基于马尔可夫蒙特卡洛法的系统辨识方法研究及应用

提出了基于马尔可夫蒙特卡洛(MCMC)的贝叶斯辨识方法，以解决高超声速飞行器系统辨识中复杂动力学模型转换为简单或稀疏模型所带来的不确定性问题，以及存在的训练数据大和积分难处理的问题。该方法将数据退火算法引入MCMC中，不仅解决了MCMC易陷入局部最优的问题，并且将数据退火与“高信息训练数据”的概念相结合，能够以较低的计算成本分析大数据集。此外，该方法可以对参数估计过程中存在的不确定性进行量化，获得未知参数的最优估计值。通过仿真实验，验证了提出的系统辨识方法的有效性,辨识出的模型能够有效应用于控制器设计之中，并获得较好的控制效果。

Research and Application of System Identification Method Based on Markov Chain Monte Carlo Method

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.