基于K-均值聚类和约简最小二乘支持向量回归机的推力估计器设计

提出了一种基K-均值聚类和约简最小二乘支持向量回归机的推力估计器设计方法.首先用K-均值聚类法将全包线范围内的数据进行聚类,然后在每一个类当中,用迭代约简最小二乘支持向量回归机设计一个子推力估计器.在用迭代约简最小二乘支持向量回归机设计子推力估计器的过程中,为了使计算数值更稳定,用Cholesky分解代替原来的迭代方法.最后仿真实验表明,此推力估计器能满足直接推力控制的需要,并和其它的方案比较起来,该方案存在一定的优势.

Thrust estimator design based on K-means clustering and reduced least squares support vector regression

In this paper, a design scheme for thrust estimator is proposed based on K-means clustering and reduced least squares support vector regression. Firstly, the K-means clustering algorithm is utilized to cluster the data in the full flight envelope, and then during each cluster, a sub-estimator is designed using the recursive reduced least squares support vector regression (RRLSSVR). In the process of designing the sub-estimator with RRLSSVR, to be more numerical stability, the Cholesky factorization is utilized as a replacement of the original iteration method. Finally, the simulation experiments show that the thrust estimator can satisfy the requirement of direct thrust control for aeroengines, and compared with the other scheme for thrust estimator, the proposed scheme in this paper is also favor.