Experimental Test of a Two-Stage Kalman Filter for Actuator Fault Detection and Diagnosis of an Unmanned Quadrotor Helicopter

Amoozgar, Mohammad Hadi; Chamseddine, Abbas; Zhang, Youmin

doi:10.1007/s10846-012-9757-7

Cited by 142 publications

(54 citation statements)

References 14 publications

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“…Control signal can be obtained by solving this quadratic-optimization problem, with the allusion that both system state (bias) and control input energy are penalized in (7). A commonly-used approach to address the quadratic optimization issue in (6-7) is to construct a Hamilton function, or in Matlab a function named as "lqr" has been defined [10], via which after specifying weight matrices Q and R , the LQR optimal solution could be found.…”

Section: Lqr-based Attitude Angles Execution and Position Trackingmentioning

confidence: 99%

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Position and heading angle control of an unmanned quadrotor helicopter using LQR method

Dong

et al. 2015

2015 34th Chinese Control Conference (CCC)

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This paper discusses the position and heading-angle control for an unmanned quadrotor helicopter. Nonlinear and decoupled linearized dynamics of the vehicle is presented. Linear Quadratic Regulator (LQR) method is briefly introduced, and based on which vehicle attitude angles and position command execution laws are constructed. Using the decoupled dynamics of vehicle, position tracking signal is transformed into the form of pitch/roll angles command input, and could be performed by adopting the attitude angles to be executed as inner loop of the control framework. Performance of the control framework is tested using a real vehicle for height and heading angle control, and corresponding results are presented and discussed.

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Section: Lqr-based Attitude Angles Execution and Position Trackingmentioning

confidence: 99%

“…Specifically, topics of Fault Detection and Diagnosis (FDD), Fault Tolerant Control (FTC), and vehicles formation control have been discussed, see Ref. [5][6][7][8]. The Quadrotor system at NAVL adopts the Qball-X4 vehicle (see Fig.…”

Section: Introductionmentioning

confidence: 99%

Position and heading angle control of an unmanned quadrotor helicopter using LQR method

Dong

et al. 2015

2015 34th Chinese Control Conference (CCC)

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“…Amoozgar et al [15] addressed the problem of Fault Detection and Diagnosis (FDD) of a quadrotor helicopter system in the presence of actuator faults. A TSKF is used to simultaneously estimate and isolate possible faults in each actuator.…”

Section: Introductionmentioning

confidence: 99%

Actuator Fault Diagnosis in a Boeing 747 Model via Adaptive Modified Two-Stage Kalman Filter

Çalışkan

Zhang

et al. 2014

International Journal of Aerospace Engineering

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An adaptive modified two-stage linear Kalman filtering algorithm is utilized to identify the loss of control effectiveness and the magnitude of low degree of stuck faults in a closed-loop nonlinear B747 aircraft. Control effectiveness factors and stuck magnitudes are used to quantify faults entering control systems through actuators. Pseudorandom excitation inputs are used to help distinguish partial loss and stuck faults. The partial loss and stuck faults in the stabilizer are isolated and identified successfully.

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“…The partial loss and stuck faults in the stabilizer are identified successfully. Amoozgar et al (2013) addressed the problem of Fault Detection and Diagnosis (FDD) of a quadrotor helicopter system in the presence of actuator faults. TSKF is used to simultaneously estimate and isolate possible faults in each actuator.…”

Section: Introductionmentioning

confidence: 99%

Sensor and Actuator FDI Applied to an UAV Dynamic Model

Çalışkan

Hajiyev

2014

IFAC Proceedings Volumes

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Experimental Test of a Two-Stage Kalman Filter for Actuator Fault Detection and Diagnosis of an Unmanned Quadrotor Helicopter

Cited by 142 publications

References 14 publications

Position and heading angle control of an unmanned quadrotor helicopter using LQR method

Position and heading angle control of an unmanned quadrotor helicopter using LQR method

Actuator Fault Diagnosis in a Boeing 747 Model via Adaptive Modified Two-Stage Kalman Filter

Sensor and Actuator FDI Applied to an UAV Dynamic Model

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