This article addresses a combined approach of sliding mode control (SMC) with generalized predictive control (GPC) to achieve fluid temperature control in the evaporator of a heat recovery steam generator. The evaporator is modelled as a first-order plus dead time process. The model is developed using the experimental data obtained at an actual power plant. An output error identification algorithm is used to minimize the error between the model and the experiments in different operating conditions. A GPC method is exploited to optimize the sliding surface and the coefficients of the switching functions used in SMC. The proposed control schemes are evaluated by thorough simulation for performance and robustness against parameter variations and disturbances.
AbdennourA. B.LeeK. Y.A decentralized controller designed for a power plant using robust local controllers and functional mappingIEEE Trans. Energy Convers.112 (1996): 394–400.
2.
KabailaP. V.On output error methods for system identificationIEEE Trans. Autom. Control28 (1983): 12–23.
3.
UtkinV. I.Variable structure systems with sliding modeIEEE Trans. Autom. Control222 (1977): 212–222.
4.
UtkinV. I., Sliding modes in control optimization (New York: Springer-Verlag1992).
5.
EdwardsC.SpurgeonS. K., Sliding mode control: Theory and applications (London: Taylor and Francis Ltd.1998).
6.
ChenF.DunniganM. W., Feedback linearization sliding-mode torque control of an induction machine In Proceedings of the IEEE PEMD Conference, 2002, pp. 110–115. (2002).
7.
WangW. J.ChenJ. Y.Passivity-based sliding mode position control for induction motor drivesIEEE Trans. Energy Convers.202 (2005): 316–321.
8.
StepanenkoY.SuC.Variable structure control of robot manipulator with nonlinear sliding manifoldsInt. J. Control582 (1993): 285–300.
9.
XuJ.-X.CaoW.-J.Synthesized sliding mode control of a single-like flexible robotInt. J. Control732 (2000): 197–209.
10.
CamachoO., A predictive approach based-sliding mode control In Proceedings of the 15th Triennial World Congress, Barcelona, Spain (2002).
11.
Pérez de la ParteM.CamachoO.CamachoE. F.Development of a GPC-based sliding mode controllerISA Trans.411 (2002): 19–30.
12.
Pérez de la ParteM.CamachoE.F., Application of a predictive sliding mode controller to a heat exchanger. In Proceddings of the IEEE Conference on Control applications, Glasgow, Scotland, September 2003, pp. 1219–1224.
13.
CamachoO.RojasR.Garcia-GabinW.Some long time delay sliding mode control approachesISA Trans.46 (2007): 95–101.
14.
BordonsC.CamachoE. F.A generalized predictive controller wide class of industrial processesIEEE Trans. Control Syst. Technol.63 (1998): 372–387.
15.
ClarkeD. W.MohtadiC.TuffsP. S.Generalized predictive control, part 1: The basic algorithmAutomatica232 (1987): 137–148.
16.
ClarkeD. W.MohtadiC.TuffsP. S.Generalized predictive control, part 2: Extensions and interpretationsAutomatica232 (1987): 149–160.
GaoW. B.HungJ. C.Variable structure control of nonlinear system: A new approachIEEE Trans. Ind. Electron.401 (1993): 45–55.
21.
CamachoO., A new approach to design and tune sliding mode controllers for chemical processes University of South Florida, Tampa, Florida, (1996) PhD Dissertation.
22.
CamachoO.SmithC.Sliding mode control: An approach to regulate nonlinear chemical processesISA Trans.39 (2000): 205–218.
23.
CamachoE. F.BordonsC., Model predictive control (London: Springer-Verlag2004).
24.
RyaS.-H.ParkJ.-H., Auto-tuning of sliding mode control parameters using fuzzy logic. In Proceedings of the American Control Conference, June 2001, vol. 1, pp. 618–623.
25.
NellesO., Nonlinear system identification: From classical approaches to neural networks and fuzzy models (Berlin, Germany: Springer-Verlag2001).
