Fuzzy Modeling and Parallel Distributed Compensation for Aircraft Flight Control from Simulated Flight Data Conference Paper uri icon


  • © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. A method is described that combines fuzzy system identification techniques with Parallel Distributed Compensation (PDC) to develop nonlinear control methods for aircraft using minimal a priori knowledge, as part of NASA’s Learn-to-Fly initiative. A fuzzy model was generated with simulated flight data, and consisted of a weighted average of multiple linear time invariant state-space cells having parameters estimated using the equation-error approach and a least-squares estimator. A compensator was designed for each subsystem using Linear Matrix Inequalities (LMI) to guarantee closed-loop stability and performance requirements. This approach is demonstrated using simulated flight data to automatically develop a fuzzy model and design control laws for a simplified longitudinal approximation of the F-16 nonlinear flight dynamics simulation. Results include a comparison of flight data with the estimated fuzzy models and simulations that illustrate the feasibility and utility of the combined fuzzy modeling and control approach.

author list (cited authors)

  • Weinstein, R., Hubbard, J. E., & Cunningham, M.

citation count

  • 6

publication date

  • June 2018