Sequential estimators are derived for suboptimal adaptive estimation of the unknown a priori
state and observation noise statistics simultaneously with the system state. First-and second …

This paper provides a review of the available tuning guidelines for model predictive control,
from theoretical and practical perspectives. It covers both popular dynamic matrix control …

Industrial implementation of model-based control methods, such as model predictive control,
is often complicated by the lack of knowledge about the disturbances entering the system. In …

With Bayesian statistics rapidly becoming accepted as a way to solve applied
statisticalproblems, the need for a comprehensive, up-to-date source on the latest advances …

This paper deals with the estimation of the noise covariance matrices of systems described
by state‐space models. Stress is laid on the systematic survey and classification of both the …

Correct knowledge of noise statistics is essential for an estimator or controller to have
reliable performance. In practice, however, the noise statistics are unknown or not known …

In this paper, we present a new method for the estimation of the prediction-error covariances
of a Kalman filter (KF), which is suitable for step-varying processes. The method uses a …

This paper demonstrates the autocovariance least-squares (ALS) technique on two
chemical reactor control problems. The method uses closed-loop process data to recover …

Recurrent neural network (RNN) has emerged as a promising tool in modeling nonlinear
dynamical systems, but the training convergence is still of concern. This paper aims to …

This article introduces two new algorithms to accurately estimate the process noise
covariance of a discrete-time Kalman filter online for robust orbit determination in the …