Identification of continuous-time hybrid “Box–Jenkins” systems with multiple unknown time delays using two-stage parameter estimation algorithm

Purpose This study/paper aims to present a separable identification algorithm for a multiple input single output (MISO) continuous time (CT) hybrid “Box–Jenkins”. Design/methodology/approach This paper proposes an optimal method for the identification of MISO CT hybrid “Box–Jenkins” systems with unk...

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Bibliographic Details
Published in:Engineering computations Vol. 36; no. 6; pp. 2111 - 2130
Main Author: Ghoul, Yamna
Format: Journal Article
Language:English
Published: Bradford Emerald Publishing Limited 15.08.2019
Emerald Group Publishing Limited
Subjects:
Algorithms
Computer simulation
Continuous time systems
Economic models
Estimates
Hybrid systems
Identification
Least squares
Mathematical models
Methods
MISO (control systems)
Monte Carlo simulation
Noise
Parameter estimation
Parameter identification
Recursive methods
Subsystems
Continuous time
Recursive identification
Box–Jenkins systems
Multiple unknown time delays
Two-stage identification algorithm
ISSN:0264-4401, 1758-7077
Online Access:Get full text
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Summary:Purpose This study/paper aims to present a separable identification algorithm for a multiple input single output (MISO) continuous time (CT) hybrid “Box–Jenkins”. Design/methodology/approach This paper proposes an optimal method for the identification of MISO CT hybrid “Box–Jenkins” systems with unknown time delays by using the two-stage recursive least-square (TS-RLS) identification algorithm. Findings The effectiveness of the proposed scheme is shown with application to a simulation example. Originality/value A two-stage recursive least-square identification method is developed for multiple input single output continuous time hybrid “Box–Jenkins” system with multiple unknown time delays from sampled data. The proposed technique allows the division of the global CT hybrid “Box–Jenkins” system into two fictitious subsystems: the first one contains the parameters of the system model, including the multiple unknown time delays, and the second contains the parameters of the noise model. Then the TS-RLS identification algorithm can be applied easily to estimate all the parameters of the studied system.
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ISSN:0264-4401
1758-7077
DOI:10.1108/EC-12-2018-0550