A neural network-based simultaneous state and actuator fault estimation under unknown input decoupling

2016 ◽  
Author(s):  
Piotr Witczak ◽  
Krzysztof Patan ◽  
Marcin Witczak ◽  
Marcin Pazera
Keyword(s):  
Neural Network ◽  
Fault Estimation ◽  
Unknown Input ◽  
Actuator Fault

A neural network approach to simultaneous state and actuator fault estimation under unknown input decoupling

2017 ◽  
Vol 250 ◽  
pp. 65-75 ◽  
Author(s):  
Piotr Witczak ◽  
Krzysztof Patan ◽  
Marcin Witczak ◽  
Marcin Mrugalski
Keyword(s):  
Neural Network ◽  
Fault Estimation ◽  
Unknown Input ◽  
Actuator Fault ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals Active fault tolerance control of a wind turbine system using an unknown input observer with an actuator fault

2018 ◽  
Vol 28 (1) ◽  
pp. 69-81 ◽  
Author(s):  
Shanzhi Li ◽  
Haoping Wang ◽  
Abdel Aitouche ◽  
Yang Tian ◽  
Nicolai Christov
Keyword(s):  
Wind Turbine ◽  
Active Fault ◽  
Fault Tolerant ◽  
Lyapunov Theory ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Unknown Input ◽  
Actuator Fault ◽  
Unknown Input Observer ◽  
Wind Turbine System

AbstractThis paper proposes a fault tolerant control scheme based on an unknown input observer for a wind turbine system subject to an actuator fault and disturbance. Firstly, an unknown input observer for state estimation and fault detection using a linear parameter varying model is developed. By solving linear matrix inequalities (LMIs) and linear matrix equalities (LMEs), the gains of the unknown input observer are obtained. The convergence of the unknown input observer is also analysed with Lyapunov theory. Secondly, using fault estimation, an active fault tolerant controller is applied to a wind turbine system. Finally, a simulation of a wind turbine benchmark with an actuator fault is tested for the proposed method. The simulation results indicate that the proposed FTC scheme is efficient.

scholarly journals Estimation of Actuator and System Faults Via an Unknown Input Interval Observer for Takagi–Sugeno Systems

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 61 ◽  
Author(s):  
Citlaly Martínez-García ◽  
Vicenç Puig ◽  
Carlos-M. Astorga-Zaragoza ◽  
Guadalupe Madrigal-Espinosa ◽  
Juan Reyes-Reyes
Keyword(s):  
Lower Bounds ◽  
Observer Design ◽  
Upper And Lower Bounds ◽  
Fault Estimation ◽  
Unknown Input ◽  
Actuator Fault ◽  
Output Noise ◽  
Interval Observer ◽  
Process Disturbances ◽  
Takagi Sugeno

This paper presents a simultaneous state variables and system and actuator fault estimation, based on an unknown input interval observer design for a discrete-time parametric uncertain Takagi–Sugeno system under actuator fault, with disturbances in the process and measurement noise. The observer design is synthesized by considering unknown but bounded process disturbances, output noise, as well as bounded parametric uncertainties. By taking into account these considerations, the upper and lower bounds of the considered faults are estimated. The gain of the unknown input interval observer is computed through a linear matrix inequalities (LMIs) approach using the robust H ∞ criteria in order to ensure attenuation of process disturbances and output noise. The interval observer scheme is experimentally evaluated by estimating the upper and lower bounds of a torque load perturbation, a friction parameter and a fault in the input voltage of a permanent magnet DC motor.

Parameter-dependent actuator fault estimation for vehicle active suspension systems based on RBFNN

2021 ◽  
pp. 095440702199301
Author(s):  
Wenping Xue ◽  
Pan Jin ◽  
Kangji Li
Keyword(s):  
Fault Tolerant ◽  
External Disturbance ◽  
Active Suspension ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Actuator Fault ◽  
Mass Variation ◽  
Fe Method ◽  
Comparison Results ◽  
Parameter Dependent

The actuator fault estimation (FE) problem is addressed in this study for the quarter-car active suspension system (ASS) with consideration of the sprung mass variation. Firstly, the ASS is modeled as a parameter-dependent system with actuator fault and external disturbance input. Then, a parameter-dependent FE observer is designed by using the radial basis function neural network (RBFNN) to approximate the actuator fault. In addition, the design conditions are turned into a linear matrix inequality (LMI) problem which can be easily solved with the aid of LMI toolbox. Finally, simulation and comparison results are given to show the accuracy and rapidity of the proposed FE method, as well as good adaptability against the sprung mass variation. Moreover, a simple FE-based active fault-tolerant control (AFTC) strategy is provided to further demonstrate the effectiveness and applicability of the proposed FE method.

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