Identi cation of Sampled Data Systems at Frequencies Beyond the
نویسندگان
چکیده
| This paper proposes a practical algorithm for identifying the dynamics of a continuous-time linear, time-invariant system, embedded in a sampled feedback loop with xed sample time, T. We will show that if the closed-loop connguration is stable, it is rather straightforward to design a set of experiments using a spectrum analyzer that will identify the plant transfer function, Gp(j!), to frequencies well beyond the Nyquist frequency, ! N = T. Experimental results are included.
منابع مشابه
SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS
In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level ...
متن کاملIdentiication in H 1 with Nonuniformly Spaced Frequency Response Measurements
In this paper, the problem of \system identi cation in H 1 " is investigated in the case when the given frequency response data is not necessarily on a uniformly spaced grid of frequencies. A large class of robustly convergent identi cation algorithms are derived. A particular algorithm is further examined and explicit worst case error bounds (in the H 1 norm) are derived for both discrete-time...
متن کاملComputational Limits on Team Identi cation of Languages
A team of learning machines is essentially a multiset of learning machines A team is said to successfully identify a concept just in case each member of some nonempty subset of the team identi es the concept Team identi cation of programs for computable functions from their graphs has been investigated by Smith Pitt showed that this notion is essentially equivalent to function identi cation by ...
متن کاملComputational Limits on Team Identi cation
A team of learning machines is essentially a multiset of learning machines. A team is said to successfully identify a concept just in case each member of some nonempty subset of the team identi es the concept. Team identi cation of programs for computable functions from their graphs has been investigated by Smith. Pitt showed that this notion is essentially equivalent to function identi cation ...
متن کاملBlind Channel Identi cation Based On Cyclic Statistics
Abstract : Blind channel identi cation and equalization based on second-order statistics by subspace tting and linear prediction have received a lot of attention lately. On the other hand, the use of cyclic statistics in fractionally sampled channels has also raised considerable interest. We propose to use these statistics in subspace tting and linear prediction for (possibly multiuser and mult...
متن کامل