Model Reference Adaptive Control of a Turbofan Engine Using Output-Feedback

Abstract

A novel, output-feedback, model reference adaptive controller (MRAC) architecture is proposed for a class of multi-input multi-output (MIMO) dynamical systems. The proposed controller is developed using a design model that contains artificial input, which may model uncertainties or nonlinearities of the controlled system. First, the ideal controller is constructed that contains the artificial input modeling the controlled system uncertainties or nonlinearities. The proposed controller is obtained by replacing the artificial input in the ideal controller with its estimate obtained using an adaptive dynamical component. It is then shown that the system unknown uncertainties or nonlinearities are asymptotically reconstructed. Linear matrix inequalities (LMIs) are used to compute the proposed controller parameters. The controller was implemented and tested on NASA’s Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) model of a typical twin spool, high-bypass turbofan engine in the 40,000-pound takeoff thrust class. The performance of the proposed controller compares favorably against the built-in controller of C-MAPSS40k. This paper presents a derivation and initial proof of concept. It is appropriate to conduct proof-of-concept work at a single design point (small signal design and analysis) before expanding the envelope. Thus, we performed the small signal design analysis and simulation and the results of our analysis are presented in this paper.