Boosting稀疏最小二乘支持向量回归机及其在推力估计中的应用

为实现航空发动机的直接推力控制代替传统的基于传感器的液压机械式控制，本文使用boosting技术提升最小二乘支持向量回归机的性能设计了推力估计器。在使用boosting的过程中，有两点与传统方法不同：（1）为了在建立稀疏最小二乘支持向量回归机的时候使数值计算更稳定，使用无放回抽取；（2）为了实现最小二乘支持向量回归机的稀疏性和降低计算的复杂度，用训练数据集的一个子集来建立最小二乘支持向量回归机，不再使用全部训练数据。仿真实验表明，基于boosting稀疏最小二乘支持向量回归机的推力估计器能够满足直接推力控制的需要，即估计推力相对误差不大于5‰。

BOOSTING SPARSE LEAST SQUARES SUPPORT VECTOR REGRESSION (BSLSSVR) AND ITS APPLICATION TO THRUST ESTIMATION

In order to realize direct thrust control instead of conventional sensors-based control for aero-engine, a thrust estimator with high accuracy is designed by using the boosting technique to improve the performance of least squares support vector regression (LSSVR). There exist two distinct features compared with the conventional boosting technique: (1) Sampling without replacement is used to avoid numerical instability for modeling LSSVR. (2) To realize the sparseness of LSSVR and reduce the computational complexity, only a subset of the training samples is used to construct LSSVR. Thus, this boosting method for LSSVR is called the boosting sparse LSSVR (BSLSSVR). Finally, simulation results show that BSLSSVR-based thrust estimator can satisfy the requirement of direct thrust control, i.e., maximum absolute value of relative error of thrust estimation is not more than 5‰.