Discretization of continuous systems with internal and external point delays through a quasiparametrical ARMA model

1997 ◽  
Vol 3 (2) ◽  
pp. 181-198 ◽  
Author(s):  
Carlos F Alastruey ◽  
Manuel Delasen
Keyword(s):  
Arma Model ◽  
Continuous Systems ◽  
External Point ◽  
Point Delays

scholarly journals On finite-time consensus objectives in time-varying interconnected discrete linear dynamic systems under internal and external delays

2018 ◽  
Vol 10 (7) ◽  
pp. 168781401878484 ◽  
Author(s):  
Manuel De la Sen ◽  
Santiago Alonso-Quesada
Keyword(s):  
Dynamic Systems ◽  
Finite Time ◽  
Closed Loop ◽  
Discrete Systems ◽  
Open Loop ◽  
Time Varying ◽  
Common Error ◽  
Linear Dynamic Systems ◽  
External Point ◽  
Point Delays

This article formulates the properties of achievable consensus of linear interconnected discrete systems with multiple internal and external point delays. The formulation is stated in an algebraic generic context as the ability of achievement of (a non-necessarily zero) finite-time common error between the various subsystems. The consensus signals are generically defined so that they can be, in general, distinct of the output or state components. However, the consensus signals of all the interconnected subsystems have the same dimension for coherency reasons. A particular attention is paid to the case of weak interconnection couplings in both the open-loop case and the closed-loop one under, in general, linear output feedback. Some further extensions are given related to consensus over intervals and related to consensus of positive interconnected systems.

Reduced-Order Parameter Estimation for Continuous Systems From Sampled Data

1990 ◽  
Vol 112 (2) ◽  
pp. 305-308 ◽  
Author(s):  
C. M. Liaw ◽  
M. Ouyang ◽  
C. T. Pan
Keyword(s):  
Continuous Time ◽  
Arma Model ◽  
Original System ◽  
Dispersion Analysis ◽  
Continuous Systems ◽  
Sampled Data ◽  
Continuous Time Model ◽  
Time Model ◽  
Minimum Discrepancy ◽  
System Stabilizer

An indirect method for estimating the parameters of the reduced continuous-time model from the sampled input/output data is presented. In this method, a discrete-time ARMA model is first identified. Then, the order of the continuous-time model is minimized by the dispersion analysis and/or accumulated dispersion analysis with the criterion of minimum discrepancy in sense of energy contribution between the original system and the reduced model. Finally, the reduced continuous-time model is matched to the identified discrete ARMA model in frequency domain. The proposed approach is applied to the identification of a power system stabilizer. The results show that the estimated continuous-time models are rather close to those supplied by the vender.

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