A data-driven model predictive control approach toward feedback linearization of nonlinear mechanical systems

Merijn Floren; Tom A.E. Oomen; Jean-Philippe Noël

A data-driven model predictive control approach toward feedback linearization of nonlinear mechanical systems

Merijn Floren, Tom A.E. Oomen, Jean-Philippe Noël

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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This paper presents a novel approach to linearize the input-output (IO) response of nonlinear mechanical systems by using model predictive control (MPC) with integral action and solving a one-step-ahead reference tracking problem. The discrete-time MPC controller, which builds on a nonlinear data-driven state-space model, controls a continuous-time plant. State estimation is performed by means of the unscented Kalman filter (UKF). The overall effectiveness of this MPC-based approach is validated in the time and frequency domains by conducting simulations on a mechanical system with output nonlinearities of polynomial type.
The obtained results show that the proposed method is superior in terms of performance and robustness when compared to the classical feedback linearization techniques based on Lie algebra.

Originele taal-2	Engels
Titel	Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics
Redacteuren	W. Desmet, B. Pluymers, D. Moens, S. Vandemaele
Uitgeverij	Katholieke Universiteit Leuven
Pagina's	2117-2124
Aantal pagina's	8
ISBN van elektronische versie	9789082893113
Status	Gepubliceerd - sep. 2020
Evenement	29th International Conference on Noise and Vibration Engineering, ISMA 2020 and 8th International Conference on Uncertainty in Structural Dynamics, USD 2020 - Leuven, België Duur: 7 sep. 2020 → 9 sep. 2020

Congres

Congres	29th International Conference on Noise and Vibration Engineering, ISMA 2020 and 8th International Conference on Uncertainty in Structural Dynamics, USD 2020
Verkorte titel	ISMA 2020
Land/Regio	België
Stad	Leuven
Periode	7/09/20 → 9/09/20

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Floren, M., Oomen, T. A. E., & Noël, J-P. (2020). A data-driven model predictive control approach toward feedback linearization of nonlinear mechanical systems. In W. Desmet, B. Pluymers, D. Moens, & S. Vandemaele (editors), Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics (blz. 2117-2124). Katholieke Universiteit Leuven.

Floren, Merijn ; Oomen, Tom A.E. ; Noël, Jean-Philippe. / A data-driven model predictive control approach toward feedback linearization of nonlinear mechanical systems. Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics. redacteur / W. Desmet ; B. Pluymers ; D. Moens ; S. Vandemaele. Katholieke Universiteit Leuven, 2020. blz. 2117-2124

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title = "A data-driven model predictive control approach toward feedback linearization of nonlinear mechanical systems",

abstract = "This paper presents a novel approach to linearize the input-output (IO) response of nonlinear mechanical systems by using model predictive control (MPC) with integral action and solving a one-step-ahead reference tracking problem. The discrete-time MPC controller, which builds on a nonlinear data-driven state-space model, controls a continuous-time plant. State estimation is performed by means of the unscented Kalman filter (UKF). The overall effectiveness of this MPC-based approach is validated in the time and frequency domains by conducting simulations on a mechanical system with output nonlinearities of polynomial type.The obtained results show that the proposed method is superior in terms of performance and robustness when compared to the classical feedback linearization techniques based on Lie algebra.",

author = "Merijn Floren and Oomen, {Tom A.E.} and Jean-Philippe No{\"e}l",

year = "2020",

month = sep,

language = "English",

pages = "2117--2124",

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booktitle = "Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics",

publisher = "Katholieke Universiteit Leuven",

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note = "29th International Conference on Noise and Vibration Engineering, ISMA 2020 and 8th International Conference on Uncertainty in Structural Dynamics, USD 2020, ISMA 2020 ; Conference date: 07-09-2020 Through 09-09-2020",

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Floren, M, Oomen, TAE & Noël, J-P 2020, A data-driven model predictive control approach toward feedback linearization of nonlinear mechanical systems. in W Desmet, B Pluymers, D Moens & S Vandemaele (redactie), Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics. Katholieke Universiteit Leuven, blz. 2117-2124, 29th International Conference on Noise and Vibration Engineering, ISMA 2020 and 8th International Conference on Uncertainty in Structural Dynamics, USD 2020, Leuven, België, 7/09/20.

A data-driven model predictive control approach toward feedback linearization of nonlinear mechanical systems. / Floren, Merijn; Oomen, Tom A.E.; Noël, Jean-Philippe.

Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics. redactie / W. Desmet; B. Pluymers; D. Moens; S. Vandemaele. Katholieke Universiteit Leuven, 2020. blz. 2117-2124.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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AU - Oomen, Tom A.E.

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N2 - This paper presents a novel approach to linearize the input-output (IO) response of nonlinear mechanical systems by using model predictive control (MPC) with integral action and solving a one-step-ahead reference tracking problem. The discrete-time MPC controller, which builds on a nonlinear data-driven state-space model, controls a continuous-time plant. State estimation is performed by means of the unscented Kalman filter (UKF). The overall effectiveness of this MPC-based approach is validated in the time and frequency domains by conducting simulations on a mechanical system with output nonlinearities of polynomial type.The obtained results show that the proposed method is superior in terms of performance and robustness when compared to the classical feedback linearization techniques based on Lie algebra.

AB - This paper presents a novel approach to linearize the input-output (IO) response of nonlinear mechanical systems by using model predictive control (MPC) with integral action and solving a one-step-ahead reference tracking problem. The discrete-time MPC controller, which builds on a nonlinear data-driven state-space model, controls a continuous-time plant. State estimation is performed by means of the unscented Kalman filter (UKF). The overall effectiveness of this MPC-based approach is validated in the time and frequency domains by conducting simulations on a mechanical system with output nonlinearities of polynomial type.The obtained results show that the proposed method is superior in terms of performance and robustness when compared to the classical feedback linearization techniques based on Lie algebra.

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Floren M, Oomen TAE, Noël J-P. A data-driven model predictive control approach toward feedback linearization of nonlinear mechanical systems. In Desmet W, Pluymers B, Moens D, Vandemaele S, redacteurs, Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics. Katholieke Universiteit Leuven. 2020. blz. 2117-2124

A data-driven model predictive control approach toward feedback linearization of nonlinear mechanical systems

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