<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine local linearisation and a nonlinear free trajectory. Single-point and multi-point linearisation methods are discussed. The MPC-NPL structure is far more reliable and less computationally demanding in comparison with the MPC-NO one because it solves a quadratic programming problem, which can be done efficiently within a foreseeable time frame. At the same time, closed-loop performance of both algorithm classes is similar. Finally, a hybrid MPC algorithm with Nonlinear Prediction, Linearisation and Nonlinear optimisation (MPC-NPL-NO) is discussed.</p></abstract>

This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine local linearisation and a nonlinear free trajectory. Single-point and multi-point linearisation methods are discussed. The MPC-NPL structure is far more reliable and less computationally demanding in comparison with the MPC-NO one because it solves a quadratic programming problem, which can be done efficiently within a foreseeable time frame. At the same time, closed-loop performance of both algorithm classes is similar. Finally, a hybrid MPC algorithm with Nonlinear Prediction, Linearisation and Nonlinear optimisation (MPC-NPL-NO) is discussed.

A Family of Model Predictive Control Algorithms With Artificial Neural Networks

Institute of Control and Computation Engineering Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"A Family of Model Predictive Control Algorithms With Artificial Neural Networks"}

This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward...

A Family of Model Predictive Control Algorithms With Artificial Neural Networks

Published Online: Jul 17, 2007

Page range: 217 - 232

DOI: https://doi.org/10.2478/v10006-007-0020-5

Keywords
predictive control, neural networks, optimisation, linearisation, quadratic programming

This content is open access.

A Family of Model Predictive Control Algorithms With Artificial Neural Networks

Published Online: Jul 17, 2007

Page range: 217 - 232

DOI: https://doi.org/10.2478/v10006-007-0020-5

Keywordspredictive control, neural networks, optimisation, linearisation, quadratic programming

This content is open access.

Keywords
predictive control, neural networks, optimisation, linearisation, quadratic programming