基于状态预测器和事件触发的多无人系统周期性DoS攻击韧性一致性控制

This paper develops an event-triggered resilient consensus control method for the nonlinear multiple unmanned systems with a data-based autoregressive integrated moving average(ARIMA)agent state prediction mechanism against periodic denial-of-service(Do S)attacks.The state predictor is used to predict the state of neighbor agents during periodic Do S attacks and maintain consistent control of multiple unmanned systems under Do S attacks.Considering the existing prediction error between the actual state and the predicted state,the estimated error is regarded as the uncertainty system disturbance,which is dealt with by the designed disturbance observer.The estimated result is used in the design of the consistent controller to compensate for the system uncertainty error term.Furthermore,this paper investigates dynamic event-triggered consensus controllers to improve resilience and consensus under periodic Do S attacks and reduce the frequency of actuator output changes.It is proved that the Zeno behavior can be excluded.Finally,the resilience and consensus capability of the proposed controller and the superiority of introducing a state predictor are demonstrated through numerical simulations.

fcl基因对须糖多孢菌丁烯基多杀菌素生物合成及生长发育的影响

fcl基因编码的GDP-岩藻糖合成酶(GDP fucose synthetase,GFS),能催化由GDP-D-甘露糖合成GDP-L-岩藻糖过程中的两步差向异构酶和还原酶反应;还参与氨基糖和核糖的生物合成,是调控生物体糖代谢、核苷酸代谢的关键酶之一。通过前期基因组测序表明须糖多孢菌Saccharopolyspora pogona中存在fcl基因。利用基因工程技术构建了fcl基因的过表达菌株S. pogona-fcl和敲除菌株S. pogona-Δfcl。结果表明该基因对菌株生长发育、蛋白表达及其转录水平、杀虫活性、丁烯基多杀菌素的生物合成均存在影响。经HPLC分析显示,S. pogona-Δfcl的丁烯基多杀菌素产量增加为野生型菌株的130%,S.pogona-fcl的丁烯基多杀菌素产量降低了25%。生测结果显示,与野生型菌株相比S.pogona-Δfcl对棉铃虫的杀虫活性明显增强,而S.pogona-fcl的杀虫活性降低。利用扫描电镜观察发现,S. pogona-Δfcl菌丝体表面出现褶皱,呈现短棒状,S. pogona-fcl菌丝形态与野生型菌株一致。以上结果表明,fcl基因的敲除影响菌丝体的生长发育,能促进丁烯基多杀菌素的生物合成和增强杀虫活性,该基因的过表达抑制了丁烯基多杀菌素的生物合成和降低了杀虫活性。SDS-PAGE结果表明,三株菌株在96 h时蛋白表达差异最为明显。对差异蛋白通过实时荧光定量聚合酶链式反应结果显示,三菌株蛋白的转录水平存在显著表达差异。通过研究结果构建了网络代谢调控图,分析fcl基因对须糖多孢菌生长发育及丁烯基多杀菌素生物合成代谢调控网络途径的影响,初步构建了fcl基因调控的代谢途径,为揭示丁烯基多杀菌素生物合成的调控机制及相关后续研究提供了实验依据。

A review on fault-tolerant cooperative control of multiple unmanned aerial vehicles

This paper presents the recent developments in Fault-Tolerant Cooperative Control(FTCC)of multiple unmanned aerial vehicles(multi-UAVs).To facilitate the analyses of FTCC methods for multi-UAVs.the formation control strategies under fault-free flight conditions of multi-UAVs are first summarized and analyzed,including the leader-following,behavior-based,virtual structure,collision avoidance,algebraic graph-based,and close formation control methods,which are viewed as the cooperative control methods for multi-UAVs at the pre-fault stage.Then,by considering the various faults encountered by the multi-UAVs,the state-of-the-art developments on individual,leader-following,and distributed FTCC schemes for multi-UAVs are reviewed in detail.Finally,conclusions and challenging issues towards future developments are presented.

Path Following Control for UAV Using Deep Reinforcement Learning Approach

Unmanned aerial vehicles(UAVs)have been extensively used in civil and industrial applications due to the rapid development of the guidance,navigation and control(GNC)technologies.Especially,using deep reinforcement learning methods for motion control acquires a major progress recently,since deep Q-learning algorithm has been successfully applied to the continuous action domain problem.This paper proposes an improved deep deterministic policy gradient(DDPG)algorithm for path following control problem of UAV.A speci-c reward function is designed for minimizing the cross-track error of the path following problem.In the training phase,a double experience replay bu®er(DERB)is used to increase the learning e±ciency and accelerate the convergence speed.First,the model of UAV path following problem has been established.After that,the framework of DDPG algorithm is constructed.Then the state space,action space and reward function of the UAV path following algorithm are designed.DERB is proposed to accelerate the training phase.Finally,simulation results are carried out to show the e®ectiveness of the proposed DERB–DDPG method.