Simultaneous State and Parameter Estimation using Robust Receding-horizon Nonlinear Kalman Filter
نویسندگان
چکیده
منابع مشابه
Data-Driven Robust Receding Horizon Fault Estimation
This paper presents a data-driven receding horizon fault estimation method for additive actuator and sensor faults in unknown linear time-invariant systems, with enhanced robustness to stochastic identification errors. State-of-the-art methods construct fault estimators with identified state-space models or Markov parameters, but they do not compensate for identification errors. Motivated by th...
متن کاملRotated Unscented Kalman Filter for Two State Nonlinear Systems
In the several past years, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.The UKF has consistently outperformed for estimation. Sometimes least estimation error doesn't yieldwith UKF for the most nonlinear systems. In this paper, we use a new approach for a two variablestate no...
متن کاملFinite-horizon robust Kalman filter design
In this paper, we study the problem of finite-horizon Kalman filtering for systems involving a norm-bounded uncertain block. A new technique is presented for robust Kalman filter design. This technique involves using multiple scaling parameters that can be optimized by solving a semidefinite program. The use of optimized scaling parameters leads to an improved design. A recursive design method ...
متن کاملNonlinear state estimation , indistinguishable states , and the extended Kalman filter
It has been shown by Judd and Smith that it is impossible to determine the state of a nonlinear dynamical system from noisy observations of the system, even with perfect knowledge of the system dynamics and unlimited prior observation. There is always a set of states indistinguishable from the true state. However, a new, simple method to assimilate data into a model and estimate the state is su...
متن کاملOptimal Receding Horizon Filter for Continuous-Time Nonlinear Stochastic Systems
A receding horizon filtering problem for nonlinear continuous-time stochastic systems is considered. The paper presents the optimal receding horizon filtering equations. Derivation of the equations is based on the Kushner-Stratonovich and Fokker-Planck-Kolmogorov equations for conditional and unconditional density functions. This result could be a theoretical basis for the optimal control in no...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2019
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2019.06.030