Fast moving horizon state estimation for nonlinear discrete‐time systems affected by disturbances is addressed by means of imperfect optimization at each time instant based on few iterations of the gradient, conjugate gradient, and Newton algorithms. Linear constraints on the state vector are taken into account through a projection on the subspace associated with such constraints. The stability of the estimation error for the resulting scheme is proved under suitable conditions. The effectiveness of the proposed approach is showcased via simulation results in comparison with moving horizon estimation based on complete optimization and extended Kalman filtering.

中文翻译：

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具有线性约束的离散时间系统的快速移动视界状态估计

基于梯度，共轭梯度和牛顿算法的几次迭代，通过在每个时刻进行不完美的优化，解决了受干扰影响的非线性离散系统的快速运动状态估计。通过在子空间上与这样的约束相关联的投影来考虑对状态向量的线性约束。在适当的条件下证明了所得方案的估计误差的稳定性。与基于完全优化和扩展卡尔曼滤波的运动层估计相比，通过仿真结果证明了该方法的有效性。