<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Classical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex dynamics. In particular, the control horizon, which determines the number of decision variables optimised on-line at each sampling instant, is crucial since it significantly affects computational complexity. This work discusses a nonlinear MPC algorithm with on-line trajectory linearisation, which makes it possible to formulate a quadratic optimisation problem, as well as parameterisation using Laguerre functions, which reduces the number of decision variables. Simulation results of classical (not parameterised) MPC algorithms and some strategies with parameterisation are thoroughly compared. It is shown that for a benchmark system the MPC algorithm with on-line linearisation and parameterisation gives very good quality of control, comparable with that possible in classical MPC with long horizons and nonlinear optimisation.</p></abstract>

Classical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex dynamics. In particular, the control horizon, which determines the number of decision variables optimised on-line at each sampling instant, is crucial since it significantly affects computational complexity. This work discusses a nonlinear MPC algorithm with on-line trajectory linearisation, which makes it possible to formulate a quadratic optimisation problem, as well as parameterisation using Laguerre functions, which reduces the number of decision variables. Simulation results of classical (not parameterised) MPC algorithms and some strategies with parameterisation are thoroughly compared. It is shown that for a benchmark system the MPC algorithm with on-line linearisation and parameterisation gives very good quality of control, comparable with that possible in classical MPC with long horizons and nonlinear optimisation.

Nonlinear Model Predictive Control for Processes with Complex Dynamics: A Parameterisation Approach Using Laguerre Functions

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Classical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex dynamics. In particular, the control...

Nonlinear Model Predictive Control for Processes with Complex Dynamics: A Parameterisation Approach Using Laguerre Functions

Publié en ligne: 03 avr. 2020

Pages: 35 - 46

Reçu: 14 juin 2019

Accepté: 18 oct. 2019

DOI: https://doi.org/10.34768/amcs-2020-0003

Mots clés
process control, nonlinear model predictive control, Laguerre functions, linearisation

© 2020 Maciej Ławryńczuk, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Nonlinear Model Predictive Control for Processes with Complex Dynamics: A Parameterisation Approach Using Laguerre Functions

Publié en ligne: 03 avr. 2020

Pages: 35 - 46

Reçu: 14 juin 2019

Accepté: 18 oct. 2019

DOI: https://doi.org/10.34768/amcs-2020-0003

Mots clésprocess control, nonlinear model predictive control, Laguerre functions, linearisation

© 2020 Maciej Ławryńczuk, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
process control, nonlinear model predictive control, Laguerre functions, linearisation