Review on model predictive control: An engineering perspective
Abstract Model-based predictive control (MPC) describes a set of advanced control
methods, which make use of a process model to predict the future behavior of the controlled …
methods, which make use of a process model to predict the future behavior of the controlled …
Learning-based model predictive control: Toward safe learning in control
Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …
sensing and computational capabilities in modern control systems, have led to a growing …
Industrial data science–a review of machine learning applications for chemical and process industries
In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to
start with examples that are irrelevant to process engineers (eg classification of images …
start with examples that are irrelevant to process engineers (eg classification of images …
A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output Filter
Model predictive control (MPC) has become one of the well-established modern control
methods for three-phase inverters with an output LC filter, where a high-quality voltage with …
methods for three-phase inverters with an output LC filter, where a high-quality voltage with …
[图书][B] Constrained model predictive control
EF Camacho, C Bordons, EF Camacho, C Bordons - 2007 - Springer
The control problem was formulated in the previous chapters considering all signals to
possess an unlimited range. This is not very realistic because in practice all processes are …
possess an unlimited range. This is not very realistic because in practice all processes are …
A survey of industrial model predictive control technology
SJ Qin, TA Badgwell - Control engineering practice, 2003 - Elsevier
This paper provides an overview of commercially available model predictive control (MPC)
technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A …
technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A …
On recurrent neural networks for learning-based control: recent results and ideas for future developments
This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks
(RNN) in control design applications. The main families of RNN are considered, namely …
(RNN) in control design applications. The main families of RNN are considered, namely …
[图书][B] Advanced control of industrial processes: structures and algorithms
P Tatjewski - 2007 - Springer
" Advanced Control of Industrial Processes" presents the concepts and algorithms of
advanced industrial process control and on-line optimisation within the framework of a …
advanced industrial process control and on-line optimisation within the framework of a …
A review of the expectation maximization algorithm in data-driven process identification
N Sammaknejad, Y Zhao, B Huang - Journal of process control, 2019 - Elsevier
Abstract The Expectation Maximization (EM) algorithm has been widely used for parameter
estimation in data-driven process identification. EM is an algorithm for maximum likelihood …
estimation in data-driven process identification. EM is an algorithm for maximum likelihood …
[HTML][HTML] Stochastic data-driven model predictive control using gaussian processes
Nonlinear model predictive control (NMPC) is one of the few control methods that can
handle multivariable nonlinear control systems with constraints. Gaussian processes (GPs) …
handle multivariable nonlinear control systems with constraints. Gaussian processes (GPs) …