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Model predictive control of attitude maneuver of a geostationary flexible satellite based on genetic algorithm

机译:基于遗传算法的对地静止柔性卫星姿态机动模型预测控制

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摘要

This article presents an application of Model Predictive Controller (MPC) to the attitude control of a geostationary flexible satellite. SIMO model has been used for the geostationary satellite, using the Lagrange equations. Flexibility is also included in the modelling equations. The state space equations are expressed in order to simplify the controller. Naturally there is no specific tuning rule to find the best parameters of an MPC controller which fits the desired controller. Being an intelligence method for optimizing problem, Genetic Algorithm has been used for optimizing the performance of MPC controller by tuning the controller parameter due to minimum rise time, settling time, overshoot of the target point of the flexible structure and its mode shape amplitudes to make large attitude maneuvers possible. The model included geosynchronous orbit environment and geostationary satellite parameters. The simulation results of the flexible satellite with attitude maneuver shows the efficiency of proposed optimization method in comparison with LQR optimal controller.
机译:本文介绍了模型预测控制器(MPC)在对地静止柔性卫星的姿态控制中的应用。使用拉格朗日方程,SIMO模型已用于对地静止卫星。建模方程式还包括灵活性。表达状态空间方程式是为了简化控制器。自然地,没有找到适合所需控制器的MPC控制器最佳参数的特定调整规则。遗传算法是一种用于优化问题的智能方法,已被用于通过优化控制器参数来优化MPC控制器的性能,该参数是由于最小上升时间,稳定时间,柔性结构目标点的过冲及其模态形状振幅而引起的,从而使得大姿态演习成为可能。该模型包括地球同步轨道环境和地球静止卫星参数。与LQR最优控制器相比,采用姿态机动的柔性卫星的仿真结果表明了所提优化方法的有效性。

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