Gain-scheduled control of a solar power plant

Johansen, Tor Arne; Hunt, Kenneth J.; Petersen, Idar

doi:10.1016/s0967-0661(00)00043-5

Cited by 46 publications

(36 citation statements)

References 11 publications

(19 reference statements)

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“…First steps in this direction were made by employing gain scheduled constant parameter GPC as well as other gain scheduled approaches (Johansen et al 2000) and switched multiple model supervisory controllers (Lemos et al 2000;Pickardt 2000;Pasamontes et al 2011).…”

Section: Literature Overviewmentioning

confidence: 99%

Solar Energy Collector Systems

Lemos

Neves-Silva

Igreja

2014

Adaptive Control of Solar Energy Collector Systems

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This chapter provides a broad overview of solar thermal energy systems. The aim is to describe the context of distributed collector solar fields used in plants that apply parabolic trough technology. Furthermore, the temperature control problem associated to distributed collector solar fields is explained and the use of adaptive control to solve it is motivated. In contrast to subsequent chapters that use mathematics to explain control algorithms and the models upon which they rely, the approach in this chapter resorts to qualitative explanations together with some plant data to introduce the main ideas.

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Section: Literature Overviewmentioning

confidence: 99%

Solar Energy Collector Systems

Lemos

Neves-Silva

Igreja

2014

Adaptive Control of Solar Energy Collector Systems

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“…In the second realisation a local controller is designed for each local model and the control output is then calculated as an interpolation of the local controller according to the current operating point. Both realisations have been widely used in the literature for control of nonlinear systems (Johansen et al, 2000;Mollov et all. 1999).…”

Section: Introductionmentioning

confidence: 99%

Local Model Networks For Modelling And Predictive Control Of Nonlinear Systems

Novák

Chalupa

Bobál

2009

ECMS 2009 Proceedings Edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera

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The paper deals with the problem of modelling and control using the Local Model Network (LMN). The idea is based on development of multiple local models for the whole operating range of the controlled process. The local models are then smoothly connected using the validity or weighting functions to provide a nonlinear global model of the plant. For saving the computational load, linear model is obtained by interpolating the parameters of local models at each sample instant and then used in Model Predictive Control (MPC) framework to calculate the future behaviour of the process. The supervisory program, based on a nonlinear global model, computes desired values of manipulated variables leading to minimum utility consumption. The approach is verified in a control of model of a heat exchanger.

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“…Johansen et al [17] proposed a gain-scheduled control for the solar plant. In this case, the algorithm uses high-order local linear auto regressive with exogenous inputs (ARX) models and the local linear controllers are designed based on pole placement.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy predictive control of a solar power plant

Flores

Sáez

Araya

et al. 2005

IEEE Trans. Fuzzy Syst.

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Abstract-This paper presents the application of fuzzy predictive control to a solar power plant. The proposed predictive controller uses fuzzy characterization of goals and constraints, based on the fuzzy optimization framework for multi-objective satisfaction problems. This approach enhances model based predictive control (MBPC) allowing the specification of more complex requirements. A brief description of the solar power plant and its simulator is given. Basic concepts of predictive control and fuzzy predictive control are introduced. Two fuzzy predictive controllers using different membership functions are designed for a solar power plant, and they are compared with a classical predictive controller. The simulation results show that the fuzzy MBPC formulation, based on a well proven successful algorithm, gives a greater flexibility to characterize the goals and constraints than classical control.

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Gain-scheduled control of a solar power plant

Cited by 46 publications

References 11 publications

Solar Energy Collector Systems

Solar Energy Collector Systems

Local Model Networks For Modelling And Predictive Control Of Nonlinear Systems

Fuzzy predictive control of a solar power plant

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