Design of robust redundancy relations for a semi-scale YF-22 aircraft model

• Published in: Control engineering practice Vol. 17; no. 7; pp. 773 - 786
• Main Authors: Fravolini, Mario Luca, Campa, Giampiero
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.07.2009; Elsevier
• Subjects: Applied sciences; Computer science; control theory; systems; Control theory. Systems; Exact sciences and technology; Fault diagnosis; Fault isolation; Fundamental areas of phenomenology (including applications); Modelling and identification; Nonlinear filters; Physics; Robotics; Sensors; Solid mechanics; Structural and continuum mechanics; Symbolic computation; Unmanned aerial vehicles; Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...); Fault diagnosis; Fault isolation; Unmanned aerial vehicles; Sensors; Symbolic computation; Nonlinear filters; Software development; Non linear filter; Modeling; Uncertain system; Unmanned aerial vehicle; Measurement error; Actuator; Vibration; Redundancy; Measurement sensor; Neural network; Scale model; Robust control; Scaling law; Finite impulse response filter; Aircraft; Fault diagnostic; Structural analysis; Defect detection; Signal to noise ratio
• ISSN: 0967-0661; 1873-6939
• DOI: 10.1016/j.conengprac.2008.12.006

This paper presents an “ad hoc” methodology for the design of diagnostic software for the detection and isolation of faults on sensors and actuators of a remotely controlled semi scale YF-22 research aircraft. Starting from the structural analysis of the nonlinear dynamic equations of the aircraft, an algorithm, based on the “variables elimination method”, is proposed to compute a set of residual equations having all the possible fault signatures. The quality of each residual equation has been ranked according to a cost function chosen to represent implementation issues such as the sensitivity to measurement noise in the numerical computation of high order derivatives. An algorithm is then proposed for selecting a subset of residual equations with maximum “failure isolability” and minimum cost, according to the selected performance criteria. The issue of robustification of the residual equations to modeling errors and measurement noise has been addressed through nonlinear uncertainty mapping using Neural Networks in conjunction to FIR filters. The fault detection and isolation method has been applied by injecting simulated faults to flight data collected by a semi-scale YF-22 research aircraft model.