Abstract
This paper investigates the neural adaptive control problem for air-breathing hypersonic vehicles. For the velocity subsystem, a radial basis function neural network (RBFNN)-based adaptive controller is first designed, which employs the auxiliary variable to compensate for the saturation nonlinearity of the scramjet control command. For the altitude subsystem, an RBFNN-based controller addresses actuator constraints and dynamics using the model predictive control, as well as counteracts uncertainties and disturbances using the neural adaptive mechanism. The effectiveness of the proposed control is verified by simulations.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
