Discrete-time hypersonic flight control based on extreme learning machine

This paper describes the neural controller design for the longitudinal dynamics of a generic hypersonic flight vehicle (HFV). The dynamics are transformed into the strict-feedback form. Considering the uncertainty, the neural controller is constructed based on the single-hidden layer feedforward network(SLFN). The hidden node parameters are modified using extreme learning machine (ELM) by assigning random values. Instead of using online sequential learning algorithm (OSLA), the output weight is updated based on the Lyapunov synthesis approach to guarantee the stability of closed-loop system. By estimating the bound of output weight vector, a novel back-stepping design is presented where less online parameters are required to be tuned. The simulation study is presented to show the effectiveness of the proposed control approach.