Observer-based sensor fault estimation in nonlinear systems

In this contribution, a fault-tolerant control strategy for the longitudinal dynamics of an autonomous vehicle is presented. The aim is to be able to detect potential failures of the vehicle’s speed sensor and then to keep the vehicle in a safe state. For this purpose, the separation principle, composed of a static output feedback controller and fault estimation observers, is designed. Indeed, two observer techniques were proposed: the proportional and integral observer and the descriptor observer. The effectiveness of the proposed scheme is validated by means of the experimental demonstrator of the VEDECOM (Véhicle Décarboné et Communinicant) Institut.

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“…The proportional and integral fault estimation observer of the system ( 27 ) is described by [ 50 ]:

…”

Section: Methodsmentioning

confidence: 99%

Proprioceptive Sensors’ Fault Tolerant Control Strategy for an Autonomous Vehicle

Boukhari

Chaibet

Boukhnifer

et al. 2018

Sensors

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“…Ke Zhang and Cocquempot (2016) proposed a less conservative UIO design method by using a finite frequency range technique instead of an entirefrequency method. In Valibeygi et al (2016) estimation of sensor faults in non-linear systems in the presence of process disturbance was considered. The unknown input observer for Lipschitz systems was applied for the fault diagnosis in Witczak et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Robust fault estimation of a blade pitch and drivetrain system in wind turbine model

Ziyabari

Shoorehdeli

Karimirad

2020

Journal of Vibration and Control

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In this article, a novel robust fault estimation scheme to ensure efficient and reliable operation of wind turbines has been presented. Wind turbines are complex systems with large flexible structures that work under very turbulent and unpredictable environmental conditions for a variable electrical grid. The proposed observer-based estimation scheme consists of a set of possible faults affecting the dynamics, sensors, and actuators of wind turbines. First, the pitch and drivetrain system faults occur simultaneously with process and sensor disturbances that are called unknown input signals. Second, through a series of coordinate transformations, the faulty subsystem is decoupled from the rest of the system. The first subsystem is affected by unknown inputs, and the second one is affected by faults. A reduced-order unknown input observer is designed to reconstruct states accurately, whereas a reduced-order sliding mode observer is designed for the second subsystem such that it is robust against unknown inputs and faults. Moreover, the reduced-order unknown input observer guarantees the asymptotic stability of the error dynamics using the Lyapunov theory method and completely removes unknown inputs; on the other hand, the reduced-order sliding mode observer is designed to reconstruct faults for the faulty subsystem accurately. Until now, authors only focused on an unknown input signal in the dynamics of the system, especially in nonlinear systems. The estimated fault will be adequate to accommodate the control loop, and sufficient conditions are developed to guarantee the stability of the state estimation error. In the next step, to figure the effectiveness of the proposed approach, a wind turbine benchmark system model is considered with faults and unknown inputs scenarios. The simulation results are used to validate the robustness of the proposed algorithms under noise conditions, and the results show that the algorithm could classify faults robustly.

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“…Popular FE approaches have been developed in a precise and effective way for nonlinear systems, where fault is modelled as additive changes appearing in actuators or sensors [14][15][16][17][18]. The major drawback of the preceding approaches resides primarily on treating actuator and sensor faults with additive terms.…”

Section: Introductionmentioning

confidence: 99%

Multiplicative Fault Estimation‐Based Adaptive Sliding Mode Fault‐Tolerant Control Design for Nonlinear Systems

et al. 2018

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This article deals with the sliding mode fault-tolerant control (FTC) problem for a nonlinear system described under Takagi-Sugeno (T-S) fuzzy representation. In particular, the nonlinear system is corrupted with multiplicative actuator faults, process faults, and uncertainties. We start by constructing the separated FTC design to ensure robust stability of the closed-loop nonlinear system. First, we propose to conceive an adaptive observer in order to estimate nonlinear system states, as well as robust multiplicative fault estimation. The novelty of the proposed approach is that the observer gains are obtained by solving the multiobjective linear matrix inequality (LMI) optimization problem. Second, an adaptive sliding mode controller is suggested to offer a solution to stabilize the closed-loop system despite the occurrence of real fault effects. Compared with the separated FTC, this paper provides an integrated sliding mode FTC in order to achieve an optimal robustness interaction between observer and controller models. Thus, in a single-step LMI formulation, sufficient conditions are developed with multiobjective optimization performances to guarantee the stability of the closed-loop system. At last, nonlinear simulation results are given to illustrate the effectiveness of the proposed FTC to treat multiplicative faults.

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Observer-based sensor fault estimation in nonlinear systems

Cited by 18 publications

References 30 publications

Proprioceptive Sensors’ Fault Tolerant Control Strategy for an Autonomous Vehicle

Proprioceptive Sensors’ Fault Tolerant Control Strategy for an Autonomous Vehicle

Robust fault estimation of a blade pitch and drivetrain system in wind turbine model

Multiplicative Fault Estimation‐Based Adaptive Sliding Mode Fault‐Tolerant Control Design for Nonlinear Systems

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