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在地面上精确测量航天器的惯性参数是困难的,并且由于燃料的消耗、航天器的交会对接、载荷及姿态的变化等因素将会使航天器的惯性参数在轨发生变化。因而航天器的控制系统、状态估计系统将会受到航天器惯性参数变化的影响。在轨辨识出航天器的惯性参数,可以为更加优化、实时的控制航天器服务。文中提出了一种基于粒子群优化算法的航天器惯性参数辨识算法。建立了引入带有模型误差以及由于航天器惯性参数变化引起的误差的航天器姿态运动学与动力学模型,基于模型误差最小准则建立目标函数,利用改进的粒子群优化算法对模型误差进行实时估计,从而实现对航天器惯性参数的辨识,并将其应用到航天器的姿态控制中,并通过仿真实验证明了该算法的有效性以及实用性。
It is difficult to accurately measure the inertial parameters of the spacecraft on the ground, and due to the fuel consumption, spacecraft rendezvous and docking, changes in load and attitude will cause the inertial parameters of the spacecraft to change in orbit. Therefore, the spacecraft’s control system and state estimation system will be affected by changes in the inertial parameters of the spacecraft. Recognizing the inertial parameters of the spacecraft on the orbit can serve spacecraft for more optimized and real-time control of the spacecraft. In this paper, an inertial parameter identification algorithm of spacecraft based on particle swarm optimization is proposed. A spacecraft attitude kinematics and dynamics model with model errors and errors due to changes of spacecraft inertial parameters is established. An objective function is established based on the minimum model error criterion. The model error is estimated in real time using an improved Particle Swarm Optimization , So as to realize the inertial parameter identification of the spacecraft, and apply it to the attitude control of the spacecraft. The simulation results show the effectiveness and practicability of the algorithm.