基于协态估计的火箭动力下降邻近最优制导

针对火箭动力下降需要满足精准软着陆的要求,提出一种基于协态估计的邻近最优制导方法。基于一阶最优必要条件,邻近最优制导方法通过在一条参考轨迹附近近似求解最优制导问题,得到闭环线性反馈制导方法。基于序列凸优化方法实现了参考轨迹的设计,由于箭载计算资源的限制以及信赖域约束的引入,在较少迭代次数下得到的参考轨迹协态并不满足一阶必要条件,导致邻近最优制导方法精度下降,因此提出了一种协态估计方法。首先,使用高斯伪谱方法对协态微分方程进行离散;然后,基于哈密尔顿函数为常数的特点,设计了新的、适应性强的性能指标;最后,基于极小值原理提出了协态估计算法。将上述方法应用到火箭动力下降仿真实例中,蒙特卡罗仿真结果表明,该方法在阻力系数偏差、推力加速度偏差和大气密度偏差下具有较好的制导精度与鲁棒性。

防空导弹最优弹道的协态估计优化算法

为了快速精确地得到防空导弹中段最优弹道,在拟勒让德谱变换法的基础上提出了协态估计优化算法.该算法克服了采用拟勒让德谱变换法所得非线性规划NLP(Non-Linear Programm ing)问题优化变量过多、收敛速度慢的缺点,并能在避免求解两点边值问题TPBVP(Two Point Boundary Value Problem)的前提下,快速解得近似满足最优解必要条件的解.针对防空导弹中段弹道优化问题的特点,应用提出的协态估计优化算法进行优化得到结果,与拟勒让德谱变换法所得结果相比,表明该算法具有收敛速度快,优化精度高的优点.

Multi-sensor federated unscented Kalman filtering algorithm in intermittent observations

Aiming at the adverse effect caused by limited detecting probability of sensors on filtering preci- sion of a nonlinear system state, a novel muhi-sensor federated unscented Kalman filtering algorithm is proposed. Firstly, combined with the residual detection strategy, effective observations are cor- rectly identified. Secondly, according to the missing characteristic of observations and the structural feature of unscented Kalman filter, the iterative process of the single-sensor unscented Kalman filter in intermittent observations is given. The key idea is that the state estimation and its error covariance matrix are replaced by the state one-step prediction and its error covariance matrix, when the phe- nomenon of observations missing occurs. Finally, based on the realization mechanism of federated filter, a new fusion framework of state estimation from each local node is designed. And the filtering precision of system state is improved further by the effective management of observations missing and the rational utilization of redundancy and complementary information among multi-sensor observa- tions. The theory analysis and simulation results show the feasibility and effectiveness of the pro- posed algorithm.