论文标题
连续发生随机系统的同时态和未知输入估计
Simultaneous State and Unknown Input Estimation for Continuous-discrete Stochastic Systems
论文作者
论文摘要
本文考虑了连续污染随机系统的同时状态和未知输入估计。可以研究两种类型的方法（有和没有未知输入的建模）可以解决此问题。提出了一种新型的连续递归四步卡尔曼滤波器，并建立了其渐近稳定性条件。提出了一种新型的单步输入Kalman滤波器，当未知输入的数量等于测量值时，并保证了稳定性。未知输入Kalman过滤器和观察者的设计是统一的。此外，还引入了需要对未知输入进行建模的自适应增强Kalman滤波器。分析并比较了递归四步Kalman滤波器和自适应增强Kalman滤波器的估计误差协方差。最后，仿真结果证明了所提出的方法的有效性。
This paper considers the simultaneous state and unknown input estimation for continuous-discrete stochastic systems. Two types of approaches (with and without modeling of unknown inputs) which can address this issue are investigated. A novel continuous recursive four-step Kalman filter is proposed and its asymptotic stability condition is established. A novel one-step unknown input Kalman filter is proposed and has guaranteed stability when the number of unknown inputs is equal to that of the measurements. The design of unknown input Kalman filters and observers is unified. Furthermore, an adaptive augmented Kalman filter which requires the modeling of unknown inputs is introduced. The estimation error covariance of the recursive four-step Kalman filter and the adaptive augmented Kalman filter is analyzed and compared. Finally, simulation results demonstrate the effectiveness of the proposed approaches.
