| |
| In this paper, we investigate state estimations of a dynamical system with random parametric uncertainties which may arbitrarily affect a state-space plant model.A robust estimator is derived based on expectation minimization of estimation errors. %The state estimator based on nominal model of the plant will introduce an appreciable bias proportional to the known input signal owing to parameter uncertainties.An analytic solution similar to that of the well-known Kalman filter is derived for this new robust estimator which can be realized recursively with a comparable computational complexity.Under some weak assumptions, %some important and attractive properties of this robust estimator such as convergence and boundedness are investigated,it is proved that this estimator converges to a stable system, the covariance matrix of estimation errors is bounded, and the estimation is asymptotically unbiased. Numerical simulations show that the obtained robust filter has an estimation accuracy comparable to other estimators and can be applied in a wider range. |
|
| Keywords:state estimation, robustness, recursive estimation, parametric uncertainty, regularized least-squares |
|
