Discrete neural altitude control for hypersonic vehicle via flight path angle tracking

In this study, the altitude control is analyzed for the longitudinal dynamics of a generic Hypersonic Flight Vehicle (HFV). By transforming altitude command into the tracking of flight path angle with fast dynamics, the system design is focusing on the control of the attitude subsystem. The virtual control is designed with nominal feedback and Neural Network (NN) approximation via back-stepping. Under the proposed controller, the Semiglobal Uniform Ultimate Boundedness (SGUUB) stability is guaranteed. The slow dynamics are transformed into the parameter estimation problem and the update law is designed. The simulation is presented to show the effectiveness of the proposed control approach.