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-strategyimproved 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.
A Distributed Collaborative Trajectory Planning Method for Multi-UAV Targets Tracking in Complex Dynamic Environment
Aiming at the problem of multi-UAV cooperative tracking dynamic targets under complex threat environment,a distributed model predictive control method with Multi-Strategy Improved Grey Wolf Optimization(MSIGWO)is proposed.By describing the problem of multi-UAV tracking dynamic flight target scenario and considering the constraints of UAV kinematics,relative kinematics,complex threats of battlefield,inter-aircraft distance and field-of-view sensors,etc.,a mathematical model of multi-UAV cooperative tracking dynamic targets is established;a multi-UAV cooperative trajectory online optimization solution framework is designed based on distributed model predictive control,and an improved Grey Wolf algorithm is proposed as a distributed trajectory planning solution strategy.The diversity of population is enhanced by the control of parameter adaptive adjustment strategy,optimal position learning update strategy and jumping out of the local optimal solution strategy so as to improve the optimal solution capability of the algorithm;The effectiveness of the proposed strategy and method are validated by numerical and hardware-in-the-loop(HIL)simulations,and the simulation results show that the proposed multi-UAV distributed cooperative trajectory planning method can effectively avoid the dynamic environment obstacles and collaboratively track the dynamic target with better tracking performance.
multi-UAVtarget trackingtrajectory planningmodel prediction controlgrey wolf optimization algorithm
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