基于粒子群优化算法的航天器惯性参数辨识

Identification of Spacecraft Inertia Parameters Based on Particle Swarm Optimization Algorithm

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 precisely measure inertia parameters on the ground.Meanwhile the inertia parameters can change on-orbit as fuel is expended and the configurations or payloads are changed.Hence spacecraft control,state estimation systems are affected by the variations of the spacecraft＇s inertia parameters.Estimating the spacecraft＇s inertia parameters on orbit is good to control the spacecraft optimally and real-time.A method of estimating the spacecraft＇s inertia parameters using the particle swarm optimization algorithm is presented in this paper.The attitude kinematics and dynamics models of spacecraft are established with the modeling error and the error caused by the change of the inertia parameters.The cost function based on the minimum-model-error criterion is introduced.The particle swarm optimization algorithm is used to make real-time estimation of the error.It is used to optimize the control system of the spacecraft.The result of the simulation shows the method is effective and practical.