Implementation of nonlinear model predictive control of magnetic levitation laboratory plant

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F25%3A39923066" target="_blank" >RIV/00216275:25530/25:39923066 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s40435-025-01878-1" target="_blank" >https://link.springer.com/article/10.1007/s40435-025-01878-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s40435-025-01878-1" target="_blank" >10.1007/s40435-025-01878-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Implementation of nonlinear model predictive control of magnetic levitation laboratory plant

Popis výsledku v původním jazyce

Nowadays, predictive control methods are very popular and are often used to control various systems. Linear predictive control techniques are relatively well discovered and standardised, but this is not true for predictive control methods used for nonlinear systems, which are far more frequent in nature. There is no universal approach to handle it. Nonlinear model predictive control (NMPC) is an extension of the linear model predictive control (MPC) method, which is widely used for solving nonlinear control problems. In this paper, a real-time adaptation of linear MPC for nonlinear systems is presented. This method uses nonlinear model dynamics with the advantage to provide a precise prediction of the system response to initial conditions, known future and estimated progress, and an inside-one-step evolving linearised discretised nonlinear model for predicting response to estimated control input deviance used for optimisation. The functionality of the proposed method is shown using MATLAB simulation and demonstrated by experiment on a fast nonlinear magnetic levitation plant. The proposed controller is compared with a simple control loop with PID with better control results of NMPC at the cost of higher computational complexity. In this work, we present the NMPC method that collects many interesting findings from other authors who dealt with predictive controllers and adds our knowledge, which leads to a standardised predictive control method suitable for controlling linear or nonlinear systems with state-space representation.

Název v anglickém jazyce

Implementation of nonlinear model predictive control of magnetic levitation laboratory plant

Popis výsledku anglicky

Nowadays, predictive control methods are very popular and are often used to control various systems. Linear predictive control techniques are relatively well discovered and standardised, but this is not true for predictive control methods used for nonlinear systems, which are far more frequent in nature. There is no universal approach to handle it. Nonlinear model predictive control (NMPC) is an extension of the linear model predictive control (MPC) method, which is widely used for solving nonlinear control problems. In this paper, a real-time adaptation of linear MPC for nonlinear systems is presented. This method uses nonlinear model dynamics with the advantage to provide a precise prediction of the system response to initial conditions, known future and estimated progress, and an inside-one-step evolving linearised discretised nonlinear model for predicting response to estimated control input deviance used for optimisation. The functionality of the proposed method is shown using MATLAB simulation and demonstrated by experiment on a fast nonlinear magnetic levitation plant. The proposed controller is compared with a simple control loop with PID with better control results of NMPC at the cost of higher computational complexity. In this work, we present the NMPC method that collects many interesting findings from other authors who dealt with predictive controllers and adds our knowledge, which leads to a standardised predictive control method suitable for controlling linear or nonlinear systems with state-space representation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Dynamics and Control

ISSN

2195-268X

e-ISSN

2195-2698

Svazek periodika

13

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

16

Strana od-do

nestránkováno

Kód UT WoS článku

001592011000001

EID výsledku v databázi Scopus

2-s2.0-105018700692