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Intelligent Control for Fixed-Wing eVTOL Aircraft


Shi, Xichen (2021) Intelligent Control for Fixed-Wing eVTOL Aircraft. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/51c6-aa57.


Urban Air Mobility (UAM) holds promise for personal air transportation by deploying "flying cars" over cities. As such, fixed-wing electric vertical take-off and landing (eVTOL) aircraft has gained popularity as they can swiftly traverse cluttered areas, while also efficiently covering longer distances. These modes of operation call for an enhanced level of precision, safety, and intelligence for flight control. The hybrid nature of these aircraft poses a unique challenge that stems from complex aerodynamic interactions between wings, rotors, and the environment. Thus accurate estimation of external forces is indispensable for a high performance flight. However, traditional methods that stitch together different control schemes often fall short during hybrid flight modes. On the other hand, learning-based approaches circumvent modeling complexities, but they often lack theoretical guarantees for stability.

In the first part of this thesis, we study the theoretical benefits of these fixed-wing eVTOL aircraft, followed by the derivation of a novel unified control framework. It consists of nonlinear position and attitude controllers using forces and moments as inputs; and control allocation modules that determine desired attitudes and thruster signals. Next, we present a composite adaptation scheme for linear-in-parameter (LiP) dynamics models, which provides accurate realtime estimation for wing and rotor forces based on measurements from a three-dimensional airflow sensor. Then, we introduce a design method to optimize multirotor configuration that ensures a property of robustness against rotor failures.

In the second part of the thesis, we use deep neural networks (DNN) to learn part of unmodeled dynamics of the flight vehicles. Spectral normalization that regulates the Lipschitz constants of the neural network is applied for better generalization outside the training domain. The resultant network is utilized in a nonlinear feedback controller with a contraction mapping update, solving the nonaffine-in-control issue that arises. Next, we formulate general methods for designing and training DNN-based dynamics, controller, and observer. The general framework can theoretically handle any nonlinear dynamics with prior knowledge of its structure. Finally, we establish a delay compensation technique that transforms nominal controllers for an undelayed system into a sample-based predictive controller with numerical integration. The proposed method handles both first-order and transport delays in actuators and balances between numerical accuracy and computational efficiency to guarantee stability under strict hardware limitations.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:VTOL; Aircraft; Robotics; Control
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Space Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Chung, Soon-Jo
Thesis Committee:
  • Burdick, Joel Wakeman (chair)
  • Chung, Soon-Jo
  • Murray, Richard M.
  • Yue, Yisong
Defense Date:12 January 2021
Record Number:CaltechTHESIS:02182021-040721884
Persistent URL:
Related URLs:
URLURL TypeDescription adapted for Ch. 3 adapted for Ch. 4 adapted for Ch. 5 adapted for part of Ch. 6 adapted for Ch. 7 VideoVideo content in Ch. 4 VideoVideo content in Ch. 6
Shi, Xichen0000-0002-5366-9256
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14083
Deposited By: Xichen Shi
Deposited On:01 Mar 2021 17:38
Last Modified:02 Nov 2021 18:57

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