System identification—discrete time

2019 ◽  
pp. 230-284
Author(s):  
K Worden ◽  
G R Tomlinson
Keyword(s):  
System Identification ◽  
Discrete Time

scholarly journals Effects of Plant Identification on the Generalized Predictive Controllers Design

2016 ◽  
Vol 14 (4) ◽  
pp. 2-10
Author(s):  
E. Garipov
Keyword(s):  
Control System ◽  
System Identification ◽  
Control Systems ◽  
Discrete Time ◽  
Optimization Procedure ◽  
Step Response ◽  
Plant Identification ◽  
Plant Characteristic ◽  
Predictive Controllers ◽  
Discrete Time Models

Abstract The effects of the estmated plant models accuracy on the control system signals quality after generalized predictive controllers design are studied in the paper. Two identification approaches are used for different in structures discrete-time models – by optimization procedure in Optimization Toolbox based on the plant step response as a standard deterministic plant characteristic and by functions in System Identification Toolbox after experiments with random signals on the plant. The generalized predictive controllers are design according to the estimated models. The processes in the simulated control systems are analyzed concerning the effects of different kind plant models on the designed controllers.

scholarly journals System Identification Using Laguerre Basis Functions

2021 ◽  
Author(s):  
philip olivier
Keyword(s):  
System Identification ◽  
Discrete Time ◽  
New Product ◽  
Basis Functions ◽  
Model Order ◽  
Identification Process ◽  
Laguerre Expansion ◽  
Laguerre Basis ◽  
Expansion Coefficients ◽  
Using Data

This document describes how to use discrete time Laguerre basis functions, and their associated z-transforms, to construct a system identification process using experimentally determined Laguerre expansion coefficients of the input and output sequences. The process is derived using a new Product Propert} for the discrete time Laguerre basis functions. The system identification process is "linear in the parameters"; it does not require assumptions/knowledge of the poles locations of the system under test. An example is presented using data generated by a system that has appeared in the recent literature. The procedure naturally produces equations that can be used to determine if the chosen model order is correct or if its order needs to be increased. Constraints can easily be incorporated.

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