Data-Driven Model Discovery and Control of Lateral-Directional Fighter Aircraft Dynamics

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2022-8-26

Author

Öznurlu, Can

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The focus of this thesis is to control the lateral-directional motion of the fighter aircraft by using integral action based Model Predictive Control (MPC) where the model is obtained by data-driven model discovery method. Dynamic Mode Decomposition with Control (DMDc) is used as a model discovery technique based only on measurement data with no modeling assumptions. The model created using this technique is used for MPC and tested against noisy conditions. In addition, performance comparison of MPC with Classical Controller is carried out. Finally, Speedgoat Unit Real-Time Target Machine®, which offers a real-time esting is used to verify the generated DMDc-MPC algorithm and understand the computational cost. The results show that the DMDc model discovery method performs very well in noisefree situations and meets the evaluation criteria together with MPC. However, its performance decreases in the presence of measurement noise. Finally, real-time test results on Speedgoat® equipment have shown that the generated DMDc-MPC algorithm has low computational cost and can be used in systems with low computational power.

Subject Keywords

Data-Driven Control, Modelling, System Identification, Model Predictive Control, Aircraft Dynamics, DMDc

URI

https://hdl.handle.net/11511/98800

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Graduate School of Applied Mathematics, Thesis

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Citation Formats

C. Öznurlu, “Data-Driven Model Discovery and Control of Lateral-Directional Fighter Aircraft Dynamics,” M.S. - Master of Science, Middle East Technical University, 2022.