Neuroadaptive Model Following Controller Design for Non-Affine Non-Square Aircraft System


Aircrafts with high performance requirements or structural damage, often operate in conditions characterized by rapidly changing nonlinear aerodynamic phenomena. A neural network based model-following adaptive control design is proposed in this paper for stable control of aircrafts in such scenarios. These systems have control dependent nonlinearities making their mathematical characterization non-affine in control and they are usually nonsquare too. The uncertainties that occur during the flight are estimated online with weight update adaptation laws derived by using the Lyapunov theory to ensure boundedness of the estimation errors and the weights. The nonsquare system model is re-structured by using slack variables to facilitate the design of control using the dynamic inversion technique. Typically, when large changes appear in the aerodynamic derivatives, fast adaptation is required to ensure the system stability. But the drawback with using high adaptive gains is that they could result in high frequency oscillations in the control signal that excite the unmodeled dynamics of the plant. A novel observer structure is employed in this paper that enables fast adaptation without inducing any high frequency oscillations. The proposed method is applied to the short period dynamics of a fighter aircraft and is compared with the recently popular L1 adaptive control technique. Simulation results demonstrate the potential of the proposed method. Copyright © 2009 by S.N. Balakrishnan and K. Rajagopal.

Meeting Name

AIAA Guidance, Navigation, and Control Conference and Exhibit (2009: Aug. 10-13, Chicago, IL)


Mechanical and Aerospace Engineering

Document Type

Article - Conference proceedings

Document Version


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© 2009 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.

Publication Date

13 Aug 2009