Accession Number:

AD1026793

Title:

Robust Adaptive Flight Control Design of Air-breathing Hypersonic Vehicles

Descriptive Note:

Technical Report,26 May 2015,25 May 2016

Corporate Author:

INDIAN INSTITUTE OF SCIENCE BANGALORE India

Personal Author(s):

Report Date:

2016-12-07

Pagination or Media Count:

67.0

Abstract:

Two robust adaptive nonlinear controller designs are presented in this report for control of an air-breathing hypersonic vehicle in the cruise phase of flight. The first type of controller uses dynamic inversion and the second one is obtained using newly developed state-constrained generalized dynamic inversion technique with the help of Lyapunov theory. Furthermore, the robustness of both controllers is enhanced by augmenting them with a fast disturbance observer. The controller is derived using dynamic inversion technique, by transforming nonlinear system dynamics into linear system dynamics with the help of transformation matrix. Further, the control expression is obtained using stable linear error dynamics, which ensures the asymptotic stability of the error. However, a perfectly known system is assumed while designing the controller using dynamic inversion, which is very difficult to achieve in the case of air-breathing hypersonic vehicle. Hence, constrained neuro-adaptive dynamic inversion technique with Jacobian learning approach is proposed in this report. In this technique, an unknown disturbance function is learned by the controller along with the Jacobian matrix ofthe unknown disturbance to ensure fewer transients. Further, barrier Lyapunov function is used in the formulation of neuro-adaptive dynamic inversion technique to constrain the errorin disturbance learning.The state-constrained generalized dynamic inversion technique is formulated using Barrier Lyapunov function in this report. Further, the derived controller is robust enough to maintain the states within the constrained bounds in presence of unknown disturbances or model uncertainties. However, the perfect tracking is achieved by augmenting the state constrained generalized dynamic inversion with constrained neuro-adaptive Jacobian matrix learning based disturbance learning mechanism.

Subject Categories:

  • Research and Experimental Aircraft

Distribution Statement:

APPROVED FOR PUBLIC RELEASE