在这份报纸,我们调查不仅进程和测量噪音,而且参数无常和确定的输入信号涉及的一个动态系统的州的评价。敏感 penalization 基于柔韧的州的评价与可以非线性地影响一个州空间的植物模型的确定的输入信号和参量的无常被扩大到不明确的线性系统。导出的柔韧的评估者的形式类似于有可比较的计算复杂性的著名 Kalman 过滤器的。在一些弱假设下面,尽管导出的州的评估者被偏导,评价错误的界限是有限的,评价错误的协变性矩阵被围住,这被证明。数字模拟证明获得的柔韧的过滤器有相对好的评价表演。
In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.