Parameter estimation of a heating system using a genetic algorithm

Presents a genetic algorithm (GA)-based method for the online parameter estimation of a heating system. A more commonly used recursive least square (RLS) estimator is compared with the GA estimator. Both the methods are used with the generalised predictive controller (GPC), to provide self-tuning co...

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Bibliographic Details
Published inIEE conference publication pp. 195 - 200
Main Authors Ahmad, M, Zhang, L, Readle, J.C
Format Conference Proceeding
LanguageEnglish
Published London IEE 1998
Subjects
Applied sciences
Computer science; control theory; systems
Control of heat systems
Control theory. Systems
Exact sciences and technology
Modelling and identification
Optimal control
Optimisation techniques
Self-adjusting control systems
Simulation, modelling and identification
genetic algorithm
recursive least square estimator
generalised predictive controller
parameter estimation
self-adjusting systems
self-tuning control
genetic algorithms
heating
predictive control
heating system
Parameter estimation
System identification
Genetic algorithm
Least squares method
Predictive control
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Summary:Presents a genetic algorithm (GA)-based method for the online parameter estimation of a heating system. A more commonly used recursive least square (RLS) estimator is compared with the GA estimator. Both the methods are used with the generalised predictive controller (GPC), to provide self-tuning control to the heating system. Results show that the GA estimates are accurate, its performance is not changed even when the GPC parameters are altered, while the RLS estimator provided some poor estimates and resulted in poor initial control performance by the GPC.
ISBN:085296708X
9780852967089
ISSN:0537-9989
DOI:10.1049/cp:19980226