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Many popular approaches in the field of robust model predictive control (MPC) are based on nominal predictions. This paper presents a novel formulation of this class of controller with proven input-to-state stability and robust constraint satisfaction. Its advantages are: (i) the design of its main ingredients are tractable for medium to large-sized systems, (ii) the terminal set does not need to be robust with respect to all the possible system uncertainties, but only for a reduced set that can be made arbitrarily small, thus facilitating its design and implementation, (iii) under certain conditions the terminal set can be taken as a positive invariant set of the nominal system, allowing us to use a terminal equality constraint, which facilitates its application to large-scale systems, and (iv) the complexity of its optimization problem is comparable to the non-robust MPC variant. We show numerical closed-loop results of its application to a multivariable chemical plant and compare it against other robust MPC formulations., Accepted version of article in Journal of Process Control (13 pages, 25 figures)

The application of multilevel converters to renewable energy systems is a growing topic due to their advantages in energy efficiency. Regarding its control, model predictive control (MPC) has become very appealing due to its natural consideration of discrete inputs, its optimization capability, and the present-day availability of powerful processing hardware. The main drawback of MPC compared to other control techniques in this field is that the control input is held constant during the sampling period, and it is usually difficult or even impossible to reduce this sampling period because of hardware limitations. For this reason, a multirate MPC algorithm is proposed, which allows to change the control input several times within the sampling period. The optimization problem is simplified and made suboptimal to substantially decrease computational burden. This approach is tested in simulation on a three-phase, five-level diode-clamped converter (DCC) operating in inverted mode with a three-phase resistive load. Results show significant reduction in harmonic distortion at the cost of an increase in the number of commutations with respect to a standard MPC operating at the same sampling period., 6 pages, 7 figures, to be published in IFAC World Congress 2020 Proceedings

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0) In this paper, we address the problem of modeling error in economically optimal control. A single layer controller is proposed that integrates the economical part of the Real Time Optimization (RTO), the dynamic part of the Model Predictive Control (MPC) and the Modifier Adaptation strategy (MA), resulting in a controller with the following characteristics: a) recursive feasibility guarantee of the controller; b) asymptotic closed-loop stability for any change in the economic cost function; c) convergence guarantee to the economic optimum of the real plant (offset-free) for any change in the cost function of the controller; and d) simple implementation of the controller. We show the behaviour of the proposal by means of a motivating example that highlights the performance of the proposed algorithm

This article presents a novel learning method based on componentwise Holder continuity, which allows one to consider independently the contribution of each input to each output of the function to be learned. The method provides a bounded prediction error, and its learning property is proven. It can be used to obtain a predictor for a nonlinear robust learning-based predictive controller for constrained systems. The resulting controller achieves better closed loop performance and larger domains of attraction than learning methods that only consider nonlinear set membership, as illustrated by a case study.

Sherpa/Romeo: Versión aceptada en repositorios institucionales tras 24 meses de embargo https://v2.sherpa.ac.uk/id/publication/4278 This paper presents a robust learning-based predictive control strategy for nonlinear systems subject to both input and output constraints, under the assumption that the model function is not known a priori and only input–output data are available. The proposed controller is obtained using a nonparametric machine learning technique to estimate a prediction model. Based on this prediction model, a novel stabilizing robust predictive controller without terminal constraint is proposed. The design procedure is purely based on data and avoids the estimation of any robust invariant set, which is in general a hard task. The resulting controller has been validated in a simulated case study.

A data-based predictive controller is proposed, offering both robust stability guarantees and online learning capabilities. To merge these two properties in a single controller, a double-prediction approach is taken. On the one hand, a safe prediction is computed using Lipschitz interpolation on the basis of an offline identification dataset, which guarantees safety of the controlled system. On the other hand, the controller also benefits from the use of a second online learning-based prediction as measurements incrementally become available over time. Sufficient conditions for robust stability and constraint satisfaction are given. Illustrations of the approach are provided in a simulated case study Feder (UE) DPI2016‐76493‐C3‐1‐R Universidad de Sevilla VI‐PPIT Ministerio de Economía y Competitividad (MINECO). España DPI2016‐76493‐C3‐1‐R

The authors have recently developed predictive controllers based on prediction models derived from experimental data, by means of a class of Holder interpolation called kinky inference. This paper provides a step forward by proposing a novel estimation method based on componentwise Holder interpolation. This allows to explicitly consider the contribution of each component on each output, yielding better estimations. Following the procedure used in previous works, this estimation method is used to provide a predictor for a nonlinear robust data-based predictive controller, whose performance and robustness is enhanced by the new setting. The properties of the proposed controller are demonstrated in a case study.

Model predictive control allows to provide high performance and safety guarantees in the form of constraint satisfaction. These properties, however, can be satisfied only if the underlying model, used for prediction, of the controlled process is sufficiently accurate. One way to address this challenge is by data-driven and machine learning approaches, such as Gaussian processes, that allow to refine the model online during operation. We present a combination of an output feedback model predictive control scheme and a Gaussian process-based prediction model that is capable of efficient online learning. To this end, the concept of evolving Gaussian processes is combined with recursive posterior prediction updates. The presented approach guarantees recursive constraint satisfaction and input-to-state stability with respect to the model-plant mismatch. Simulation studies underline that the Gaussian process prediction model can be successfully and efficiently learned online. The resulting computational load is significantly reduced via the combination of the recursive update procedure and by limiting the number of training data points while maintaining good performance., 29 pages, 13 figures, 3 tables, 1 algorithm, revision submitted to International Journal of Robust and Nonlinear Control

In this study, the authors propose a stabilising data-based model predictive controller for systems subject to constraints in which the prediction model is inferred from experimental data of the plant using a machine learning technique. The inference method is a modification of the kinky inference tailored for model predictive control. In particular, the modified method has a lower computational effort and provides smoother predictions than the original method. The controller formulation considers soft constraints in the outputs, hard constraints in the inputs and guarantees closed-loop robust stability as well as performance by means of the use of different control and prediction horizons and a weighted terminal cost. Under the assumption that the model of the system is Holder continuous, they prove that the closed-loop system is input-to-state stable with respect to the estimation errors. The results are demonstrated in a case study of a continuously stirred-tank reactor.

This paper presents stabilizing Model Predictive Controllers (MPC) to be applied to black-box systems subject to constraints in the inputs and the outputs. The prediction model of the controllers is inferred from experimental data of the inputs and outputs of the plant. Using a nonparametric machine learning technique called SPKI, the estimated (possibly nonlinear) model function is provided. Based on this, a predictive controller with stability guaranteed by design is proposed. Robust stability and recursive feasibility is ensured by using tightened constraints in the optimisation problem but without adding a terminal constraint on the optimisation problem. The proposed predictive controller has been validated in a simulation case study.

This paper presents a novel tracking predictive controller for constrained nonlinear systems capable to deal with sudden and large variations of a piece-wise constant setpoint signal. The uncertain nature of the setpoint may lead to stability and feasibility issues if a regulation predictive controller based on the stabilizing terminal constraint is used. The tracking model predictive controller presented in this paper extends the MPC for tracking for constrained linear systems to the more complex case of constrained nonlinear systems. The key idea is the addition of an artificial reference as a new decision variable. The considered cost function penalizes the deviation of the predicted trajectory with respect to the artificial reference as well as the distance between the artificial reference and the setpoint. Closed-loop stability and recursive feasibility for any setpoint are guaranteed, thanks to an appropriate terminal cost and extended stabilizing terminal constraint. Also, two simplified formulations are shown: the design based on a terminal equality constraint and the design without terminal constraint. The resulting controller ensures recursive feasibility for any changing setpoint. In the case of unreachable setpoints, asymptotic stability of the optimal reachable setpoint is also proved. The properties of the controller have been tested on a constrained continuous stirred tank reactor simulation model and have been experimentally validated on a four-tanks plant. Fil: Limón, Daniel. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España Fil: Ferramosca, Antonio. Universidad Tecnológica Nacional. Facultad Regional Reconquista; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina Fil: Alvarado, Ignacio. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España Fil: Alamo, Teodoro. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España

Amyloid-β (Aβ) plays a relevant role in the neurodegenerative process of Alzheimer's disease (AD). The 25-35 peptide of amyloid-β (Aβ25-35) induces the inflammatory response in brain experimental models. Mucin-type O-glycosylation has been associated with inflammation of brain tissues in AD, thus in this work, we aimed at identifying changes in the glycosylation profile generated by the injection of Aβ25-35 into the CA1 of the hippocampus of rats, using histochemistry with lectins. Our results indicate that 100μM Aβ25-35 induce increased recognition of the Amaranthus leucocarpus lectin (ALL) (specific for Galβ1,3-GalNAcα1,0-Ser/Thr); whereas concanavalin A (Con A) (specific for α-Man) showed no differences among treated and control groups of rats. Jacalin and peanut agglutinin (Galβ1,3GalNAcα1,0-Ser/Thr) showed no recognition of brain cells of control or treated rats. After 6-h treatment of the tissue with trypsin or with 200mM GalNAc, the interaction with ALL was inhibited. Immunohistochemistry showed positive anti-NeuN and ALL-recognition of neurons; however, anti-GFAP and anti-CD11b showed no co-localization with ALL. The ALL+ neurons revealed the presence of cytochrome C in the cytosol and active caspase 3 in the cytosol and nucleus. Administration of the interleukin-1 receptor antagonist (IL-1RA) to Aβ25-35-treated rats diminished neuroinflammation and ALL recognition. These results suggest a close relationship among over-expression of mucin-type O-glycosylation, the neuroinflammatory process, and neuronal death.

Economic Model Predictive Controllers, consisting of an economic criterion as stage cost for the dynamic regulation problem, have shown to improve the economic performance of the controlled plant. However, throughout the operation of the plant, if the economic criterion changes – due to variations of prices, costs, production demand, market fluctuations, reconciled data, disturbances, etc. – the optimal operation point also changes. In industrial applications, a nonlinear description of the plant may not be available, since identifying a nonlinear plant is a very difficult task. Thus, the models used for prediction are in general linear. The nonlinear behavior of the plant makes that the controller designed using a linear model (identified at certain operation point) may exhibit a poor closed-loop performance or even loss of feasibility and stability when the plant is operated at a different operation point. A way to avoid this issue is to consider a collection of linear models identified at each of the equilibrium points where the plant will be operated. This is called a multi-model description of the plant. In this work, a multi-model economic MPC is proposed, which takes into account the uncertainties that arise from the difference between nonlinear and linear models, by means of a multi-model approach: a finite family of linear models is considered (multi-model uncertainty), each of them operating appropriately in a certain region around a given operation point. Recursive feasibility, convergence to the economic setpoint and stability are ensured. The proposed controller is applied in two simulations for controlling an isothermal chemical reactor with consecutive-competitive reactions, and a continuous flow stirred-tank reactor with parallel reactions. Fil: Ferramosca, Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Reconquista; Argentina Fil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Limón, Daniel. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España

Acute and subacute administration of zinc exert neuroprotective effects in hypoxia-ischemia animal models; yet the effect of chronic administration of zinc still remains unknown. We addressed this issue by injecting zinc at a tolerable dose (0.5 mg/kg weight, i.p.) for 14 days before common carotid artery occlusion (CCAO) in a rat. After CCAO, the level of zinc was measured by atomic absorption spectrophotometry, nitrites were determined by Griess method, lipoperoxidation was measured by Gerard-Monnier assay, and mRNA expression of 84 genes coding for cytokines, chemokines, and their receptors was measured by qRT-PCR, whereas nitrotyrosine, chemokines, and their receptors were assessed by ELISA and histopathological changes in the temporoparietal cortex-hippocampus at different time points. Long-term memory was evaluated using Morris water maze. Following CCAO, a significant increase in nitrosative stress, inflammatory chemokines/receptors, and cell death was observed after 8 h, and a 2.5-fold increase in zinc levels was detected after 7 days. Although CXCL12 and FGF2 protein levels were significantly increased, the long-term memory was impaired 12 days after reperfusion in the Zn+CCAO group. Our data suggest that the chronic administration of zinc at tolerable doses causes nitrosative stress, toxic zinc accumulation, and neuroinflammation, which might account for the neuronal death and cerebral dysfunction after CCAO.

Min-max model predictive control (MPC) is one of the few techniques suitable for robust stabilization of uncertain nonlinear systems subject to constraints. Stability issues as well as robustness have been recently studied and some novel contributions on this topic have appeared in the literature. In this survey, we distill from an extensive literature a general framework for synthesizing min-max MPC schemes with an a priori robust stability guarantee. First, we introduce a general prediction model that covers a wide class of uncertainties, which includes bounded disturbances as well as state and input dependent disturbances (uncertainties). Second, we extend the notion of regional input-to-state stability (ISS) in order to fit the considered class of uncertainties. Then, we establish that the standard min-max approach can only guarantee practical stability. We concentrate our attention on two different solutions for solving this problem. The first one is based on a particular design of the stage cost of the performance index, which leads to a Hoo strategy, while the second one is based on a dual-mode strategy. Under fairly mild assumptions both controllers guarantee ISS of the resulting closed-loop system. Moreover, it is shown that the nonlinear auxiliary control law introduced in [29] to solve the H8 problem can be used,for nonlinear systems affine incontrol, in all the proposed min-max schemes and also in presence of state-independent disturbances. A simulation example illustrates the techniques surveyed in this article. .

The objective of this paper is, on the base of existing results, to provide a general framework for synthesizing min-max MPC schemes with an a priori robust stability guarantee for nonlinear constrained systems. Using regional input-to-state stability, it is proven that the standard min-max approach can only guarantee practical stability. This is due to the choice of the stage cost. In order to avoid this problem, two different solutions have been considered: the first one is based on a particular design of the stage cost of the performance index, while the second one is based on a dual-mode strategy. It is shown that under fairly mild assumptions both controllers guarantee input-to-state stability.

Model Predictive Control (MPC) is one of the most used advanced control strategy in the industries, mainly due to its capability to fulfill economic objectives, taking into account a simplified dynamic model of the plant, constraints, and stability requirements. In the last years, several economic formulations of MPC have been presented, which overcome the standard setpoint-tracking formulation. The goal of this work is to provide, by means of application to a highly nonlinear plant, a comparison of different strategies, focusing mainly on economic optimality, computational burden, and economic performance (understood as transient economic optimality).

Economic Model Predictive Controllers, consisting of an economic criterion as stage cost for the dynamic regulation problem, have shown to improve the economic performance of the controlled plant, as well as to ensure stability of the economic setpoint. However, throughout the operation of the plant, economic criteria are usually subject to frequent changes, due to variations of prices, costs, production demand, market fluctuations, reconciled data, disturbances, etc. A different economic criterion determines a change of the optimal operation point and this may cause a loss of feasibility and/or stability. In this paper a stabilizing economic MPC for changing economic criterion for linear prediction models is presented. The proposed controller always ensures feasibility for any given economic criterion, thanks to the particular choice of the terminal ingredients. Asymptotic stability is also proved, providing a Lyapunov function. Fil: Ferramosca, Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina Fil: Limon, Daniel. Universidad de Sevilla. Departamento de Ingenieria de Sistemas y Automatica. Escuela Superior de Ingenieros Industriales; España Fil: Camacho, Eduardo. Universidad de Sevilla. Departamento de Ingenieria de Sistemas y Automatica. Escuela Superior de Ingenieros Industriales; España

In the process industries model predictive controllers (MPC) have the task of controlling the plant ensuring stability and constraints satisfaction, while an economic cost is minimized. Usually the economicobjective is optimized by an upper level Real Time Optimizer (RTO) that passes the economically optimalsetpoints to the MPC level. The drawback of this structure is the possible inconsistence/unreachabilityof those setpoints, due to the different models employed by the RTO and the MPC, as well as their dif-ferent time scales. In this paper an MPC that explicitly integrates the RTO structure into the dynamiccontrol layer is presented. To overcome the complexity of this one-layer formulation a gradient-basedapproximation is proposed, which provides a low-computational-cost suboptimal solution. Fil: Alamo, Teodoro. Universidad de Sevilla. Departamento de Ingenieria de Sistemas y Automatica; España Fil: Ferramosca, Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina Fil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina Fil: Limón, Daniel. Universidad de Sevilla. Departamento de Ingenieria de Sistemas y Automatica; España Fil: Odloak, Darci. Universidade de Sao Paulo; Brasil

In this chapter, a distributed MPC strategy suitable for changing setpoints is described. Based on a cooperative distributed control structure, an extended-cost MPC formulation is proposed, which integrates the problem of computing feasible steady state targets—usually known as Steady State Target Optimizer (SSTO) optimization problem—and the dynamic control problem into a single optimization problem. The proposed controller is able to drive the system to any admissible setpoint in an admissible way, ensuring feasibility under any change of setpoint. It also provides a larger domain of attraction than standard MPC for regulation, due to the particular terminal constraint. Moreover, the controller ensures convergence to the centralized optimum, even in case of coupled constraints. This is possible thanks to the design of the cost function, which integrates the SSTO, and to the warm start algorithm used to initialize the optimization algorithm. A numerical simulation illustrates the benefits of the proposal.

This paper proposes a cooperative distributed linear model predictive control strategy for tracking changing setpoints, applicable to any finite number of subsystems. The proposed controller is able to drive the whole system to any admissible setpoint in an admissible way, ensuring feasibility under any change of setpoint. It also provides a larger domain of attraction than standard distributed MPC for regulation, due to the particular terminal constraint. Moreover, the controller ensures convergence to the centralized optimum, even in case of coupled constraints. This is possible thanks to the warm start used to initialize the optimization Algorithm, and to the design of the cost function, which integrates a Steady State Target Optimizer (SSTO). The controller is applied to a real 4 tanks plant. Fil: Ferramosca, Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España Fil: Limón, Daniel. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España Fil: Alvarado, I.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España Fil: Camacho, E. F.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España

This paper is devoted to the design of a predictive controller for constrained linear systems to track periodic references. The only assumption on the dynamics of the reference is that it is periodic and its period is known. It is also assumed that the reference signal is a priori known by the controller. Inspired in the hierarchical control scheme based on the trajectory planification, the ideas of the MPC for tracking [Limon et al., 2008] are extended to this case. The proposed predictive controller has the future sequence of inputs and an artificial reference as decision variables. The cost function is divided into two terms: one penalizes the tracking error with the artificial reference and other penalizes the deviation of the artificial reference to the reference to be tracked. Stability is ensured thanks to the addition of two constraints: a terminal constraint on the predicted trajectory and a constraint that enforces the artificial reference to be periodic. It is proved that the proposed controller is recursively feasible and the controlled system satisfies the hard constraints, is asymptotically stable and converges to the best possible reachable trajectory. The properties of the proposed controller are illustrated in an example. Spanish Ministry of Science and Innovation and FEDER DPI2008-05818 Spanish Ministry of Science and Innovation and FEDER DPI2010-21589-C05-01

This paper deals with the problem of robust tracking of target sets using a model predictive control (MPC) law. Real industries applications often require a control strategy in which some system outputs are controlled within specified ranges or zones (zone control), while some others variables – possibly including input variables – are steered to fixed target or setpoint. From a theoretical point of view, the control objective of this kind of problem can be seen as a target set (in the output space) instead of a target point, since inside the zones there are no preferences between one point or another. This problem is particularly interesting in case of additive disturbances which might push the outputs out of the zones. In this work, a stable robust MPC formulation for constrained linear systems, based on nominal predictions is presented. The main features of this controller are the use of nominal predictions, restricted constraints and the concept of distance from a point to a set as offset cost function. The controller ensures both recursive feasibility and local optimality. The properties of the controller are shown in a simulation test, in which we consider a subsystem of an industrial FCC system. Fil: Ferramosca, Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Limon, D.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España Fil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Alvarado, I.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España Fil: Camacho, E. F.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España

This paper deals with the robustness of Model Predictive Controllers for constrained uncertain nonlinear systems. The uncertainty is assumed to be modeled by a state and input dependent signal and a disturbance signal. The framework used for the analysis of the robust stability of the systems controlled by MPC is the wellknown Input-to-State Stability. It is shown how this notion is suitable in spite of the presence of constraints on the system and of the possible discontinuity of the control law.

This paper deals with the tracking problem for constrained nonlinear systems using a model predictive control (MPC) law. MPC provides a control law suitable for regulating constrained linear and nonlinear systems to a given target steady state. However, when the target operating point changes, the feasibility of the controller may be lost and the controller fails to track the reference. In this paper, a novel MPC for tracking changing constant references is presented. The main characteristics of this controller are: (i) considering an artificial steady state as a decision variable, (ii) minimizing a cost that penalizes the error with the artificial steady state, (iii) adding to the cost function an additional term that penalizes the deviation between the artificial steady state and the target steady state (the so-called offset cost function) and (iv) considering an invariant set for tracking as extended terminal constraint. The calculation of the stabilizing parameters of the proposed controller is studied and some methods are proposed. The properties of this controller has been tested on a constrained CSTR simulation model.

This paper presents the application of a robust tube-based MPC for tracking of piece-wise constant references to a plant based on the quadruple-tank process . This controller, defined for LTI system subject to additive and bounded disturbances, ensures: (i) the feasibility for any admissible setpoint, (ii) the robust constraint satisfaction, (iii) robust stability and convergence to (a neighborhood of) the desired steady state.

17th IFAC World Congress 2008. Seoul (Korea). 06/07/2008 This paper presents the application of a robust model predictive control for tracking of piece-wise constant references (RMPCT) to a distributed collector field, ACUREX, at the solar power plant of PSA (Solar Plant of Almería). The main characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong disturbances in the process. The real plant is assumed to be modeled as a linear system with additive bounded uncertainties on the states. Under mild assumptions, the proposed RMPCT can steer the uncertain system in an admissible evolution to any admissible steady state, that is, under any change of the set point. This allows us to reject constant disturbances compensating the effect of then changing the setpoint.