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3D Path Planning of the Solar Powered UAV in the Urban-Mountainous Environment with Multi-Objective and Multi-Constraint Based on the Enhanced Sparrow Search Algorithm Incorporating the Levy Flight Strategy
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作者 Pengyang Xie Ben Ma +2 位作者 Bingbing Wang Jian Chen Gang Xiao 《Guidance, Navigation and Control》 2024年第1期149-175,共27页
In response to practical application challenges in utilizing solar-powered unmanned aerial vehicle(UAV)for remote sensing,this study presents a three-dimensional path planning method tailored for urban-mountainous env... In response to practical application challenges in utilizing solar-powered unmanned aerial vehicle(UAV)for remote sensing,this study presents a three-dimensional path planning method tailored for urban-mountainous environment.Taking into account constraints related to the solar-powered UAV,terrain,and mission objectives,a multi-objective trajectory optimization model is transferred into a single-objective optimization problem with weight factors and multiconstraint and is developed with a focus on three key indicators:minimizing trajectory length,maximizing energy flow efficiency,and minimizing regional risk levels.Additionally,an enhanced sparrow search algorithm incorporating the Levy flight strategy(SSA-Levy)is introduced to address trajectory planning challenges in such complex environments.Through simulation,the proposed algorithm is compared with particle swarm optimization(PSO)and the regular sparrow search algorithm(SSA)across 17 standard test functions and a simplified simulation of urban-mountainous environments.The results of the simulation demonstrate the superior effectiveness of the designed improved SSA based on the Levy flight strategy for solving the established single-objective trajectory optimization model. 展开更多
关键词 Solar powered UAV multi-objective optimization problem single-objective optimization problem multi-constraint sparrow search algorithm Levy flight strategy
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Multi-objective microgrid optimal dispatching based on improved bird swarm algorithm 被引量:4
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作者 Xiaoyan Ma Yunfei Mu +4 位作者 Yu Zhang Chenxi Zang Shurong Li Xinyang Jiang Meng Cui 《Global Energy Interconnection》 EI CAS CSCD 2022年第2期154-167,共14页
Multi-objective optimal dispatching schemes with intelligent algorithms are recognized as effective measures to promote the economics and environmental friendliness of microgrid applications.However,the low accuracy a... Multi-objective optimal dispatching schemes with intelligent algorithms are recognized as effective measures to promote the economics and environmental friendliness of microgrid applications.However,the low accuracy and poor convergence of these algorithms have been challenging for system operators.The bird swarm algorithm(BSA),a new bio-heuristic cluster intelligent algorithm,can potentially address these challenges;however,its computational iterative process may fall into a local optimum and result in premature convergence when optimizing small portions of multi-extremum functions.To analyze the impact of a multi-objective economic-environmental dispatching of a microgrid and overcome the aforementioned problems of the BSA,a self-adaptive levy flight strategy-based BSA(LF-BSA)was proposed.It can solve the dispatching problems of microgrid and enhance its dispatching convergence accuracy,stability,and speed,thereby improving its optimization performance.Six typical test functions were used to compare the LF-BSA with three commonly accepted algorithms to verify its excellence.Finally,a typical summer-time daily microgrid scenario under grid-connected operational conditions was simulated.The results proved the feasibility of the proposed LF-BSA,effectiveness of the multi-objective optimization,and necessity of using renewable energy and energy storage in microgrid dispatching optimization. 展开更多
关键词 MICROGRID Operation optimization Bird swarm algorithm Levy flight strategy SELF-ADAPTIVE
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Energy-optimal trajectory planning for solar-powered aircraft using soft actor-critic 被引量:6
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作者 Wenjun NI Ying BI +1 位作者 Di WU Xiaoping MA 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第10期337-353,共17页
High-Altitude Long-Endurance(HALE)solar-powered Unmanned Aircraft Vehicles(UAVs)can utilize solar energy as power source and maintain extremely long cruise endurance,which has attracted extensive attentions from resea... High-Altitude Long-Endurance(HALE)solar-powered Unmanned Aircraft Vehicles(UAVs)can utilize solar energy as power source and maintain extremely long cruise endurance,which has attracted extensive attentions from researchers.Trajectory optimization is a promising way to achieve superior flight time because of the finite solar energy absorbed in a day.In this work,a method of trajectory optimization and guidance for HALE solar-powered aircraft based on a Reinforcement Learning(RL)framework is introduced.According to flight and environment information,a neural network controller outputs commands of thrust,attack angle,and bank angle to realize an autonomous flight based on energy maximization.The validity of the proposed method was evaluated in a 5-km radius area in simulation,and results have shown that after one day-night cycle,the battery energy of the RL-controller was improved by 31%and 17%compared with those of a Steady-State(SS)strategy with a constant speed and a constant altitude and a kind of statemachine strategy,respectively.In addition,results of an uninterrupted flight test have shown that the endurance of the RL controller was longer than those of the control cases. 展开更多
关键词 flight strategy Guidance and control Reinforcement learning Solar powered aeroplane Trajectory generation
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