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

Research output: Contribution to conference › Paper › Academic

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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.

Original language	English
Pages	2117-2124
Publication status	Published - Sep 2020
Event	29th International Conference on Noise and Vibration Engineering, ISMA 2020 and 8th International Conference on Uncertainty in Structural Dynamics, USD 2020 - Leuven, Belgium Duration: 7 Sep 2020 → 9 Sep 2020

Conference

Conference	29th International Conference on Noise and Vibration Engineering, ISMA 2020 and 8th International Conference on Uncertainty in Structural Dynamics, USD 2020
Abbreviated title	ISMA 2020
Country	Belgium
City	Leuven
Period	7/09/20 → 9/09/20

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@conference{79468bde2b494c5096469700598299d1,

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",

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",

}

Floren, M, Oomen, TAE & Noël, J-P 2020, 'A data-driven model predictive control approach toward feedback linearization of nonlinear mechanical systems', Paper presented at 29th International Conference on Noise and Vibration Engineering, ISMA 2020 and 8th International Conference on Uncertainty in Structural Dynamics, USD 2020, Leuven, Belgium, 7/09/20 - 9/09/20 pp. 2117-2124.

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

2020. 2117-2124 Paper presented at 29th International Conference on Noise and Vibration Engineering, ISMA 2020 and 8th International Conference on Uncertainty in Structural Dynamics, USD 2020, Leuven, Belgium.

Research output: Contribution to conference › Paper › Academic

TY - CONF

T1 - A data-driven model predictive control approach toward feedback linearization of nonlinear mechanical systems

AU - Floren, Merijn

AU - Oomen, Tom A.E.

AU - Noël, Jean-Philippe

PY - 2020/9

Y1 - 2020/9

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.

M3 - Paper

SP - 2117

EP - 2124

T2 - 29th International Conference on Noise and Vibration Engineering, ISMA 2020 and 8th International Conference on Uncertainty in Structural Dynamics, USD 2020

Y2 - 7 September 2020 through 9 September 2020

ER -