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针对挠性卫星姿态快速机动快速稳定控制中的路径优化问题,研究了一种基于云多目标粒子群算法(CMOPSO)的姿态机动路径优化方法.为了解决云多目标粒子群算法寻优初期可能出现粒子陷入局部最优的问题,提出了一种随迭代次数呈反正切函数变化调整惯性权重的改进云多目标粒子群算法.针对挠性卫星大角度姿态机动问题,考虑挠性卫星姿态机动过程中角加速度和角速度的限制,建立了姿态机动路径参数的多目标优化模型,并采用改进的CMOPSO进行优化.仿真结果验证了所提改进的云多目标粒子群算法在挠性卫星姿态快速机动快速稳定控制中的有效性.
In order to solve the problem of path optimization in rapid maneuvering fast and stable control of flexible satellite attitude, a method of attitude maneuvering path optimization based on cloud multi-objective particle swarm optimization (CMOPSO) is proposed. In order to solve the problem that cloud multi-objective particle swarm optimization We propose a modified cloud multi-objective particle swarm optimization algorithm that adjusts the inertia weight as the function of the arctangent of the iteration times.Aiming at the large-angle attitude maneuver problem of flexible satellites, considering the attitude maneuvering process of the flexible satellite, The angular acceleration and the angular velocity, the multi-objective optimization model of attitude maneuvering path parameters is established and optimized by the improved CMOPSO.The simulation results verify that the proposed improved multi-objective particle swarm optimization algorithm is fast and stable in flexible satellite attitude Effectiveness in control.