双率系统辅助模型框架下的随机牛顿递推辨识

针对双率系统, 采用基于辅助模型的改进随机牛顿递推算法辨识输出误差模型. 若当前参数估计对应的估计系统不稳定, 则出现中间不可测时刻输出估计发散, 辨识过程停止. 增加非线性模型与常规辅助模型一起为下步递推提供信息估计, 确保递推进行. 为避免出现输入不充分或者广泛时Hessian 阵奇异或者接近奇异的情况,在Hessian 阵的递推中增加对称正定矩阵. 最后给出了所提出辨识算法的一致收敛性证明.

Auxiliary-model-based stochastic Newton recursive identification for dual-rate system

An auxiliary-model-based improved stochastic Newton recursive algorithm is utilized to identify the output-error model for the dual-rate system. The output estimations at the time between two slow-sampled periods will diverge when the unstable estimated system arises and the identification process will cease. Therefore, a nonlinear model is proposed as the sub-auxiliary model. Together with the general auxiliary model, the proposed model provides estimated information for the next recursion. When the input is not general enough, the Hessian matrix will be singular or nearly singular. A positive definite symmetric matrix is added to the recursion for Hessian matrix to ensure it positive definite. Finally, the uniform convergence of the proposed algorithm is proved.