Identification of multivariable high-performance turbofan engine dynamics from closed loop data

Abstract

A typical engine control design cycle consists of developing a dynamic engine simulation from steady-state component performance data, designing a control based upon this simulation, and then testing and modifying the control in an engine test cell to meet performance requirements. This design cycle was successful for state-of-the-art engines. However, for more advanced multivariable engines that exhibit strong variable interactions, this procedure will result in substantial trial and error modification of the control during the testing phase. One method to automate the design process and reduce control modification testing and development cost would be to identify accurate dynamic models directly from the closed-loop test data. These identified models would then be used in conjunction with a synthesis procedure to systematically refine the control. Recent advances in closed-loop identifiability present a methodology for this direct identification of engine model dynamics from closed-loop test data. The application of an identification method to simulated and actual closed-loop F100 engine data is described. This study was undertaken to determine if useful dynamic engine models could be identified directly from closed-loop engine test data.