G Prasad, GW Irwin, E Swidenbank, BW Hogg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper reports a non-linear model predictive control (NPMPC) strategy for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and extended Kalman filtering (EKF) to obtain a linear state-space model. The linear model and a quadratic programming routine are then used to design a constrained long-range predictive control routine. A special feature of the control strategy is the selection of a specific set of model parameters for on-line closed-loop estimation to account for time-varying system characteristics resulting from major system disturbances and ageing. Acting as stochastic disturbance states, these parameters provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs and system states. A plant model with 14 non-linear ODEs, simulating the dominant characteristics of a 200 MW oil-fired power plant at Ballylumford, N. Ireland, has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results during large system disturbances and extremely high rate of load changes right across the operating range. The results compare favourably to those obtained with non-linear state-space GPC method designed under similar conditions.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherInternational Association of Science and Technology for Development
Number of pages6
Publication statusPublished (in print/issue) - Jul 1999
EventIASTED International Conference, Control and Applications (CA'99), 25-30 July, Banff, Canada - Banff, Canada
Duration: 1 Jul 1999 → …


ConferenceIASTED International Conference, Control and Applications (CA'99), 25-30 July, Banff, Canada
Period1/07/99 → …


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