针对复杂威胁环境下多无人机协同跟踪动态目标的问题,提出了一种多策略改进灰狼优化算法(multi-strategy improved grey wolf optimization,MSIGWO)的分布式模型预测控制方法。通过对多无人机跟踪动态飞行目标场景问题描述,考虑无人机...针对复杂威胁环境下多无人机协同跟踪动态目标的问题,提出了一种多策略改进灰狼优化算法(multi-strategy improved grey wolf optimization,MSIGWO)的分布式模型预测控制方法。通过对多无人机跟踪动态飞行目标场景问题描述,考虑无人机运动学、相对运动学、战场复杂威胁、机间距离和视场传感器等约束,建立了多无人机协同跟踪动态目标的数学模型;基于分布式模型预测控制设计了多无人机协同轨迹在线优化求解框架,提出了一种改进灰狼算法作为分布式轨迹规划求解策略,通过控制参数自适应调整策略,最优位置学习更新策略以及跳出局部最优解策略来增强种群多样性,进而提升算法的优化求解能力;应用数值仿真和半实物仿真验证了所提出策略和方法的有效性。仿真结果表明:提出的多无人机分布式协同轨迹规划方法能够在有效避开动态环境障碍的条件下协同跟踪动态目标,具有较优的跟踪效能。展开更多
In the air combat process,confrontation position is the critical factor to determine the confrontation situation,attack effect and escape probability of UAVs.Therefore,selecting the optimal confrontation position beco...In the air combat process,confrontation position is the critical factor to determine the confrontation situation,attack effect and escape probability of UAVs.Therefore,selecting the optimal confrontation position becomes the primary goal of maneuver decision-making.By taking the position as the UAV’s maneuver strategy,this paper constructs the optimal confrontation position selecting games(OCPSGs)model.In the OCPSGs model,the payoff function of each UAV is defined by the difference between the comprehensive advantages of both sides,and the strategy space of each UAV at every step is defined by its accessible space determined by the maneuverability.Then we design the limit approximation of mixed strategy Nash equilibrium(LAMSNQ)algorithm,which provides a method to determine the optimal probability distribution of positions in the strategy space.In the simulation phase,we assume the motions on three directions are independent and the strategy space is a cuboid to simplify the model.Several simulations are performed to verify the feasibility,effectiveness and stability of the algorithm.展开更多
文摘针对复杂威胁环境下多无人机协同跟踪动态目标的问题,提出了一种多策略改进灰狼优化算法(multi-strategy improved grey wolf optimization,MSIGWO)的分布式模型预测控制方法。通过对多无人机跟踪动态飞行目标场景问题描述,考虑无人机运动学、相对运动学、战场复杂威胁、机间距离和视场传感器等约束,建立了多无人机协同跟踪动态目标的数学模型;基于分布式模型预测控制设计了多无人机协同轨迹在线优化求解框架,提出了一种改进灰狼算法作为分布式轨迹规划求解策略,通过控制参数自适应调整策略,最优位置学习更新策略以及跳出局部最优解策略来增强种群多样性,进而提升算法的优化求解能力;应用数值仿真和半实物仿真验证了所提出策略和方法的有效性。仿真结果表明:提出的多无人机分布式协同轨迹规划方法能够在有效避开动态环境障碍的条件下协同跟踪动态目标,具有较优的跟踪效能。
基金National Key R&D Program of China(Grant No.2021YFA1000402)National Natural Science Foundation of China(Grant No.72071159)to provide fund for conducting experiments。
文摘In the air combat process,confrontation position is the critical factor to determine the confrontation situation,attack effect and escape probability of UAVs.Therefore,selecting the optimal confrontation position becomes the primary goal of maneuver decision-making.By taking the position as the UAV’s maneuver strategy,this paper constructs the optimal confrontation position selecting games(OCPSGs)model.In the OCPSGs model,the payoff function of each UAV is defined by the difference between the comprehensive advantages of both sides,and the strategy space of each UAV at every step is defined by its accessible space determined by the maneuverability.Then we design the limit approximation of mixed strategy Nash equilibrium(LAMSNQ)algorithm,which provides a method to determine the optimal probability distribution of positions in the strategy space.In the simulation phase,we assume the motions on three directions are independent and the strategy space is a cuboid to simplify the model.Several simulations are performed to verify the feasibility,effectiveness and stability of the algorithm.
