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第30届中国控制与决策会议

作者： Caicha Cui Daqi Zhu Bing Sun Laboratory of underwater vehicles and Intelligent Systems Shanghai Maritime University

The trajectory tracking control In UUV’s control system is an important research topic. Its kinematic model can help people understand the motion, while it is the thrusters that makes UUV move. Based on the dynamic model, this paper study about the tracking control problem of UUV. A trajectory re-planning controller based on model predictive control(MPC) is designed and added in the trajectory tracking control layer to form a new control system to produce a re-planned trajectory with obstacle avoidance. Firstly, an obstacle avoidance function is used for the design of MPC trajectory re-planning controller. Then, the tracking control law is obtained by the MPC model with speed and thrust constraints and the sequential quadratic programming optimization algorithm. Lastly, compared to the backstepping tracking algorithm, the speed jump of backstepping is sharp, while the MPC tracking controller can solve the speed jump problem well, and the MPC controller has a lower requirement for the thrust. Simulation results of different obstacles demonstrate its efficiency of the proposed the MPC trajectory re-planning tracking control algorithm for UUV.

关键词： unmanned underwater vehicles Dynamic Model Model Predictive Control Trajectory Re-planning Trajectory Tracking

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Submarine Hunter: Efficient and Secure Multi-Type unmanned vehicles

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Computers, Materials & Continua 2023年第7期76卷 573-589页

作者： Halah Hasan Mahmoud Marwan Kadhim Mohammed Al-Shammari Gehad Abdullah Amran Elsayed Tag eldin Ala R.Alareqi Nivin A.Ghamry Ehaa A.Lnajjar Esmail Almosharea Computer Center University of BaghdadBaghdad6751Iraq Department of Management Science and Engineering Dalian University of TechnologyLiaoningDalian116024China Department of Information Technology Faculty of Computer Sciences and Information Technology AL Razi UniversitySana’aYemen Faculty of Engineering and Technology Future University in EgyptNew Cairo11835Egypt Faculty of Engineering Mechanics Dalian University of TechnologyLiaoningDalian116024China Faculty of Computers and Artificial Intelligence Cairo UniversityGizaEgypt Department of Business Administration Dalian University of TechnologyLiaoningDalian116024China College of Software Engineering Dalian University of TechnologyChina

Utilizing artificial intelligence(AI)to protect smart coastal cities has become a novel vision for scientific and industrial *** of these AI technologies is using efficient and secure multi-environment unmanned vehicles(UVs)for anti-submarine *** study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity *** Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity *** research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical *** results suggest that detecting the submarine early increases the likelihood of averting a *** dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access *** communication between unmanned Aerial vehicles(UAVs)improves the level of secrecy necessary for the *** swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its ***,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other *** dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.

关键词： unmanned vehicles unmanned aerial vehicles unmanned underwater vehicles high altitude unmanned aerial vehicles anti-submarine warfare re-fragmentation dragonfly algorithm

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一种改进的指令调节自适应控制器在无人水下航行器深度控制中的实验研究

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Journal of Marine Science and Application 2021年第3期20卷 504-523页

作者： Charita D.Makavita Shantha G.Jayasinghe Hung D.Nguyen Dev Ranmuthugala Australian Maritime College University of TasmaniaLauncestonTasmania 7250Australia Department of Mechanical Engineering University of Sri JayewardenepuraRatmalana 10390Sri Lanka

Command governor–based adaptive control(CGAC)is a recent control strategy that has been explored as a possible candidate for the challenging task of precise maneuvering of unmanned underwater vehicles(UUVs)with parameter *** is derived from standard model reference adaptive control(MRAC)by adding a command governor that guarantees acceptable transient performance without compromising stability and a command filter that improves the robustness against noise and time *** simulation and experimental studies have shown substantial overall performance improvements of CGAC over MRAC for UUVs,it has also shown that the command filter leads to a marked reduction in initial tracking performance of *** a solution,this paper proposes the replacement of the command filter by a weight filter to improve the initial tracking performance without compromising robustness and the addition of a closed-loop state predictor to further improve the overall tracking *** new modified CGAC(M-CGAC)has been experimentally validated and the results indicate that it successfully mitigates the initial tracking performance reduction,significantly improves the overall tracking performance,uses less control force,and increases the robustness to noise and time ***,M-CGAC is a viable adaptive control algorithm for current and future UUV applications.

关键词： Command governor adaptive control Measurement noise Time delay Transient tracking unmanned underwater vehicles Robustness

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