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

OpenAlex · English

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 variables and six control inputs. Findings In the simulations, the longitudinal and lateral dynamics of an F‐16 aircraft dynamic model are considered, and the sensor and control surface/actuator failures are detected and isolated. Research limitations/implications A real‐time detection of sensor and control surface/actuator failures affecting the mean of the innovation process applied to the linearized F‐16 fighter flight dynamic is examined and an effective approach to isolate the sensor and control surface/actuator failures is proposed. The nonlinear F‐16 model is linearized. Failures affecting the covariance of the innovation sequence is not considered in the paper. Originality/value An approach has been proposed to detect and isolate the aircraft sensor and control surface/actuator failures occurred in the aircraft control system. An extended Kalman filter has been developed for the nonlinear flight dynamic estimation of an F‐16 fighter. Failures in the sensors and control surfaces/actuators affect the characteristics of the innovation sequence of the EKF. The failures that affect the mean of the innovation sequence have been considered. When the EKF is used, the decision statistics changes regardless the fault is in the sensors or in the control surfaces/actuators, while a RKF is used, it is easy to distinguish the sensor and control surface/actuator faults.