Model predictive control in aerospace systems: Current state and opportunities
CONTROLLER design is more troublesome in aerospace systems due to, inter alia, diversity
of mission platforms, convoluted nonlinear dynamics, predominantly strict mission and …
of mission platforms, convoluted nonlinear dynamics, predominantly strict mission and …
[HTML][HTML] Maximizing information from chemical engineering data sets: Applications to machine learning
It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …
impact on chemical engineering. But classical machine learning approaches may be weak …
Unbiased finite impluse response filtering: An iterative alternative to Kalman filtering ignoring noise and initial conditions
If a system and its observation are both represented in state space with linear equations, the
system noise and the measurement noise are white, Gaussian, and mutually uncorrelated …
system noise and the measurement noise are white, Gaussian, and mutually uncorrelated …
Recent advances in the monitoring, modelling and control of crystallization systems
Crystallization is one of the most important unit operations used for the separation and
purification of crystalline solid products. Appropriate design and control of the crystallization …
purification of crystalline solid products. Appropriate design and control of the crystallization …
MPC: Current practice and challenges
ML Darby, M Nikolaou - Control Engineering Practice, 2012 - Elsevier
Linear Model Predictive Control (MPC) continues to be the technology of choice for
constrained, multivariable control applications in the process industry. Successful …
constrained, multivariable control applications in the process industry. Successful …
Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter
The performance of Bayesian state estimators, such as the extended Kalman filter (EKF), is
dependent on the accurate characterisation of the uncertainties in the state dynamics and in …
dependent on the accurate characterisation of the uncertainties in the state dynamics and in …
A real-time algorithm for moving horizon state and parameter estimation
A moving horizon estimation (MHE) approach to simultaneously estimate states and
parameters is revisited. Two different noise models are considered, one with measurement …
parameters is revisited. Two different noise models are considered, one with measurement …
Nonlinear Bayesian state estimation: A review of recent developments
Online estimation of the internal states is a perquisite for monitoring, control, and fault
diagnosis of many engineering processes. A cost effective approach to monitor these …
diagnosis of many engineering processes. A cost effective approach to monitor these …
Advances and selected recent developments in state and parameter estimation
This paper deals with two topics from state and parameter estimation. The first contribution of
this work provides an overview of techniques used for determining which parameters of a …
this work provides an overview of techniques used for determining which parameters of a …
Constrained nonlinear state estimation based on the UKF approach
S Kolås, BA Foss, TS Schei - Computers & Chemical Engineering, 2009 - Elsevier
In this paper we investigate the use of an alternative to the extended Kalman filter (EKF), the
unscented Kalman filter (UKF). First we give a broad overview of different UKF algorithms …
unscented Kalman filter (UKF). First we give a broad overview of different UKF algorithms …