Subspace-based Identification of Continuous-Time Stochastic Systems via Random Distribution Approach
نویسندگان
چکیده
منابع مشابه
Indirect continuous-time system identification - A subspace downsampling approach
This article presents a new indirect identification method for continuous-time systems able to resolve the problem of fast sampling. To do this, a Subspace IDentification DownSampling (SIDDS) approach that takes into consideration the intermediate sampling instants of the input signal is proposed. This is done by partitioning the data set into m subsets, where m is the downsampling factor. Then...
متن کاملStochastic subspace identification via “LQ decomposition”
A new stochastic subspace identification algorithm is developed with the help of a stochastic realization on a finite interval. First, a finite-interval realization algorithm is rederived via “block-LDL decomposition” for a finite string of complete covariance sequence. Next, a stochastic subspace identification method is derived by adapting the finiteinterval realization algorithm to incomplet...
متن کاملContinuous-time subspace identification in closed-loop
This paper deals with the problem of continuoustime model identification and presents a subspace-based algorithm capable of dealing with data generated by systems operating in closed-loop. The algorithm is developed by reformulating the identification problem from the continuous-time model to an equivalent one to which discrete-time subspace identification techniques can be applied. More precis...
متن کاملSubspace Predictive Control for Continuous-time Systems
The paper presents a model predictive control method for continuous-time systems based on subspace identification. It’s developed by reformulating the continuous-time systems using Laguerre filters to obtain the subspace prediction output. Then, the subspace predictors are derived by QR decomposition from input-output and Laguerre matrices. The subspace predictive controller is designed with th...
متن کاملA subspace method for frequency selective identification of stochastic systems ⋆
A parametric method for the estimation of vector valued discrete-time stochastic systems or equivalently the spectrum of a stochastic process is presented. The key feature is that the method can be used to frequency selectively fit the model to the data. This means that parts of the spectrum can be modeled with a lower model order than otherwise would be necessary if the entire spectrum would b...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications
سال: 1999
ISSN: 2188-4730,2188-4749
DOI: 10.5687/sss.1999.53