian matrix. The proposed controllers ensure the group of mobile robots to move in a desired formation. Existing results in formation control using graph theoretical methods are extended to nonlinear systems of high dimensions. Simulation results show the effectiveness of the proposed controllers.

In this chapter, we compare two cooperative control algorithms for multiple Unmanned Aerial Vehicles (UAVs) to search, detect, and locate multiple mobile RF (Radio Frequency) emitting ground targets. We assume the UAVs are equipped with low-precision RF direction finding sensors with no ranging capability and the targets may emit signals randomly with variable duration. In the first algorithm the UAVs search a large area cooperatively until a target is detected. Once a target is detected, each UAV uses a cost function to determine whether to continue searching to minimize overall search time or to cooperate in localization of the target, joining in a proper orbit for precise triangulation to increase localization accuracy. In the second algorithm the UAVs fly in formations of three for both search and target localization. The first algorithm minimizes the total search time, while the second algorithm minimizes the time to localize targets after detection. Both algorithms combine a set of intentional cooperative rules with individual UAV behaviors optimizing a performance criterion to search a large area. This chapter will compare the total search time and localization accuracy generated by multiple UAVs using the two algorithms simulations as we vary ratios of the numbers of UAVs to the number of targets.