Optimization-Based Carefree Clearance : An Optimization-Based Approach to Identifying Worst-Case Manoeuvres for Fighter Aircraft

Sammanfattning

The development of advanced Flight Control Systems (FCSs) is continuously progressing at a rapid pace. Originally consisting of purely mechanical functions for the deflection of control surfaces, the transition to Fly-By-Wire technology allowed for the inclusion of highly automatized algorithms within the control system. However, for such complex systems follows a rigorous validation and verification process to ensure safe and reliable flight. In the clearance of its control laws, the FCS must be tested for all possible uncertainties and manoeuvres, resulting in a lengthy and costly process, not least for fighter aircraft with the additional requirement of carefree handling. The demand for efficient and comprehensive tools drives the effort of this thesis, which explores the use of optimization, specifically multi-modal Genetic Algorithms for identification of diverse worst-case manoeuvres. Against the traditional methodology which uses gridding of flight conditions and a set of predefined manoeuvres for assessing clearance, the optimization-based methods were consistently able to find manoeuvres resulting in more problematic outcomes.