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Reference: Lambert, E. P., (1987). Process control applications of long-range prediction. DPhil. University of Oxford.Citable link to this page:

 

Process control applications of long-range prediction

Abstract: The recent Generalised Predictive Control algorithm (Clarke et al, 1984,87) is a self-tuning/adaptive control algorithm that is based upon long-range prediction, and is thusclaimed to be particularly suitable for process control application.The complicated nature of GPC prevents the application of standard analytical techniques.Therefore an alternative technique is developed where an equivalent closed loopexpression is repeatedly calculated for various control scenarios. The properties of GPCare investigated and, in particular, it is shown that 'default' values for GPC's design parametersgive a mean-level type of control law that can reasonably be expected to providerobust control for a wide variety of processes.Two successful industrial applications of GPC are then reported. The first series oftrials involve the SISO control of soap moisture for a full-scale drying process. After abrief period of PRBS assisted self-tuning default GPC control performance is shown tobe significantly better than the existing manual control, despite the presence of a largetime-delay, poor measurements and severe production restrictions.The second application concerns the MIMO inner loop control of a spray drying towerusing two types of GPC controller: full multivariable MGPC, and multi-loop DGPC.Again after only a brief period of PRBS assisted self-tuning both provide dramaticallysuperior control compared to the existing multi-loop gain-scheduled PID control scheme.In particular the use of MGPC successfully avoids any requirement for a priori knowledgeof the process time-delay structure or input-output pairing. The decoupling performanceof MGPC is improved by scaling and that of DGPC by the use of feed-forward. Thepractical effectiveness of GPC's design parameters (e.g. P, T and λ) is also demonstrated.On the estimation side of adaptive control the current state-of-the-art algorithms arereviewed and shown to suffer from problems such as 'blowup', parameter drift and sensitivityto unmeasurable load disturbances. To overcome these problems two novel estimationalgorithms (CLS and DLS) are developed that extend the RLS cost-function to includeweighting of estimated parameters. The exploitation of the 'fault detection' properties ofCLS is proposed as a more realistic estimation philosophy for adaptive control than the'continuous retention of adaptivity'.

Type of Award:DPhil Level of Award:Doctoral Awarding Institution: University of Oxford Notes:This thesis was digitised thanks to the generosity of Dr Leonard Polonsky

Contributors

Clarke, DavidMore by this contributor

RoleSupervisor

 

D. W. ClarkeMore by this contributor

RoleSupervisor

 Bibliographic Details

Issue Date: 1987Identifiers

Urn: uuid:de56df0b-466c-42ce-a03b-72228ad6af2a

Source identifier: 602830346 Item Description

Type: Thesis;

Language: eng Subjects: Predictive control Adaptive control systems Tiny URL: td:602830346

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Autor: Lambert, E. P. - institutionUniversity of Oxford facultyMathematical and Physical Sciences Division - - - - Contributors Clarke,

Fuente: https://ora.ox.ac.uk/objects/uuid:de56df0b-466c-42ce-a03b-72228ad6af2a



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