<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Model predictive control (MPC) algorithms are widely used in practical applications. They are usually formulated as optimization problems. If a model used for prediction is linear (or linearized on-line), then the optimization problem is a standard, i.e., quadratic, one. Otherwise, it is a nonlinear, in general, nonconvex optimization problem. In the latter case, numerical problems may occur during solving this problem, and the time needed to calculate control signals cannot be determined. Therefore, approaches based on linear or linearized models are preferred in practical applications. A novel, fuzzy, numerically efficient MPC algorithm is proposed in the paper. It can offer better performance than the algorithms based on linear models, and very close to that of the algorithms based on nonlinear optimization. Its main advantage is the short time needed to calculate the control value at each sampling instant compared with optimization-based numerical algorithms; it is a combination of analytical and numerical versions of MPC algorithms. The efficiency of the proposed approach is demonstrated using control systems of two nonlinear control plants: the first one is a chemical CSTR reactor with a van de Vusse reaction, and the second one is a pH reactor.</p>
</abstract>

Model predictive control (MPC) algorithms are widely used in practical applications. They are usually formulated as optimization problems. If a model used for prediction is linear (or linearized on-line), then the optimization problem is a standard, i.e., quadratic, one. Otherwise, it is a nonlinear, in general, nonconvex optimization problem. In the latter case, numerical problems may occur during solving this problem, and the time needed to calculate control signals cannot be determined. Therefore, approaches based on linear or linearized models are preferred in practical applications. A novel, fuzzy, numerically efficient MPC algorithm is proposed in the paper. It can offer better performance than the algorithms based on linear models, and very close to that of the algorithms based on nonlinear optimization. Its main advantage is the short time needed to calculate the control value at each sampling instant compared with optimization-based numerical algorithms; it is a combination of analytical and numerical versions of MPC algorithms. The efficiency of the proposed approach is demonstrated using control systems of two nonlinear control plants: the first one is a chemical CSTR reactor with a van de Vusse reaction, and the second one is a pH reactor.

A numerically efficient fuzzy MPC algorithm with fast generation of the control signal

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Model predictive control (MPC) algorithms are widely used in practical applications. They are usually formulated as optimization problems. If a model used for...

A numerically efficient fuzzy MPC algorithm with fast generation of the control signal

Publié en ligne: 03 avr. 2021

Pages: 59 - 71

Reçu: 31 déc. 2019

Accepté: 05 oct. 2020

DOI: https://doi.org/10.34768/amcs-2021-0005

Mots clés
model predictive control, fuzzy systems, fuzzy control, nonlinear control

© 2021 Piotr M. Marusak, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A numerically efficient fuzzy MPC algorithm with fast generation of the control signal

Publié en ligne: 03 avr. 2021

Pages: 59 - 71

Reçu: 31 déc. 2019

Accepté: 05 oct. 2020

DOI: https://doi.org/10.34768/amcs-2021-0005

Mots clésmodel predictive control, fuzzy systems, fuzzy control, nonlinear control

© 2021 Piotr M. Marusak, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
model predictive control, fuzzy systems, fuzzy control, nonlinear control