摘要
针对非均匀多采样率系统的建模问题,根据因果关系,建立了非均匀多采样率系统的状态空间模型.对于含有提升变量的状态空间模型,提出基于子空间技术的辨识方法.首先,由系统的输入输出数据建立由Hankel矩阵组成的扩展状态空间方程;其次,利用斜交投影的原理,以及奇异值分解,通过子空间辨识算法确定增广观测矩阵和状态向量;最后,通过最小二乘方法确定模型的参数矩阵.该方法简单有效且对初值具有鲁棒性.仿真实例验证了方法的有效性.
According to the modeling of non-uniformly multirate sampling system,a state space model is derived due to the casual relationship.Subspace-based identification is developed for state space models,which have lifting variables.Firstly,an extended state space equation formed by input-output Hankel matrices is established.Then,the extended observability matrices and state vectors are obtained by subspace-based identification algorithm through the oblique proj ection and singular value decomposition.Lastly,the parameter matrices are determined using the least square algorithm.A simulation example is presented to illustrate the performance and robustness for initials of the proposed method.
出处
《大连理工大学学报》
EI
CAS
CSCD
北大核心
2014年第5期575-580,共6页
Journal of Dalian University of Technology
关键词
非均匀多采样率系统
状态空间模型
子空间方法
系统辨识
non-uniformly multirate sampling system
state space model
subspace-based method
system identification
