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Observer-Based Fault-Tolerant Predictive Control for LPV Systems with Sensor Faults: An Active Car Suspension Application

Abstract : In this paper, an observer-based robust fault-tolerant predictive control (ORFTPC) strategy is proposed for Linear Parameter-Varying (LPV) systems subject to input constraints and sensor failures. The main objective of this work is to establish a real observer based on a virtual observer to be used to estimate both states and sensor failures of the system. The proposed virtual observer is employed to improve the observation precision and reduce the impacts of the sensor faults and the external disturbances in the LPV systems. In addition, a real observer is proposed to overcome the virtual observer margins and to ensure that all states and sensor faults of the system are properly estimated, without the need for any fault isolation modules. The proposed solution demonstrates that, using both observers, a robust fault-tolerant predictive control is established via the Lyapunov function. Moreover, sufficient stability conditions are derived using the Lyapunov approach for the convergence of the proposed robust controller. Furthermore, the proposed approach simultaneously computes the gains of the real observer and the controller from a linear matrix inequality (LMI), which is deduced from the estimation errors. Finally, the performance of the proposed approach is investigated by a simulation example of a quarter-vehicle model, and the simulation results under a sensor fault illustrate the robustness and performance of the proposed method.
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https://hal.archives-ouvertes.fr/hal-03540573
Contributor : Frédéric Davesne Connect in order to contact the contributor
Submitted on : Monday, January 24, 2022 - 10:09:11 AM
Last modification on : Wednesday, January 26, 2022 - 3:33:47 AM

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Abdelaziz Abboudi, Sofiane Bououden, Mohammed Chadli, Ilyes Boulkaibet, Bilel Neji. Observer-Based Fault-Tolerant Predictive Control for LPV Systems with Sensor Faults: An Active Car Suspension Application. Applied Sciences, MDPI, 2022, 12 (2), pp.684. ⟨10.3390/app12020684⟩. ⟨hal-03540573⟩

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