非均匀采样数据系统的新型模型描述方法
发布时间：2018-08-14 12:41
【摘要】：提升技术是处理非均匀采样数据(Non-uniformly sampled-data,NUSD)系统的标准工具.然而,提升状态空间模型存在因果约束问题,相应的提升传递函数模型结构复杂,且参数数目过多.因此,它们不便于非均匀采样数据系统的辨识与控制.通过引入时变后移算子,本文提出了一种输入输出表达的新型模型描述方法.该模型能够克服提升系统模型的缺点,使得传统单率系统的辨识和控制方法能够推广到非均匀采样数据系统中.仿真结果表明了新模型的优越性和有效性.
[Abstract]:Lifting is a standard tool for handling Non-uniformly sampled-data / NUSD systems. However, the lifting state space model has causality constraints, and the corresponding lifting transfer function model has a complex structure and a large number of parameters. Therefore, they are not convenient for identification and control of non-uniform sampling data systems. By introducing time-varying backward shift operator, a new model description method for input and output expression is proposed in this paper. This model can overcome the shortcomings of the lifting system model and make the traditional identification and control methods of single-rate systems can be extended to non-uniform sampling data systems. The simulation results show the superiority and effectiveness of the new model.
【作者单位】： 江南大学轻工过程先进控制教育部重点实验室;江南大学物联网工程学院;
【基金】：国家自然科学基金(61403166) 江苏省自然科学基金(BK20140164) 中央高校基本科研业务费专项资金(JUSRP11561,JUSRP51510)资助~~
【分类号】：N945.14;TP13
本文编号：2182892
[Abstract]:Lifting is a standard tool for handling Non-uniformly sampled-data / NUSD systems. However, the lifting state space model has causality constraints, and the corresponding lifting transfer function model has a complex structure and a large number of parameters. Therefore, they are not convenient for identification and control of non-uniform sampling data systems. By introducing time-varying backward shift operator, a new model description method for input and output expression is proposed in this paper. This model can overcome the shortcomings of the lifting system model and make the traditional identification and control methods of single-rate systems can be extended to non-uniform sampling data systems. The simulation results show the superiority and effectiveness of the new model.
【作者单位】： 江南大学轻工过程先进控制教育部重点实验室;江南大学物联网工程学院;
【基金】：国家自然科学基金(61403166) 江苏省自然科学基金(BK20140164) 中央高校基本科研业务费专项资金(JUSRP11561,JUSRP51510)资助~~
【分类号】：N945.14;TP13
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