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

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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 languageEnglish
Pages2117-2124
Publication statusPublished - Sep 2020
Event29th 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 20209 Sep 2020

Conference

Conference29th International Conference on Noise and Vibration Engineering, ISMA 2020 and 8th International Conference on Uncertainty in Structural Dynamics, USD 2020
Abbreviated titleISMA 2020
CountryBelgium
CityLeuven
Period7/09/209/09/20

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