In this paper, we present a decentralized multitarget tracking system for cooperative unmanned aerial vehicles (UAVs) with limited sensing capabilities. The proposed system distributively incorporates a clustering algorithm, an optimal sensor manager, and an optimal path planner to track multiple mobile targets. A set of extended Kalman filters is used by each UAV to estimate the locations of mobile targets. The UAVs are equipped with gimbaled cameras with a limited field of view and sensing range. To make the system scalable, the density-based spatial clustering of applications with noise algorithm is used to group targets, sensor managers determine optimal gimbal poses and path planners collectively coordinate UAVs' movements. The effective performance of the proposed system is shown through simulation results.

Applying the selective harmonic elimination (SHE) technique to grid-connected-cascaded modular multilevel inverters has been widely discussed in the literature. However, due to the difficulties of solving high-order nonlinear transcendental equations, the SHE technique cannot be implemented in real time so its applications are limited. This paper presents a technique to convert the nonlinear equations to a specific control system and use a look-up table, integral controllers and a decoupling controller to help the system converge to a zero-error steady state. An inner instantaneous observer is introduced to directly extract the harmonic spectrum of the output voltages without applying FFT algorithm to output step voltage waveforms. It can not only simplify software design but also eliminate the phase delay generated by conventional spectrum extracting algorithm. The proposed technique can achieve an accurate switching angle control and a fast response. Simulation and experimental results verified that the system can achieve zero-error steady state within one line period.

In this paper, a novel decentralized task allocation algorithm based on the Hungarian approach is proposed. The proposed algorithm guarantees an optimal solution as long as the agent network is connected, i.e., the second smallest eigenvalue of the Laplacian matrix of the agent graph is greater than zero. In order to show the motivation of the proposed algorithm, the original centralized auction and Hungarian algorithms are compared in terms of the converging speed versus the number of agents. The result shows the superiority of the Hungarian algorithm in scalability over the auction algorithm. Then, the performance of the proposed decentralized Hungarian-Based algorithm (DHBA) is compared with the consensus-based auction algorithm (CBAA) under different situations, including different number of agents and different network topologies. The simulation results show that DHBA outperforms CBAA in all cases on the basis of the converging speed, the optimality of assignments, and the computational time.

In this brief, we present a novel decentralized controller for multiple unmanned aerial vehicles (UAVs) to fly in a symmetric formation surrounding a hostile mobile target while maintaining and controlling the network connectivity. The multi-UAV information exchange topology is considered as a time-varying function of interagent distances. We investigate the impact of controller parameters on UAV group behavior to maintain connectivity. We also explore the use of the proposed controller to track a desired connectivity profile along with steering UAVs in a target-centric formation. Simulation results validate the effectiveness of the proposed controller.

In this paper, we present a novel approach, namely Distributed Matching-by-Clone Hungarian-Based Algorithm (DMCHBA), to multi-agent task-allocation problems, in which the number of agents is smaller than the number of tasks. The proposed DMCHBA assumes that agents employ an implicit coordination mechanism and consists of two iterative phases, i.e., the communication phase and the assignment phase. In the communication phase, agents communicate with their connected neighbors and exchange their local knowledge base until they converge on the global knowledge base. In the assignment phase, each agent builds a squared cost matrix by cloning agents and adding pseudo tasks when necessary, and applying the Hungarian method for task allocation. A local planning algorithm is then applied to identify the order of task execution for an agent. The proposed DMCHBA is proven to produce conflict-free assignments among agents in finite time. We compare the performance of DMCHBA with the consensus-based bundle algorithm (CBBA), the distributed recursive Hungarian-based algorithms (DRHBA), and the cluster-based Hungarian algorithm (CHBA) in Monte-Carlo simulations with different numbers of agents and tasks. The numerical results reveal the superior convergence and optimality of DMCHBA over all other selected algorithms.

A method for aerial rendezvous of small unmanned aircraft systems is proposed. The system includes placing a passively towed drogue into an orbit that is suitable for a small unmanned aircraft systems to follow, and a seeker guidance method for use with a monocular camera mounted on the centerline of the unmanned aircraft systems. The principle contributions of the work include a method for controlling the drogue path in moderate winds by manipulating the mothership orbit and airspeed, and a vision-based nonlinear pursuit tracking method that uses pitch rate and roll commands to guide the unmanned aircraft systems to an aerial rendezvous with the drogue. Simulation and flight-test results are presented to demonstrate the suitability of these methods.