Robust Adaptive Neural Control of Nonminimum Phase Hypersonic Vehicle Model

This paper investigates the robust adaptive neural control of nonminimum phase hypersonic flight vehicle using composite learning. To overcome the nonminimum phase behavior, the output redefinition is employed and the attitude subsystem is transformed to the internal subsystem and the input-output subsystem. For the input-output subsystem, the adaptive neural control works together with the robust control to follow the reference command of pitch angle derived from the internal subsystem. Furthermore, the sliding mode control is constructed in a similar way. For the update of the neural weights, the composite learning is constructed using the prediction error. The stability of the closed-loop system is analyzed via the Lyapunov approach and the ultimately uniform boundedness of the tracking errors can be guaranteed. The effectiveness of the methodology is illustrated by the simulation results.

Automated summary

This paper investigates the robust adaptive neural control of nonminimum phase hypersonic flight vehicle using composite learning. The output redefinition and transformation of attitude subsystem into internal and input-output subsystem are employed to overcome nonminimum phase behavior. The use of composite learning for updating neural weights ensures stability and boundedness of tracking errors in the closed-loop system.