Abstract: In this paper, a Bayesian identification method based on Markov chain Monte Carlo is proposed to solve the problems of hypersonic vehicle system identification, including the uncertainties caused by transforming complex dynamic model into simple or sparse model, large training data and complex integration. In this method, the data annealing algorithm is introduced into MCMC, which not only solves the problem that MCMC is easy to fall into local optimization, but also combines data annealing with the concept of “high information training data” analyze large data sets with low computational cost. In addition, this method can quantify the uncertainty in the process of parameter estimation and obtain the optimal estimation value of unknown parameters. Through simulation experiments, the effectiveness of this system identification method proposed in this paper is verified, and the identified can be used in controller design and has good control effect.

Key words: Hypersonic vehicle, System identification, Markov chain Monte Carlo (MCMC), Bayesian, Uncertainty