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The resilience of unmanned aerial vehicle (UAV) swarm is its joint capability to resist possible threat, adapt to disruptive events, and restore its intended performance under a specific time period. The quantitative assessment of the UAV swarm resilience requires a thorough understanding of its missions. In this paper, a mission-oriented framework is proposed to implement the resilience evaluation for the UAV swarm. Guided by the framework, the resilience evaluation for the UAV swarm performing joint reconnaissance mission is studied. A UAV swarm model is developed for joint reconnaissance mission based on complex networks and agent-based models. The following aspects of the UAV swarm are considered in the proposed model, namely, the mission orientation, UAV attributes, swarm topology, UAV cooperative strategy, UAV information exchange and fusion strategy, potential threats, recovery strategies, etc. Then, a novel performance metric is proposed to measure the mission capability of the UAV swarm performing joint reconnaissance mission. Results from the simulations show that, compared with existing studies, the proposed approach can provide more realistic and objective resilience evaluation for the mission-oriented UAV swarm. The above works can be used to support the decision making and the optimal design of the UAV swarm, given different missions.

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Abstract. Parallel synchronized switch harvesting on inductor (P-SSHI) circuits have been proved to enhance piezoelectric vibration energy harvesting (PVEH), but the maximum conversion efficiency is obtained only when the voltage across the storage capacitor (VSC) equates to the optimal value. For bi-stable rotating PVEH with nonlinear magnetic force in engineering applications, however, the VSC will change greatly when powering real electric loads, thus it is impossible to maintain high conversion efficiency. In order to solve this problem, this paper presents an improved P-SSHI circuit with controllable optimal voltage (COV-PSSHI) by using a voltage control strategy between the storage capacitor and the electric load. The innovation is to control and maintain the VSC close to the optimal value. Firstly, the COV-PSSHI circuit is proposed and its theoretic model is built in detail. Then its average harvested power (AHP) is theoretically derived and AHP of the COV-PSSHI circuit is proved to be more than that of a classical P-SSHI circuit. In the end, experiments are performed to validate the performance of the COV-PSSHI circuit. It can be seen that the COV-PSSHI circuit can increase the AHP by factor 1.25 compared with classical P-SSHI circuits, which is enough to intermittently power the wireless sensor node. Also power consumption of the voltage control circuit has few effects on the COV-PSSHI circuit. In particular, it needs to optimize the envelop capacitor, the parallel inductor and two threshold voltages of the voltage controller in order to implement the COV-PSSHI circuit well in practice.

A practical piezoelectric vibration energy harvesting (PVEH) system is usually composed of two coupled parts: a harvesting structure and an interface circuit. Thus, it is much necessary to build system-level coupled models for analyzing PVEH systems, so that the whole PVEH system can be optimized to obtain a high overall efficiency. In this paper, two classes of coupled models are proposed by joint finite element and circuit analysis. The first one is to integrate the equivalent circuit model of the harvesting structure with the interface circuit and the second one is to integrate the equivalent electrical impedance of the interface circuit into the finite element model of the harvesting structure. Then equivalent circuit model parameters of the harvesting structure are estimated by finite element analysis and the equivalent electrical impedance of the interface circuit is derived by circuit analysis. In the end, simulations are done to validate and compare the proposed two classes of system-level coupled models. The results demonstrate that harvested powers from the two classes of coupled models approximate to theoretic values. Thus, the proposed coupled models can be used for system-level optimizations in engineering applications.