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In order to enhance the capability of tracking targets autonomously of UAV, a model for UAV on-line path planning is established based on the theoretical framework of partially observable markov decision process(POMDP). The elements of the POMDP model are analyzed and described. According to the diversity of the target motion in real world, the law of state transition in POMDP model is described by the method of Interactive Multiple Model(IMM) To adapt to the target maneuvering changes. The action strategy of the UAV is calculated through nominal belief-state optimization(NBO) algorithm which is designed to search optimal action policy to minimize the cumulative cost of action. The generated action strategy controls the UAV flight. The simulation results show that the established POMDP model can achieve autonomous planning for UAV route, and it can control the UAV to effectively track target. The planning path is more reasonable and efficient than the result of using single state transition law.

This paper studies the distributed convex optimization problem, where the global utility function is the sum of local cost functions associated to the individual agents. Only using the local information, a novel continuous-time distributed algorithm based on proportional-integral-differential (PID) control strategy is proposed. Under the assumption that the global utility function is strictly convex and local utility functions have locally Lipschitz gradients, the exponential convergence of the proposed algorithm is established with undirected and connected graph among these agents. Finally, numerical simulations are presented to illustrate the effectiveness of theoretical results.

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This paper studies the resource allocation problems with second‐order multi‐agent systems under the constraint of the global network resource. A novel distributed continuous‐time algorithm, which only uses the local information, is proposed. By using matrix transform and differential inequality technique, the exponential convergence is obtained under the undirected and connected graph. To reduce energy consumption and communication costs, a distributed event‐triggered algorithm is designed. It is proved that not only this algorithm converges exponentially to the optimal allocation, but also the event‐triggered time sequence does not exhibit Zeno‐behavior. Finally, numerical examples illustrate the effectiveness of theoretical results.