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Exercise performance and zinc administration individually yield a protective effect on various neurodegenerative models, including ischemic brain injury. Therefore, this work was aimed at evaluating the combined effect of subacute prophylactic zinc administration and swimming exercise in a transient cerebral ischemia model. The prophylactic zinc administration (2.5 mg/kg of body weight) was provided every 24 h for four days before a 30 min common carotid artery occlusion (CCAO), and 24 h after reperfusion, the rats were subjected to swimming exercise in the Morris Water Maze (MWM). Learning was evaluated daily for five days, and memory on day 12 postreperfusion; anxiety or depression-like behavior was measured by the elevated plus maze and the motor activity by open-field test. Nitrites, lipid peroxidation, and the activity of superoxide dismutase (SOD) and catalase (CAT) were assessed in the temporoparietal cortex and hippocampus. The three nitric oxide (NO) synthase isoforms, chemokines, and their receptor levels were measured by ELISA. Nissl staining evaluated hippocampus cytoarchitecture and Iba-1 immunohistochemistry activated the microglia. Swimming exercise alone could not prevent ischemic damage but, combined with prophylactic zinc administration, reversed the cognitive deficit, decreased NOS and chemokine levels, prevented tissue damage, and increased Iba-1 (+) cell number. These results suggest that the subacute prophylactic zinc administration combined with swimming exercise, but not the individual treatment, prevents the ischemic damage on day 12 postreperfusion in the transient ischemia model.

This article presents an extension to the nonlinear model predictive control (MPC) for tracking scheme able to guarantee convergence even in cases of nonconvex output admissible sets. This is achieved by incorporating a convexifying homeomorphism in the optimization problem, allowing it to be solved in the convex space. A novel class of nonconvex sets is also defined for which a systematic procedure to construct a convexifying homeomorphism is provided. This homeomorphism is then embedded in the MPC optimization problem in such a way that the homeomorphism is no longer required in closed form. Finally, the effectiveness of the proposed method is showcased through an illustrative example.

In this article, we present an implementation of a low-memory footprint model predictive control (MPC)-based controller in programmable logic controllers (PLCs). Automatic code generation of standardized IEC 61131–3 PLC programming languages is used to solve the MPC’s optimization problem online. The implementation is designed for its application in a realistic industrial environment, including timing considerations and accounting for the possibility of the PLC not being exclusively dedicated to the MPC controller. We describe the controller architecture and algorithm, show the results of its memory footprint with regard to the problem dimensions, and present the results of its implementation to control a hardware-in-the-loop multivariable chemical plant.

The aim of this study was to investigate the prevalence of anxiety and depression among a population of exact and natural science university students from Benemerita Universidad Autonoma de Puebla (BUAP)-Mexico under COVID-19 lockdown. Furthermore, we explored influencing factors pertaining to the online learning environment at home. A total of 502 college students; 192 men (38.2%) and 310 women (61.8%) participated in this cross-sectional web-based survey. A Goldberg Anxiety and Depression Scale (GADS) standardized e-questionnaire was generated using Google Forms, and the link was shared through email. The data collection process was conducted during voluntary COVID-19 lockdown during the second university semester (October-November, 2020) and before the final exams period. The data recovered was analyzed in three consecutive levels, including univariate, bivariate, and multivariate analysis. The college students experienced high levels of anxiety and depression. The sampled population of men and women was 75.5% and 92.3% respectively measured positive for anxiety, whereas depression was measured at 63.5% and 78.4% respectively. Factors influencing at-home e-learning such as internet connection quality, internet cost, status of owning or sharing a PC, inhabitants per household and length of academic program completed were correlated positively with high prevalence (>60%) of anxiety and depression among students. Based on the reported results, we suggest that both the university authorities and government could work together to address these high levels of anxiety and depression to reduce their impact among university students with the ultimate goal of achieving optimal learning during lockdown conditions.

Tetanus toxin is a polypeptide of 1315 aminoacids of 150 kDa, from which a non-toxic fraction of 50 kDa is extracted that has the ability to cross the blood brain barrier through mechanisms that full-length toxin, have developed to throughout its evolution. We refer to axonal retrograde trip and synaptic jumps. Studies indicate that the C-terminal domain of the tetanus toxin heavy chain, in vivo and in vitro, has neuromodulatory, neuroprotective and antiapoptotic properties when confronted with neurotoxic agents such as MPTP, 6-hydroxypamine or methamphetamine. Therefore, there are high expectations about the therapy with HC-TeTx, and its multiple variants, regarding the neuroprotection they provide. With all before said, we propose that HC has the potential to be a drug with multiple applications. Even with this, questions remain to be resolved and more studies are required to show in detail the aforementioned effects.

The goal of this note is to highlight that the reformulation of saturation as constraints, typical of the computation of the maximal output admissible sets used in the reference governor literature, is not without loss of generality and may introduce a relevant amount of conservativeness. The note also proposes a modified reference governor able to overcome this limitation.

The growing use of air conditioning systems has become one of the main drivers of energy consumption in buildings. Many efforts are being made to develop new designs and control strategies to improve energy efficiency and minimise electricity consumption. In this work, a model for a case study of multiple-chiller-based cooling system is presented, based on surrogate models derived from information provided by manufacturers, and the study of the economic performance index. Then, an economic predictive control strategy will aim to operate the system optimizing the efficiency of the plant. Instead of the classical two-layer economic predictive control structure, where the reference to be tracked by the controller is given by a real-time optimizer, here we consider a single-layer control strategy where the gradients with respect to the manipulated inputs of the economic performance index are included in the cost function of the model predictive controller. The resulting optimization problem to be solved on line is a QP, which considerably eases the optimization problem, while also avoiding discrepancies between layers that could lead to loss of feasibility.

Modifier-Adaptation methodologies have been widely used to overcome plant-model mismatch and control a system to its steady-state optimal setpoint. They use gradient information of the real plant to design modifiers that correct the model, so that the first order necessary conditions for optimality of the model-based problem converge to those of the optimal one. In this paper, we get rid of the hypothesis that the plant optimum needs to be an equilibrium point. Instead, we only require it to be a periodic trajectory. We show the behaviour of the proposed approach by means of a motivating example that highlights the necessity of this formulation in cases where the system changes periodically through time.

How to efficiently implement Model Predictive Control (MPC) in embedded systems is a topic that is attracting a lot of research recently, due to its impact in practical applications. Implementing MPC in industrial Programmable Logic Controllers (PLCs) is of particular interest due to their widespread prevalence in the industry in comparison with other embedded systems, such as FPGAs or microcontrollers. In this paper, we present a PLC implementation of real-time embedded MPC for multivariable systems described by linear time-invariant input/output models subject to upper and lower bounds on input and output variables. The MPC algorithm uses a recently developed primal active-set method for bounded-variable least-squares problems. We highlight and address some crucial challenges that arise in implementing the MPC algorithm in a PLC. Possible extensions of the proposed methods are presented along with hardware-in-the-loop simulation results of controlling a nonlinear multivariable system using a real industrial PLC.

This article presents a sparse, low-memory footprint optimization algorithm for the implementation of the model predictive control (MPC) for tracking formulation in embedded systems. This MPC formulation has several advantages over standard MPC formulations, such as an increased domain of attraction and guaranteed recursive feasibility even in the event of a sudden reference change. However, this comes at the expense of the addition of a small amount of decision variables to the MPC's optimization problem that complicates the structure of its matrices. We propose a sparse optimization algorithm, based on an extension of the alternating direction method of multipliers, that exploits the structure of this particular MPC formulation. We describe the controller formulation and detail how its structure is exploited by means of the aforementioned optimization algorithm. We show closed-loop simulations comparing the proposed solver against other solvers and approaches from the literature., Comment: Accepted version of the article published in IEEE Transactions on Control Systems Technology (8 pages, 5 figures)

The explicit reference governor (ERG) is an add-on unit that provides constraint handling capabilities to pre-stabilized systems. In particular, the ERG acts on the reference of the pre-stabilized system in such a way that, even for large transients, constraints are satisfied. A standard way to build an ERG is to translate state and input constraints into a constraint on the value of the Lyapunov function associated to the currently applied reference. The main challenge of this approach is the determination of the largest reference-dependent Lyapunov level set that ensures constraint satisfaction. In general, the optimization problem to compute such a level set is non-convex. This letter proposes a novel systematic approach for designing an ERG for linear systems subject to combinations of intersections and unions of concave constraints. More precisely, first, it is shown that the solution of the stated non-convex problem can be approximated by solving multiple convex problems (one convex optimization problem for each constraint) and then suitably combining their solutions. Then, since these convex problems do not admit a closed-form solution, a virtual continuous-time system is proposed to estimate their solutions. Finally, an upper-bound for the estimation error is provided analytically, and a procedure is proposed to increase the robustness of the ERG against estimation errors. The effectiveness of the proposed scheme is demonstrated on a simulated case study.

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.

Data centers are energy-hungry infrastructures that provide cloud computing services. The growing number of data centers in use has led to a drastic increment of the energy consumption associated to these facilities, causing environmental concerns. For that reason, efficient management strategies are needed in order to reduce the energy consumption while the quality of service is kept. This paper presents a unified management approach for the thermal and workload distribution problem in data centers, shaped as a Model Predictive Control problem. The corresponding optimization problem is intractable for conventional solvers because the model is based on multiple queues and the decision variables are a mix of integer and real valued ones. A highly parallelizable particle based optimization algorithm is proposed to solve the optimization problem. Numerical simulations are provided in order to illustrate the effectiveness of the strategy.

Enthusiasm in speech has a huge impact on listeners. Students of enthusiastic teachers show better performance. Leaders that are enthusiastic influence employee's innovative behavior and can also spark excitement in customers. We, at TalkMeUp, want to help people learn how to talk with enthusiasm in order to spark creativity among their listeners. In this work we want to present a multimodal speech analysis platform. We provide feedback on enthusiasm by analyzing eye contact, facial expressions, voice prosody, and text content.

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.

This paper presents a novel model predictive control (MPC) formulation for set-point tracking. Stabilizing predictive controllers based on terminal ingredients may exhibit stability and feasibility issues in the event of a reference change for small to moderate prediction horizons. In the MPC for tracking formulation, these issues are solved by the addition of an artificial equilibrium point as a new decision variable, providing a significantly enlarged domain of attraction and guaranteeing recursive feasibility for any reference change. However, it may suffer from performance issues if the prediction horizon is not large enough. This paper presents an extension of this formulation where a harmonic artificial reference is used in place of the equilibrium point. The proposed formulation achieves even greater domains of attraction and can significantly outperform other MPC formulations when the prediction horizon is small. We prove the asymptotic stability and recursive feasibility of the proposed controller, as well as provide guidelines for the design of its main ingredients. Finally, we highlight its advantages with a case study of a ball and plate system., Comment: Accepted version of the article published in IEEE Transactions on Automatic Control (14 pages, 11 figures)

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

Cuenta con otro ed.: IFAC-PapersOnLine Incluída en el vol. 53, Issue 2 Article number 145388 This paper presents a predictive controller whose model is based on input-output data of the nonlinear system to be controlled. It uses a Lipschitz interpolation technique in which new data may be included in the database in real time, so the controller improves the system model online. An exploration and exploitation policy is proposed, allowing the controller to robustly and cautiously steer the system to the best reachable reference, even if the model lacks data in such region. The conditions needed to ensure recursive feasibility in the presence of output and input constraints and in spite of the uncertainties are given. The results are illustrated in a simulated case study. Feder (UE) DPI2016-76493-C3-1-R

This paper deals with an economic predictive controller for the optimal operation of a plant under the assumption that the only measurement of the system is the economic cost function to be minimized. In order to predict the evolution of the economic cost for a given input trajectory, an oracle with a NARX structure is proposed. Sufficient conditions to ensure the existence of such oracle are given, and based on this oracle, a novel predictive controller is proposed. Under certain assumptions, including ideal accurate estimation, it is proven that the proposed oracle-based economic predictive controller provides the same solution of a standard economic MPC based on the model plant, inheriting the properties of this class of controllers. The proposed oracle-based economic predictive controller is applied to a quadruple-tank process example.

One of the challenges of model predictive control is achieving a large domain of attraction with a small prediction horizon, in order to reduce the computation time and ease its implementation in embedded platforms. The domain of attraction can be enlarged by increasing the prediction horizon, at the expense of an increase in the number of decision variables, or by enlarging the terminal set. In MPC for tracking the terminal set is enlarged by the addition of an artificial equilibrium point as a decision variable, while maintaining stability of the closed loop system. In this paper we propose an extension of the MPC for tracking formulation that adds a single harmonic signal as an artificial reference. We show that a significant increase of the domain of attraction is achieved with the addition of a low number of decision variables, especially for low values of the prediction horizon.

Fast gradient methods (FGM) are very popular in the field of large scale convex optimization problems. Recently, it has been shown that restart strategies can guarantee global linear convergence for non-strongly convex optimization problems if a quadratic functional growth condition is satisfied [1], [2]. In this context, a novel restart FGM algorithm with global linear convergence is proposed in this paper. The main advantages of the algorithm with respect to other linearly convergent restart FGM algorithms are its simplicity and that it does not require prior knowledge of the optimal value of the objective function or of the quadratic functional growth parameter. We present some numerical simulations that illustrate the performance of the algorithm.

Indoor fans are high-authority actuators in heating, ventilation, and air conditioning (HVAC) systems since they facilitate the transfer of heat between refrigerant and room air. In some variable refrigerant flow (VRF) systems, the indoor fan speeds are under the control of the occupants, rather than the HVAC control system. This paper studies the benefits of transferring control of the indoor fans to the HVAC controller. We quantify the system performance using five metrics related to occupant comfort and power consumption. The first metric measures the ability of the HVAC system to accommodate users with different temperature preferences by quantifying the largest difference in requested room temperatures that can be achieved with and without the aid of indoor fans. The second and third metrics measure the ability of the HVAC system to reject extreme heating and cooling loads. The final two metrics measure the reduction in power consumption obtained by manipulating the indoor fan speeds. Each of these metrics is computed via linear programming for varying numbers of indoor units. Simulation results indicate that the maximum steady-state difference in room temperatures is tripled, and the maximum rejected heating and cooling loads are doubled. Furthermore, power consumption is significantly reduced.

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

Several studies have shown that intrastriatal application of 1-methyl-4-phenylpyridinium (MPP+) produces similar biochemical changes in rat to those seen in Parkinson's disease (PD), such as dopaminergic terminal degeneration and consequent appearance of motor deficits, making the MPP+ lesion a widely used model of parkinsonism in rodents. Previous results from our group have shown a neuroprotective effect of the carboxyl-terminal domain of the heavy chain of tetanus toxin (Hc-TeTx) under different types of stress. In the present study, pretreatment with the intraperitoneal injection of Hc-TeTx in rats prevents the decrease of tyrosine hydroxylase immunoreactivity in the striatum due to injury with MPP+, when applied stereotaxically in the striatum. Similarly, striatal catecholamine contents are restored, as well as the levels of two other dopaminergic markers, the dopamine transporter (DAT) and the vesicular monoamine transporter-2 (VMAT-2). Additionally, uptake studies of [3H]-dopamine and [3H]-MPP+ reveal that DAT action is not affected by Hc-TeTx, discarding a protective effect due to a reduced entry of MPP+ into nerve terminals. Behavioral assessments show that Hc-TeTx pretreatment improves the motor skills (amphetamine-induced rotation, forelimb use, and adjusting steps) of MPP+-treated rats. Our results lead us to consider Hc-TeTx as a potential therapeutic tool in pathologies caused by impairment of dopaminergic innervation in the striatum, as is the case of PD.

We present an output feedback nonlinear model predictive control approach that uses a Gaussian process model for prediction. We show nominal stability assuming that the Gaussian process model is able to represent the real process and establish input-to-state stability assuming a bounded error between the real process and the Gaussian model approximation. These results are achieved using a predictive control formulation without terminal region. The approach is illustrated using a continuous stirred-tank reactor benchmark problem.

In this paper, we propose an extension to the existing Model Predictive Control scheme for tracking. This extension is able to provide a solution for the case where the set of steady-state admissible outputs is non-convex. This is achieved by means of a transformation that maps the output set into a convex set. In the proposed scheme, the cost function and constraints of the usual tracking MPC are modified, so that the controller can drive the system to any point in the admissible steady-state domain without violating any constraints. The paper discusses the feasibility and stability of the proposed approach and a final simulation demonstrates the effectiveness of the approach.

This paper presents an economic model predictive controller, under the assumption that the only measurable signal of the plant is the economic cost to be minimized. In order to forecast the evolution of this economic cost for a given input trajectory, a prediction model with a NARX structure, the so-called oracle, is proposed. Sufficient conditions to ensure the existence of such oracle are studied, proving that it can be derived for a general nonlinear system if the economic cost function is a Morse function. Based on this oracle, economic model predictive controllers are proposed, and their stability is demonstrated in nominal conditions under a standard dissipativity assumption. The viability of these controllers in practical settings (where the oracle may provide imperfect predictions for generic inputs) is proven by means of input-to-state stability. These properties have been illustrated in a case study based on a continuously stirred tank reactor., Preprint submitted to Computers & Chemical Engineering. 19 pages, 6 figures

Article number 109614 The goal of this paper is to present a systematic method to compute reference dependent positively in- variant sets for systems subject to constraints. To this end, we first characterize these sets as level sets of reference dependent Lyapunov functions. Based on this characterization and using Sum of Squares theory, we provide a polynomial certificate for the existence of such sets. Subsequently, through some algebraic manipulations, we express this certificate in terms of a Semi-Definite Programming problem which maximizes the size of the resulting reference dependent invariant sets. We then present some results implementing the proposed method to an example and propose some variants that may help in reducing possible numerical issues. Finally, the proposed approach is employed in the Model Predictive Control for Tracking scheme to compute the terminal set, and in the Explicit Reference Governor framework to compute the so-called Dynamic Safety Margin. The effectiveness of the proposed method in each of the schemes is demonstrated through simulation studies. Ministerio de Economía y Competitividad de España DPI2016-76493-C3-1-R Ministerio de Ciencia e Innovación (España) PID2019-106212RB-C41

In this paper, we propose a novel robust model predictive controller for tracking periodic signals for linear systems subject to bounded additive uncertainties based on nominal predictions and constraint tightening. The proposed controller joins optimal periodic trajectory planning and robust control for tracking in a single optimization problem and guarantees that the perturbed closed-loop system converges asymptotically to a neighborhood of an optimal reachable periodic trajectory while robustly satisfying the constraints. In addition, the closed-loop system maintains recursive feasibility even in the presence of sudden changes in the target reference.

Aggregation of the amyloid beta (Aβ) peptide and hyperphosphorylation of tau protein, which are markers of Alzheimer's disease (AD), have been reported also in diabetes mellitus (DM). One regulator of tau phosphorylation is O-GlcNAcylation, whereas for hyperphosphorylation it could be GSK3beta, which is activated in hyperglycemic conditions. With this in mind, both O-GlcNAcylation and phosphorylation of tau protein were evaluated in the brain of rats with streptozotocin (STZ)-induced hyperglycemia and hyperinsulinemia and treated with the As25-35 peptide in the hippocampal region CA1. Weight, glycated hemoglobin, glucose, and insulin were determined. Male Wistar rats were divided in groups (N=20): a) control, b) treated only with the Aβ25-35 peptide, c) treated with Aβ25-35 and STZ, and d) treated only with STZ. Results showed statistically significant differences in the mean weight, glucose levels, insulin concentration, and HbA1c percentage, between C- and D-treated groups and not STZ-treated A and B (P

In this paper we propose a novel economic robust predictive controller for periodic operation. The proposed controller joins dynamic and economic trajectory planning and robust predictive controller for tracking in a single layer taking into account bounded disturbances, algebraic constraints and periodic operation. We study the closed-loop system properties of the proposed controller and provide a design procedure that guarantees that the perturbed closed-loop system converges asymptotically to the optimal economic reachable periodic trajectory, constraint satisfaction and recursive feasibility. The proposed controller has been applied to control a cluster of interconnected micro-grids. Each nano-grid is connected to an electric utility and has a renewable energy source, a cluster of batteries and a metal hydride based hydrogen storage system. The cluster must satisfy a periodic energy demand while maximizing the profit of the energy sold to the electric utility taking into account time varying prices.

This paper presents the design and realization of a linear Model Predictive Controller (MPC) and state estimator for a multi-zone heat pump in the Modelica modeling language, in order to validate closed-loop performance prior to experimental testing. The vapor compression system uses a variable speed compressor and a set of expansion valves for control, and it is required to regulate zone temperatures to set-points without offset. Constraints are imposed on all control inputs and also the values of both measured and unmeasured system outputs. Because experimental testing is both expensive and time-consuming, we have developed a tool chain for software-in-the-loop validation that uses a Modelica model for the plant, integrated with a software representation of the MPC that is realized in a combination of Modelica and C that is suitable for real-time use. We show the results of closed-loop tests of the controller with a nonlinear system model, which provide a partial validation of the controller and tool chain.

Their flexibility to learn general function classes renders nonparametric regression algorithms particularly attractive in system identification and data-based control settings, where little a priori knowledge about a dynamical system is to be presumed. Building on approaches known as NSM- or Lipschitz regression, we propose a new nonparametic machine learning approach. While it inherits theoretical learning guarantees from the methods it is built upon, it is designed to limit the computational effort both for learning and for generating predictions. This renders our method applicable to online system identification and control settings where the desired sample frequency precludes previous nonparametric approaches from being deployed. Apart from deriving a guarantee on the ability of our method to learn any continuous function, we illustrate some of its practical merits on a number of benchmark comparison problems.