Nonlinear continuous-time generalized predictive control of solar power plant

Khoukhi, Billal; Tadjine, Mohamed; Boucherit, Mohamed Seghir

doi:10.1051/smdo/2015003

Cited by 16 publications

(11 citation statements)

References 14 publications

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“…In [36] a practical nonlinear MPC is developed. In [37] a nonlinear continuous time generalized predictive control (GPC) is presented and simulation results are shown. In [38], an improvement of a gain scheduling model predictive controller is proposed and tested on a model of the ACUREX solar field.…”

Section: Solar Field Outlet Temperature Regulator: Nonlinear Model Prmentioning

confidence: 99%

Hybrid Nonlinear MPC of a Solar Cooling Plant

et al. 2019

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Solar energy for cooling systems has been widely used to fulfill the growing air conditioning demand. The advantage of this approach is based on the fact that the need of air conditioning is usually well correlated to solar radiation. These kinds of plants can work in different operation modes resulting on a hybrid system. The control approaches designed for this kind of plant have usually a twofold goal: (a) regulating the outlet temperature of the solar collector field and (b) choosing the operation mode. Since the operation mode is defined by a set of valve positions (discrete variables), the overall control problem is a nonlinear optimization problem which involves discrete and continuous variables. This problems are difficult to solve within the normal sampling times for control purposes (around 20–30 s). In this paper, a two layer control strategy is proposed. The first layer is a nonlinear model predictive controller for regulating the outlet temperature of the solar field. The second layer is a fuzzy algorithm which selects the adequate operation mode for the plant taken into account the operation conditions. The control strategy is tested on a model of the plant showing a proper performance.

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Section: Solar Field Outlet Temperature Regulator: Nonlinear Model Prmentioning

confidence: 99%

Hybrid Nonlinear MPC of a Solar Cooling Plant

et al. 2019

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“…Likewise, projection convergence properties directly apply for individual model parameters in the nonlinear RBF adaptation algorithm shown in Equation (7). Indeed, for a given set of variables v meas , the projection algorithm is guaranteed to improve the latest model parameter estimation previously found in the vicinity of that point.…”

Section: Adaptationmentioning

confidence: 99%

Nonlinear Adaptive Control of Heat Transfer Fluid Temperature in a Parabolic Trough Solar Power Plant

2017

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Control of highly nonlinear processes such as solar collector fields is usually a challenging task. A common approach to this problem involves deploying a set of operation point range-specific controllers, whose actions are to be combined in a switching strategy. Discontinuities in control actions upon switching may lead to instabilities and, therefore, achieving bumpless transitions is always a concern. In addition, linear adaptive predictive controllers need to cope with nonlinearities by using high adaptation speeds, often leading to model vulnerability in the presence of aggressive perturbations. Finally, most of the proposed solutions rely on complex plant model developments. In this work, a multivariable nonlinear model-based adaptive predictive controller has been developed and tested against a parabolic trough solar power plant simulation. Since the model employed by this controller accounts for process nonlinearities, adaptation speed can be dramatically reduced, therefore increasing model robustness. The controller is easily initialized and is able to identify and track the process dynamics, including its nonlinearities as it evolves with time, thus requiring neither process up-front modeling nor switching. The presented controller outperforms its linear counterpart both in terms of accuracy and robustness and, due to the generality of its design, it is expected to be applicable to a wide class of linear and nonlinear processes.

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“…Andrade et al (2013) developed a practical nonlinear MPC. More recently, in Khoukhi et al (2015) a nonlinear continuous time generalized predictive control (GPC) is developed and tested only in simulation.…”

Section: Introductionmentioning

confidence: 99%

Gain-scheduling model predictive control of a Fresnel collector field

Gallego

Merello

Berenguel

et al. 2019

Control Engineering Practice

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Model predictive control strategies have been applied successfully when controlling solar plants. If the control algorithm uses a linear model associated only to an operating point, when the plant is working far from the design conditions, the performance of the controller may deteriorate.In this paper, a gain scheduling model predictive control strategy is designed for the Fresnel collector field located at the Escuela Superior de Ingenieros de Sevilla. Simulation results are provided comparing the proposed strategy with another linear MPC controller showing a better performance. Furthermore, two real tests are presented showing the effectiveness of the proposed strategy.

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Nonlinear continuous-time generalized predictive control of solar power plant

Cited by 16 publications

References 14 publications

Hybrid Nonlinear MPC of a Solar Cooling Plant

Hybrid Nonlinear MPC of a Solar Cooling Plant

Nonlinear Adaptive Control of Heat Transfer Fluid Temperature in a Parabolic Trough Solar Power Plant

Gain-scheduling model predictive control of a Fresnel collector field

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