Fast Nonlinear Model Predictive Control Algorithm with Neural Approximation for Embedded Systems: Preliminary Results
- Patryk Chaber
This work presents preliminary results of research concerned with a fast nonlinear Model Predictive Control (MPC) algorithm implemented in an embedded system. In order to obtain a computationally efficient solution, a linear approximation of the predicted trajectory of the controlled variables is calculated for each sampling instant on-line which leads to a quadratic optimisation problem. Furthermore, the matrix of derivatives, which defines the linearised trajectory, is not determined analytically, but it is calculated (approximated) by a specially trained neural network. In order to show effectiveness of the discussed approach, a dynamic process with two inputs and two outputs is considered for which not only simulation results, but also results of real experiments performed in an embedded system based on a microcontroller are given.
- Record ID
- Publication size in sheets
- Bartoszewicz Andrzej, Andrzej Bartoszewicz Kabziński Jacek, Jacek Kabziński Kacprzyk Janusz Janusz Kacprzyk (eds.): Advanced, Contemporary Control Proceedings of KKA 2020—The 20th Polish Control Conference, Łódź, Poland, 2020, Springer, 519 p., ISBN 978-3-030-50936-1. DOI:10.1007/978-3-030-50936-1 Opening in a new tab
- Keywords in English
- Embedded systems Microcontrollers Model Predictive Control Neural networks Nonlinear control
- DOI:10.1007/978-3-030-50936-1_89 Opening in a new tab
- https://link.springer.com/chapter/10.1007/978-3-030-50936-1_89 Opening in a new tab
- (en) English
- Score (nominal)
- Score source
- = 20.0, 09-06-2021, ChapterFromConference
- Uniform Resource Identifier
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