Fikret Caliskan

Sensor and control surface/actuator failure detection and isolation applied to F‐16 flight dynamic

Purpose – The purpose of the paper is to present an approach to detect and isolate the aircraft sensor and control surface/actuator failures affecting the mean of the Kalman filter innovation sequence.Design/methodology/approach – The extended Kalman filter (EKF) is developed for nonlinear flight dynamic estimation of an F‐16 fighter and the effects of the sensor and control surface/actuator failures in the innovation sequence of the designed EKF are investigated. A robust Kalman filter (RKF) is very useful to isolate the control surface/actuator failures and sensor failures. The technique for control surface detection and identification is applied to an unstable multi‐input multi‐output model of a nonlinear AFTI/F‐16 fighter. The fighter is stabilized by means of a linear quadratic optimal controller. The control gain brings all the eigenvalues that are outside the unit circle, inside the unit circle. It also keeps the mechanical limits on the deflections of control surfaces. The fighter has nine state v...

Sensor/actuator fault diagnosis based on statistical analysis of innovation sequence and Robust Kalman Filtering

In this paper, an approach to detect and isolate the aircraft sensor/actuator faults affecting the mean of the Kalman filter innovation sequence is presented. The effects of the sensor and actuator faults in the innovation process of the channels are investigated, and a decision approach to isolate the sensor and actuator faults is proposed. When a Kalman filter is used, the decision statistics change regardless of whether the fault is in the sensors or in the actuators, whilst when a Robust Kalman Filter (RKF) is used, it is easy to distinguish the sensor and actuator faults. A novel feature of this diagnostic method is that the innovation sequence based fault isolation algorithm has been presented and hence, the sensor/actuator fault detection and isolation problem has been solved. The categories (or classes) of the likely faults are not demanded. The statistical characteristics of the system are not required to be known after the fault has occurred. In the simulations, the longitudinal dynamics of an aircraft control system are considered, and the detection and isolation of pitch rate gyro faults and actuator faults affecting the mean of the innovation sequence are examined.

Innovation sequence application to aircraft sensor fault detection: comparison of checking covariance matrix algorithms

In this paper, the algorithms verifying the covariance matrix of the Kalman filter innovation sequence are compared with respect to detected minimum fault rate and detection time. Four algorithms are dealt with; the algorithm verifying the trace of the covariance matrix of the innovation sequence, the algorithm verifying the sum of all elements of the inverse covariance matrix of the innovation sequence, the optimal algorithm verifying the ratio of two quadratic forms of which matrices are theoretic and selected covariance matrices of Kalman filter innovation sequence, and the algorithm verifying the generalized variance of the covariance matrix of the innovation sequence. The algorithms are implemented for longitudinal dynamics of an aircraft to detect sensor faults, and some suggestions are given on the use of the algorithms in flight control systems.

EKF based surface fault detection and reconfiguration in aircraft control systems

In this paper, a surface fault detection algorithm based on the extended Kalman filter (EKF) is presented. The innovation, the difference between the actual plant states, and the plant states identified by the EKF are evaluated for the fault detection and identification, and the control reconfiguration procedure is executed by considering the identified control distribution matrix.

Kalman filter and neural network‐based icing identification applied to A340 aircraft dynamics

Purpose – The purpose of this paper is to maintain safe flight and to improve existing deicing (in‐flight removal of ice) and anti‐icing (prevention of ice accretion) systems under in‐flight icing conditions.Design/methodology/approach – A recent academic research on aircraft icing phenomenon is presented. Several wind tunnel tests of an experimental aircraft provided by NASA are used in the neural network training. Five ice‐affected parameters are chosen in the light of these experiments and researches. An offline artificial neural network is used as an identification technique. The Kalman filter is used to increase the state measurements accuracy such that neural network training performance gets better. A linear A340 dynamic model is selected in cruise conditions. This linear model is simulated in time varying manner in terms of changing icing parameters in a system dynamic matrix. The obtained data are used in neural network training and testing.Findings – Airframe icing can grow in many ways and man...

Actuator failure detection and reconfigurable control for F-16 aircraft model

Abstract In this paper, an approach to actuator failure detection and reconfigurable control for flight control system is presented. The effects of the actuator failures in the innovation process of the Kalman filter are investigated, and a reconfiguration in control law is proposed. In the simulations, the longitudinal and lateral dynamics of an F-16 aircraft model is considered, and actuator failure detection and reconfigurable control are examined.

Fault detection in flight control systems based on the generalized variance of the Kalman filter innovation sequence

For multidimensional dynamic systems a sensor fault detection algorithm based on the confidence interval of the generalized variance of the Kalman filter innovation sequence, is presented. The longitudinal dynamics of an aircraft control system, as an example, is considered, and detection of the fault in the air velocity sensor affecting the covariance matrix of the Kalman filter is examined.

Aircraft Sensor and Actuator Fault Detection, Isolation, and Accommodation

The purpose of the paper is to present an approach to detect, isolate, and accommodate the aircraft sensor and actuator faults using unknown input observers (UIOs). Full-order observers, reduced-order observers, and UIOs are widely used in state estimations. After the estimation of states, fault detection can be provided by conducting residual analysis. Despite of the existence of unknown inputs, fault detection and isolation are implemented for a very large, four-engined, cargo jet aircraft model. Sensor accommodation is realized via switching under redundant sensor existence assumption. Actuator accommodation is provided by gain scheduling. Hence, if a fault occurs in an actuator corresponding to the control surfaces, the remainder ( n−1 ) actuators are used to avoid hazardous flight. Sensor and actuator faults are detected by using residuals. Sensor faults are effective on the outputs, while actuator faults are effective on the state equations. Fault isolation is implemented by taking into account that...

Active Fault-Tolerant Control of UAV Dynamics against Sensor-Actuator Failures

AbstractIn this paper, an active fault-tolerant flight control system against sensor/actuator failures for unmanned aerial vehicles (UAVs) is proposed. First, an approach to detecting and isolating UAV sensor failures affecting the mean of the Kalman filter (KF) innovation sequence is proposed. Second, an adaptive two-stage linear Kalman filtering algorithm is used to isolate the sensor and actuator faults and to estimate the loss of control effectiveness and the magnitude of degree of stuck faults in a UAV model. Control effectiveness factors and stuck magnitudes are used to quantify the faults entering the UAV flight control system through actuators. In the case of a sensor fault, the faulty sensor is isolated, and the KF that ignores the feedback from the faulty sensor is built. If the fault is an actuator fault, then the actuator fault isolation and identification are performed using the adaptive two-stage KF. The parameters of the feedback controller are tuned by the control reconfiguration procedure...

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

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.