In this study,the state estimation problems for linear discrete systems with uncertain parameters,deterministic input signals and d-step measurement delay are investigated.A robust state estimator with a similar itera...In this study,the state estimation problems for linear discrete systems with uncertain parameters,deterministic input signals and d-step measurement delay are investigated.A robust state estimator with a similar iterative form and comparable computational complexity to the Kalman filter is derived based on the state augmentation method and the sensitivity penalisation of the innovation process.It is discussed that the steady-state properties such as boundedness and convergence of the robust state estimator under the assumptions that the system parameters are time invariant.Numerical simulation results show that compared with the Kalman filter,the obtained state estimator is more robust to modelling errors and has nice estimation accuracy.展开更多
基金supported by National Natural Science Foundation of China,Grant Number:61873138Shandong Provincial Natural Science Foundation,Grant Number:ZR2019MF063,ZR2020MF064.
文摘In this study,the state estimation problems for linear discrete systems with uncertain parameters,deterministic input signals and d-step measurement delay are investigated.A robust state estimator with a similar iterative form and comparable computational complexity to the Kalman filter is derived based on the state augmentation method and the sensitivity penalisation of the innovation process.It is discussed that the steady-state properties such as boundedness and convergence of the robust state estimator under the assumptions that the system parameters are time invariant.Numerical simulation results show that compared with the Kalman filter,the obtained state estimator is more robust to modelling errors and has nice estimation accuracy.
