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个人信息Personal Information
教授
博士生导师
硕士生导师
主要任职：Professor at the Institute of Advanced Measurement & Control Technology
其他任职：先进检测与控制技术研究所所长
性别：男
毕业院校：上海交通大学
学位：博士
所在单位：控制科学与工程学院
学科：控制理论与控制工程. 化学工程
办公地点：大连理工大学控制科学与工程学院先进检测与控制技术研究所
大连市凌工路2号大连理工大学海山楼A座724室
联系方式：Tel:(0411)84706465 实验室网站：http://act.dlut.edu.cn/
电子邮箱：tliu@dlut.edu.cn
Refined instrumental variable parameter estimation of continuous-time Box-Jenkins models from irregularly sampled data
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论文类型：期刊论文
发表时间：2017-01-20
发表刊物：IET CONTROL THEORY AND APPLICATIONS
收录刊物：SCIE、EI
卷号：11
期号：2
页面范围：291-300
ISSN号：1751-8644
关键字：sampled data systems; parameter estimation; continuous time systems; iterative methods; computational efficiency; noise model; prediction error method; plant model; instrumental variable method; two-step iterative procedure; plant-noise model decomposition; Box-Jenkins structure; irregularly sampled data; continuous-time Box-Jenkins model parameter estimation
摘要：This study investigates the estimation of continuous-time Box-Jenkins model parameters from irregularly sampled data. The Box-Jenkins structure has been successful in describing systems subject to coloured noise, since it contains two sub-models that feature the characteristics of both plant and noise systems. Based on plant-noise model decomposition, a two-step iterative procedure is proposed to solve the estimation problem, which consists of an instrumental variable method for the plant model and a prediction error method for the noise model. The proposed method is of low complexity and shows good estimation robustness and accuracy. Implementation issues are discussed to improve the computational efficiency. Numerical examples are presented to demonstrate the effectiveness of the proposed method.