【摘要】 为了快速精确地求解小推力最优轨道转移问题,提出了一种基于无损卡尔曼滤波(UKF)参数估计算法的轨道机动优化方法。针对小推力情况下的燃料最优轨道转移问题,应用极大值原理将其转化为对应的最优控制两点边值问题,然后以协态变量初值为待估计参数,以端点条件为期望观测值,将该两点边值问题转化为参数估计问题,并应用UKF滤波算法求解。该算法收敛原理是基于概率估计理论的,避免了传统间接法所需的相关梯度矩阵的推导,同时又克服了协态变量初值猜测的困难。由于问题构造形式相似,该算法同样适用于求解时间最短轨道转移问题。数值仿真表明,该算法结构简洁,求解效率较高,所得结果精确且具有良好的鲁棒性。更多还原

【Abstract】 Based on Unscented Kalman Filter( UKF) parameter estimation,an algorithm for efficient low-thrust orbit transfer optimization with high accuracy is developed in this paper. Minimum-fuel orbit transfer problem is mainly discussed. First,the problem is formulated as a two-point boundary value problem by using the maximum principle. By setting the initial values of the costates as parameters to be estimated and the boundary values as desired observations,this problem is transformed into a parameter estimation problem,and then is solved by using unscented Kalman filter. The convergence of this algorithm is based on the theory of probability,so the derivation of the gradient matrixes required by traditional indirect methods is avoided. Moreover,the difficulty of guessing the initial values of the costates is overcome. Due to the similarity in problem construction,minimum-time orbit transfer problem can also be solved by using this algorithm. Numerical simulations demonstrate that the algorithm is simple,efficient,accurate and robust.更多还原