Date of Award
Master of Science (MS)
Electrical & Computer Engineering
Electrical and Computer Engineering
Oscar R. González
W. Steven Gray
Thomas E. Alberts
The Modeling and Control for Agile Aircraft Development (MCAAD) group at NASA Langley Research Center(LaRC) is developing techniques for Real-Time Global Modeling (RTGM) and Robust Learning Control (RLC) for NASA’s Transformational Tools and Technologies Project. This project seeks to develop a systematic approach to reduce the iterative nature of aircraft design by introducing a model-less control law and enabling inflight aerodynamic modeling and controller design. The development of the flight control system without prior knowledge of the aircraft aerodynamic model makes use of TakagiSugeno-Kang fuzzy logic inference systems for pitch and roll controllers and are tested in various simulations and wind tunnel platforms. These fuzzy logic controllers are not based on a mathematical model but rather on a rule base of generic flight control laws generated from the designer’s knowledge of aircraft flight mechanics. The controller architecture uses two channels to provide absolute and incremental controller commands as needed. The absolute channel is designed to reject disturbances and decrease rise time, while the incremental channel provides tracking and reduced steady state error. To provide controllers with acceptable performance without the need for tuning, a general method for selecting input and output scaling gains for the fuzzy inference systems is proposed. A performance and robustness comparison of similarly configured Type-1 and Interval Type-2 fuzzy logic controllers is made. The fuzzy logic controllers were implemented on an aircraft model in the NASA Langley 12-Foot low speed tunnel mounted on a free-to-pitch and free-to-roll rig. The development of the controller architectures and wind tunnel results are presented.
Scott, Christopher M..
"Development of a Fuzzy Logic Model-Less Aircraft Controller"
(2022). Master of Science (MS), Thesis, Electrical & Computer Engineering, Old Dominion University, DOI: 10.25777/tt34-rx13