IMU Sensor Fault Diagnosis and Estimation for Quadrotor UAVs

Avram, Remus C.; Zhang, Xiaodong; Campbell, Jacob; Muse, Jonathan A.

doi:10.1016/j.ifacol.2015.09.556

Cited by 54 publications

(24 citation statements)

References 9 publications

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“…Marzat J. and Avram R.C. et al proposed sensor fault detection and isolation algorithms using control model information [11] and sliding mode observer [12], respectively. The evaluation and performance of model-based fault detection and isolation algorithms always depend on the accuracy of the system model used.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

Geng

Чулин

Wang

2020

Sensors

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The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. This paper determines the conditions for model establishment of a single-track 3-DOF vehicle dynamics model and then performs Taylor expansion for modeling linearization. On the basis of that, a novel fault-tolerant model predictive control algorithm (FTMPC) is proposed for robust path tracking control of autonomous vehicle. First, the linear time-varying model predictive control algorithm for lateral motion control of vehicle is designed by constructing the objective function and considering the front wheel declination and dynamic constraint of tire cornering. Then, the motion state information obtained by multi-sensory perception systems of vision, GPS, and LIDAR is fused by using an improved weighted fusion algorithm based on the output error variance. A novel fault signal detection algorithm based on Kalman filtering and Chi-square detector is also designed in our work. The output of the fault signal detector is a fault detection matrix. Finally, the fault signals are isolated by multiplication of signal matrix, fault detection matrix, and weight matrix in the process of data fusion. The effectiveness of the proposed method is validated with simulation experiment of lane changing path tracking control. The comparative analysis of simulation results shows that the proposed method can achieve the expected fault-tolerant performance and much better path tracking control performance in case of sensor failure.

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Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

Geng

Чулин

Wang

2020

Sensors

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“…Moreover, the aerodynamic coefficients highly depend on the specific aircraft structure and flight envelope, thus the corresponding FDI and estimation methods might fail in any unexpected condition [16]. In contrast, the second category adopts an aerodynamicsindependent model, e.g., the wind velocity triangle [17]- [19], the aircraft dynamic model with three-axis load factors as inputs [20]- [22], or a combination of the above two models [16], [23]. Such aerodynamics-independent models simplify the design of FDI and estimation algorithms by avoiding the use of uncertain aerodynamics, hence the corresponding algorithms can be easily configured for different aircraft without adapting to the changing aerodynamics [16], [20].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Fault-Tolerant Moving Horizon Air Data Estimation for the RECONFIGURE Benchmark

Wan

Keviczky

2019

IEEE Trans. Contr. Syst. Technol.

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“…Based on fault estimation, FTC controller is reconfigured to compensate or reject faults [29,30]. For the quadrotor UAV, there have been a great deal of research [31,32]. For example, considering disturbances of unknown bounds, Chang et al [33] propose an adaptive sliding mode algorithm to deal with bias fault and noise.…”

Section: Introductionmentioning

confidence: 99%

“…(1) For the quadrotor UAV, different from the sensor fault research [31][32][33][34][35], this paper addresses the diverse sensor faults (three additive and one multiplicative faults), and simultaneously considers uncertain parameters, external disturbances, and measurement noises. (2) Different from the adaptive backstepping methods for special second-order system in [19,20], the proposed approach can be used for different kinds of systems besides quadrotor UAV.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy adaptive nonlinear sensor‐fault tolerant control for a quadrotor unmanned aerial vehicle

Cao

Zhou

et al. 2018

Asian Journal of Control

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In this paper, a novel fuzzy adaptive nonlinear fault tolerant control design scheme is proposed for attitude dynamics of quadrotor UAV subjected to four sensor faults (bias, drift, loss of accuracy, loss of effectiveness). The sensor faults in Euler angle loop are transformed equivalently into a mismatched uncertainty vector, and other unknown items involving faults, uncertain parameters and external disturbances in angular velocity loop are lumped into an unknown nonlinear function vector.Fuzzy logic systems with adaptive parameters are used to approximate the mismatched uncertainty and lumped nonlinear function vectors. Dynamic surface control is applied to design the fault tolerant controller, and sliding mode control is introduced to improve the control accuracy. All signals of the closed-loop control system are proved to be semi-global uniformly ultimately bounded. Simulations demonstrate the effectiveness of the proposed approach for sensor faults. KEYWORDS dynamic surface control, fault tolerant control, fuzzy logic system, Quadrotor unmanned aerial vehicle, sensor fault, sliding mode control

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IMU Sensor Fault Diagnosis and Estimation for Quadrotor UAVs

Cited by 54 publications

References 9 publications

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

Real-Time Fault-Tolerant Moving Horizon Air Data Estimation for the RECONFIGURE Benchmark

Fuzzy adaptive nonlinear sensor‐fault tolerant control for a quadrotor unmanned aerial vehicle

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