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Differential dynamic programming (DDP) is a direct single shooting method for trajectory optimization. Its efficiency derives from the exploitation of temporal structure (inherent to optimal control problems) and explicit roll-out/integration of the system dynamics. However, it suffers from numerical instability and, when compared to direct multiple shooting methods, it has limited initialization options (allows initialization of controls, but not of states) and lacks proper handling of control constraints. In this work, we tackle these issues with a feasibility-driven approach that regulates the dynamic feasibility during the numerical optimization and ensures control limits. Our feasibility search emulates the numerical resolution of a direct multiple shooting problem with only dynamics constraints. We show that our approach (named BOX-FDDP) has better numerical convergence than BOX-DDP+ (a single shooting method), and that its convergence rate and runtime performance are competitive with state-of-the-art direct transcription formulations solved using the interior point and active set algorithms available in KNITRO. We further show that BOX-FDDP decreases the dynamic feasibility error monotonically--as in state-of-the-art nonlinear programming algorithms. We demonstrate the benefits of our approach by generating complex and athletic motions for quadruped and humanoid robots. Finally, we highlight that BOX-FDDP is suitable for model predictive control in legged robots., Comment: 17 pages, 9 figures, draft version

Numerous control strategies of temperature regulation have been carried out for proton-exchange membrane fuel cell systems including a cooling fan in order to ensure operation at the desired condition and extend the lifetime of the fuel cell stack. However, most existing control strategies are developed without considering the efficiency limitation of the cooling system such that the cooling fan may be unable to eliminate the additional heat. Moreover, there are unknown modelling errors, external disturbance and noise during modelling and experiment processes for fuel cells. Due to those unknown dynamics, the conventional control strategies may fail to achieve the expectant results. To address this issue, an alternative control strategy is proposed in this paper, which consists of a composite proportional-integral (PI) controller with an unknown system dynamics estimator. First, the control strategy is developed by reducing the temperature of input air through the humidifier and simultaneously increasing the mass flow of air in order to eliminate the excess heat that a cooling fan cannot remove. Moreover, an unknown system dynamics estimator is proposed in order to compensate the effect of the unknown dynamics. The construction of the estimator is designed through finding an invariant manifold which implies the relation between known variables and the unknown manifold. The invariant manifold is derived by applying a simple low-pass filter to the system which is beneficial to avoid the requirement of the unmeasurable state derivative. Furthermore, the proposed estimator is easily merged into the proposed PI control strategy and ensures the exponential convergence of estimated errors. Besides, the estimator is further modified such that the derivative of the desired temperature is not required in the controller. Finally, numerical simulations of the PEMFC system are provided and the results illustrate the efficacy of the proposed control strategy., This work was partially funded by the Spanish National Project DOVELAR (ref. RTI2018-096001-B-C32), the AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482), and the Spanish State Research Agency through the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656). )is work was partially funded by the Chinese Scholarship Council (CSC) under grant (201808390007). )is work was partially funded by National Natural Science Foundation of China under Grants 61922037 and 61873115.

This study proposes an anti-slip control system for electric trains based on the fuzzy logic theory, which prevents the wheels from slipping during the acceleration and simultaneously tracks the desired speed profile. To improve the control performance, the train longitudinal velocity and the slip ratio are estimated. By using a field-oriented control (FOC), the angular speed of the traction motor is controlled. The fuzzy control system determines the desired angular speed of the traction motor as the reference input of FOC to obtain the desired slip ratio and track the desired speed profile. Simulation results show the effectiveness of the control system in various wheel–rail surface conditions based on the real parameters of ER24PC locomotive.

This paper discusses some issues related with the design of a bank of interval observers for uncertain switched systems, in which several sources of uncertainty are considered: parametric uncertainties, unknown disturbances, measurement noise, and unknown switching signal. More specifically, this paper focuses on analyzing the interval estimation accuracy when changes of active mode induce non-positivity of the interval state estimation errors. In particular, it is shown that by combining two types of interval observers, referred to as local and global, the accuracy and reliability of the estimation can be improved. The properties of the obtained so-called glocal observer are investigated and illustrated by means of numerical simulations. This work has been supported by the Spanish State Research Agency (AEI)through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016-0656) andthe grant Juan de la Cierva-Formacion (FJCI-2016-29019), by the ResearchCouncil of Norway through the AMOS Center, grant number 223254 and bythe University of Stavanger through the project IN-12267

This paper proposes two control strategies that mitigate the cross contamination of H and O in a high-pressure alkaline electrolyzer, which consequently increases the supplied gases purity: one based on a decoupled PI scheme and the other based on optimal control tools. In order to reduce the diffusion of gases through the membrane, the controllers establish the opening of two outlet valves based on the pressure of the system and the difference in liquid level between both separation chambers. Therefore, two multiple input - multiple output controllers are designed here. For this purpose, a high-fidelity model previously developed was simplified in order to obtain a control-oriented model. The proposed controllers were evaluated in simulation using the high-fidelity nonlinear model in a wide operating range, which resulted in less than 1% impurity of gases., This work has been partially funded by the Cheerful CSIC project (MHE-200065).

In urban drainage systems (UDS), a proven method for reducing the combined sewer overflow (CSO) pollution is real-time control (RTC) based on model predictive control (MPC). MPC methodologies for RTC of UDSs in the literature rely on the computation of the optimal control strategies based on deterministic rain forecast. However, in reality, uncertainties exist in rainfall forecasts which affect severely accuracy of computing the optimal control strategies. Under this context, this work aims to focus on the uncertainty associated with the rainfall forecasting and its effects. One option is to use stochastic information about the rain events in the controller; in the case of using MPC methods, the class called stochastic MPC is available, including several approaches such as the chance-constrained MPC method. In this study, we apply stochastic MPC to the UDS using the chance-constrained method. Moreover, we also compare the operational behavior of both the classical MPC with perfect forecast and the chance-constrained MPC based on different stochastic scenarios of the rain forecast. The application and comparison have been based on simulations using a SWMM model of the Astlingen urban drainage benchmark network., Comment: A preprint of a submitted article to the Journal Control Engineering Practice

Efficiency, reliability and lifetime of polymer electrolyte membrane fuel cells (PEMFCs) are significantly limited by inadequate water management. High-performance water active control algorithms cannot be implemented due to the absence of adequate online sensors that can measure the internal liquid water saturation. A promising technique that can be applied in this context is the state observer. However, fuel cell models present strong nonlinearities, model uncertainty, unmatched unknown parameters and sensor noise, which are major difficulties in observer design. The algorithm proposed in this work is based on a time-varying adaptive observer, that offers an estimation of the liquid water state and behaviour in the cathode catalyst layer of a PEMFC, coupled with a low-power peaking-free observer with dynamic dead-zone filtering that is used as a high-performance soft sensor. The algorithm is shown to provide an accurate estimation of the liquid water saturation and the liquid water transport parameters even in the presence of sensor noise and model inaccuracies. The results are validated through numerical simulations and in a real experimental prototype., This work has been partially funded by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656) and by the project DOVELAR (ref. RTI2018-096001-B-C32).

To obtain autonomy in applications that involve Unmanned Aerial Vehicles (UAVs), the capacity of self-location and perception of the operational environment is a fundamental requirement. To this effect, GPS represents the typical solution for determining the position of a UAV operating in outdoor and open environments. On the other hand, GPS cannot be a reliable solution for a different kind of environments like cluttered and indoor ones. In this scenario, a good alternative is represented by the monocular SLAM (Simultaneous Localization and Mapping) methods. A monocular SLAM system allows a UAV to operate in a priori unknown environment using an onboard camera to simultaneously build a map of its surroundings while at the same time locates itself respect to this map. So, given the problem of an aerial robot that must follow a free-moving cooperative target in a GPS denied environment, this work presents a monocular-based SLAM approach for cooperative UAV&ndash, Target systems that addresses the state estimation problem of (i) the UAV position and velocity, (ii) the target position and velocity, (iii) the landmarks positions (map). The proposed monocular SLAM system incorporates altitude measurements obtained from an altimeter. In this case, an observability analysis is carried out to show that the observability properties of the system are improved by incorporating altitude measurements. Furthermore, a novel technique to estimate the approximate depth of the new visual landmarks is proposed, which takes advantage of the cooperative target. Additionally, a control system is proposed for maintaining a stable flight formation of the UAV with respect to the target. In this case, the stability of control laws is proved using the Lyapunov theory. The experimental results obtained from real data as well as the results obtained from computer simulations show that the proposed scheme can provide good performance.

This paper proposes the design of an interval observer-based approach for linear dynamic systems affected by both time-invariant and time-varying uncertainties. First, different interval observer schemes are compared and analysed when dealing with the different types of uncertainties. Then, an integrated interval observer is proposed in order to overcome the drawbacks of using the set-based approach, i.e. the non-preservation of the parameter uncertainty time dependency and the wrapping effect. Furthermore, H∞ performance is considered in order to compute the observer gain by using an LMI technique. Finally, a numerical example and a real case study based on a two-tank system are employed for both illustrating and analysing the effectiveness of the proposed approach., This work has been partially funded by the Spanish Government (MINECO) through the project DEOCS (ref. DPI2016-76493-C3-3-R), by MINECO and FEDER through the project SCAV (ref. MINECO DPI2017-88403-R) and by Agència de Gestió d'Ajuts Universitaris i de Recerca.

In this paper, an adaptive proportional–integral (PI) control strategy for nonlinear systems with constrained actuators is discussed. In particular, a variable-structure anti-windup strategy that can be retrofitted onto different control structures is proposed and validated. Both the developed controller and anti-windup method are attuned to an automotive industrial control application: the control of air mass flow-rate and pressure in a fuel cell system using a high speed turbo-compressor and an electromechanical valve. Both the compressor and valve are rate-limited. The results from experimental tests agree with the simulations obtained and showing the effectiveness of the proposed approach by evidencing the fast control dynamics and the proper and desired closed-loop performance of the scheme without windup phenomena., Facultad de Ingeniería, Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales

One of the relevant information provided by the prognostics and health management algorithms is the estimation of the Remaining Useful Life (RUL). The prediction of the expected RUL is very useful to decrease maintenance cost, operational downtime and safety hazards. This paper proposes a new strategy of health-aware Model Predictive Control (MPC) for a Linear Parameter Varying (LPV) system that includes as an additional goal extending the system RUL via their estimation using reliability tools. In this approach, the RUL maximization is included in the objective function of the LPV-MPC controller. The RUL is included in the MPC model as an extra parameter varying equation that considers the control action as scheduling variable. The proposed control approach allows the controller to accommodate to the parameter changes. Through computing an estimation of the state variables during prediction, the MPC model can be modified to the estimated state evolution at each time instant. Moreover, for solving the optimization problem by using a series of Quadratic Programs (QP) in each time instant, a new iterative approach is exhibited, which improves the computational efficiency. A pasteurization plant control system is used as a case study to illustrate the performance of the proposed approach., This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the projects DEOCS (ref. MINECO DPI2016- 76493) and SCAV (ref. MINECO DPI2017-88403-R). This work has also been partially funded by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482) and by Agencia de Gestio d’Ajuts Universitaris i de Recerca

This paper establishes a methodology based on linear matrix inequalities (LMIs) to design a shifting H infinity linear parameter varying (LPV) state-feedback controller for systems affected by time-varying input saturations. By means of the shifting paradigm, the instantaneous saturation values are linked to a scheduling parameter vector. Then, the disturbance rejection is dealt with the quadratic boundedness concept and the shifting H infinity methodology. The design conditions are obtained within the LPV framework using ellipsoidal invariant sets, thus obtaining an LMI-based feasibility problem that can be solved via available solvers. Finally, the main characteristics of the proposed approach are validated by means of an illustrative example. This work has been partially funded by the Spanish State Re-search Agency (AEI) and the European Regional Development Fund(ERFD) through the project SCAV (ref. MINECO DPI2017-88403-R) and the SMART project (ref. nºEFA 153/16 Interreg Coop-eration Program POCTEFA 2014-2020). This work has also beenpartially funded by AGAUR of Generalitat de Catalunya throughthe Advanced Control Systems (SAC) group grant (2017 SGR 482)and by the Spanish State Research Agency through the Maria deMaeztu Seal of Excellence to IRI (MDM-2016-0656). A. Ruiz isalso supported by Formaci ́o Personal Investigador a la UniversitatPolit`ecnica de Catalunya under an FPI-UPC 2018 grant (ref. 20 FPI-UPC 2018). D. Rotondo has been also supported by AEI through theJuan de la Cierva - Formacion grant (FJCI-2016-29019) and by theUniversity of Stavanger through the project IN-12267

Trabajo presentado en la IEEE International Conference on Systems, Man, and Cybernetics (SMC), celebrada en Toronto (Canadá), del 11 al 14 de octubre de 2020, This paper exploits the analogy between the stochastic and zonotopic Kalman filters with the aim of formulating a metric that allows to assess the impact of an external zonotopically bounded signal in an state estimate optimal control scheme. To that end, the concept of linear quadratic zonotopic (LQZ) controller is introduced. Besides, the design of a zonotopically bounded watermarking signal such that enforces replay attack detectability is also addressed. The proposed detection scheme takes advantage of the observability loss of an a priori known exogenous signal during the replay phase of the attack. Consequently, by injecting the corresponding estimation error generated by a set-based observer, a zonotopically bounded signal such that imposes replay attack detectability is achieved. A numerical example is provided.

Under the dual pressure of water environment and water safety in wet weather, how to improve the reliability, flexibility and sustainability of urban drainage system is increasingly important to ensure the safety of urban water system. The urban drainage system has characteristics of dynamic, multi-objective and uncertainty. From the perspective of watershed treatment, under the multi-objective coordination of further reducing the combined system overflow pollution and maximizing the "source -process-end" capacity of drainage system, the operation and control of the system become increasingly complex and difficult. Real time control (RTC) maximizes the storage and treatment capacity of facilities, by dynamically adjusting the control strategy according to the real-time monitoring data, and intervening in the ancillary facilities of drainage system and wastewater treatment plant, in order to provide an intelligent solution for the operation of urban drainage system. Therefore, this paper summarizes the research progress of RTC, the development of control process and strategy, control model and control algorithm, and the inspiration for applying RTC in China.

Active fault detection facilitates determination of the fault characteristics by injecting proper auxiliary input signals into the system. This article proposes an observer-based on-line active fault detection method for discrete-time systems with bounded uncertainties. First, the output including disturbances, measurement noise and interval uncertainties at each sample time is enclosed in a zonotope. In order to reduce the conservativeness in the fault detection process, a zonotopic observer is designed to estimate the system states allowing to generate the output zonotopes. Then, a proper auxiliary input signal is designed to separate the output zonotopes of the faulty model from the healthy model that is injected into the system to facilitate the detection of small fault. Since the auxiliary input signal generation leads to a nonconvex optimization problem, it is transformed into a mixed integer quadratic programming problem. Finally, a case study based on a DC motor is used to show the effectiveness of the proposed method., Research foundation for talents of NCUT, 213051360020XN173/017, 110051360020XN092; National Natural Science Foundation of China, 51805021; 61973023, 61573050; the Fundamental Research Funds for the Central Universities, XK1802‐4

This paper presents the design of an input sequence in order to actively guarantee detectability of integrity attacks. The design of the input sequence is formulated as an optimization problemwhere the performance degradation imposed in the protected system is minimized while guaranteeing attack detectability by separating the reachable sets of the system in healthy and attacked operation. By considering uncertainties bounded by zonotopes, the design of anoptimal open-loop input sequence such that guarantee the separability of the reachable zonotopicsets can be computed by solving a Mixed Integer Quadratic Program (MIQP). Following thisapproach, attack detection can be guaranteed by: I) forcing a distinct behavior of the systemoutputs; II) ensuring that residuals under attack will exit the healthy residual set. Furthermore, the present work also considers the imposition of residuals detectability for the specific replay attack scenario affecting an state estimate control system. The effectiveness of the proposals is validated in simulation by means of a numerical example. This work has been partially funded by AGAUR ACCIO RIS3CATUTILITIES 4.0 – P7 SECUTIL

In this report a new approach of failure rate prediction is presented based on Fuzzy Clustering technic for a more deterministic and accurate implementation of neuro-fuzzy systems. This technique is compared with two benchmark methods: Artificial Neural Networks (ANN) and Adaptative Neuro-Fuzzy Inference Systems (ANFIS). Furthermore, an analysis of the necessary inputs is carried out with the goal of defining the useful information needed for the models. All these methods are applied to real data of Barcelona water distribution system and the models predictions are compared with calculated pipe failure rate.

This paper presents a literature review about the concept of redox flow batteries and its automation and monitoring. Specifically, it is focused on the presentation of all-vanadium redox flow batteries which have several benefits, compared with other existing technologies and methods for energy stored purposes. The main aspects that are reviewed in this work correspond to the characterization, modeling, supervision and control of the vanadium redox flow batteries. A research is presented where redox flow batteries are contextualized in the current energy situation, compared with other types of energy storage systems. Furthermore, a presentation about the current challenges on research, and the main existing installations is view. A discussion about the main dynamic models that have been proposed during last years, as well as the different control strategies and observers, is presented., This research was funded by the CSIC under the PTI FLOWBAT 2021 project (reference: 642 201980E101), the Spanish Ministry of Economy and Competitiveness under Projects CICYT RTI2018-094665-B-I00, DOVELAR ref. RTI2018-096001-B-C32 (MCIU/AEI/FEDER, UE) and María de Maeztu Seal of Excellence to IRI (MDM-2016-0656), and by the Generalitat de Catalunya through the Project 2017 SGR 482.

Trabajo presentado en la IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), celebrada en Macao (China), del 3 al 8 de noviembre de 2019, This paper proposes the use of piecewise Cn smooth curve for mobile-base motion planning and control, coined Timed-Elastic Smooth Curve (TESC) planner. Based on a Timed-Elastic Band, the problem is defined so that the trajectory lies on a spline in SE(2) with non-vanishing n-th derivatives at every point. Formulated as a multi-objective nonlinear optimization problem, it allows imposing soft constraints such as collision-avoidance, velocity, acceleration and jerk limits, and more. The planning process is realtime-capable allowing the robot to navigate in dynamic complex scenarios. The proposed method is compared against the state-of-the-art in various scenarios. Results show that trajectories generated by the TESC planner have smaller average acceleration and are more efcient in terms of total curvature and pseudo-kinetic energy while being produced with more consistency than state-of-the-art planners do., This work was partially supported by the EU H2020 project LOGIMATIC (H2020-Galileo-2015-1-687534), and by the Spanish State Research Agency through projects EB-SLAM (DPI2017-89564-P) and the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656).

In this paper, we study robust invariant set characterizations of discrete-time descriptor systems and propose an active mode detection mechanism for discrete-time descriptor systems considering multiple modes of operation. The considered class of descriptor systems assumes regularity, stability and is affected by unknown-but-bounded disturbances. As a first theoretical result, we establish a general framework for robust invariant sets for discrete-time descriptor systems in both causal and non-causal cases. Particular transformations are subsequently proposed for handling causal and non-causal descriptor systems and will be used to characterize the effects of disturbances. Based on these set-theoretic notions and a designed input signal for active set separations, we propose an active mode detection mechanism by exploiting the strong invariance properties., This work was partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project DEOCS (ref. DPI2016-76493-C3-3-R), the FPI grant (ref. BES-2014-068319), the FPI mobility grants (ref. EEBB-I-17-12257 and EEBB-I-18-12900), and by a grant of Ministry of Research and Innovation, CNCS - UEFISCDI, Romania project number PN-III-P1-1.1-TE-2016-0862, MOSCBIOS, within PNCDI III.

This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalised flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamical allocation of safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators’ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the case study considered.

Trabajo presentado en la 20th IEEE International Conference on Industrial Technology, celebrada en Melbourne (Australia) del 13 al 15 de febrero de 2019, Uncertain generation by renewable sources and load variations have resulted in adding energy storage systems in the grid to maintain grid parameters (voltage, frequency) within prescribed limits. The disturbances being non-deterministic in nature, the voltage regulation control by the storage systems relies mostly on dual loop architecture with an outer voltage and inner current loop. Improvement in controller dynamics can be achieved through feed forward of disturbance profile but at expense of additional sensors and communication in the grid. This work explores the application of an adaptive disturbance rejection control scheme for disturbance estimation (without using additional sensors) employing an extended state and proportional integral observer (PI+ESO). The proposed observer aim to achieve robust disturbance estimation under grid parameter uncertainty. The effectiveness of the proposed scheme over the conventional one will be put forward through H¿ and H2 norm analysis of the system. The design and simulation results of the proposed scheme will be presented in this work., This work is done as part of project which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement No 675318 (INCITE). This work has been partially funded by the MINECO/FEDER project DPI2015-69286-C3-2-R. This work is supported by the Spanish State Research Agency through the Mara de Maeztu Seal of Excellence to IRI (MDM-2016-0656). This work is partially funded by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482)

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC)., This work has been supported by Tecnológico Nacional de México grants 6723.18-P and 5400.19-P, the Instituto de Ciencia Tecnológia e Innovación, Chiapas (ICTIECH) 1123/2019. This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the projects SCAV (ref. MINECO DPI2017-88403-R) and DEOCS (ref. MINECO DPI2016-76493), and also by AGAUR ACCIO RIS3CAT UTILITIES 4.0 – P7 SECUTIL. This work has been also supported by the AEI through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016-0656) and the grant Juan de la Cierva-Formacion (FJCI-2016-29019).

Trabajo presentado en la 4th Conference on Control and Fault Tolerant Systems (SysTol), celebrada en Casablanca (Marruecos), del 18 al 20 de septiembre de 2019, This paper proposes a fault-tolerant control (FTC) strategy based on virtual actuators for discrete-time descriptor systems subject to multiplicative and additive actuator faults. The goal of this FTC strategy is to reconfigure the closed-loop system by means of a virtual actuator that allows maintaining the use of the nominal controller without having to retune it. To achieve this goal, we define the structure of a dynamical virtual actuator for descriptor systems including multiplicative and additive actuator faults. In order to use a state-feedback controller for implementing the FTC strategy, a state observer for descriptor systems is also defined. Under the proposed FTC scheme, the separation principle of the virtual actuator, the nominal closed-loop system and the state observer is established. Therefore, all the gains can be designed separately. Finally, a numerical example is provided to show the effectiveness of the proposed FTC strategy., This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project DEOCS (ref. DPI2016-76493-C3-3-R), the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656), and the grant Juan de la Cierva-Formacion (FJCI-2016-29019), and by the ´ Fundamental Research Funds for the Central University (No. 3072019CFJ0406).

A novel partitioning approach for linear switching large-scale systems is presented. We assume that the modes of the switching system are unknown a priori but can be detected. We propose an online partitioning scheme that can partition the system when the mode switches, thus adapting the partition to the mode. Moreover, after the system has been partitioned, we apply a decentralized state-feedback control scheme to stabilize the system. We also apply a dwell time stability scheme to prove that the closed-loop system remains stable even after both the mode and partition changes. The proposed approach is illustrated by means of an automatic generation control problem related to frequency deviation regulation in a large-scale power network., This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 675318 (INCITE). (Wicak Ananduta and Tomás Pippia are co-first authors.)

Trabajo presentado en el 3rd IFAC Workshop on Linear Parameter-Varying Systems, celebrado en Eindhoven (Países Bajos), del 4 al 6 de noviembre de 2019, A nonlinear system is said to be quadratically bounded (QB) if all its solutions are bounded and this is guaranteed using a quadratic Lyapunov function. This paper considers the QB analysis and state-feedback controller design problems for quadratic parameter varying (QPV) systems. The developed approach, which relies on a linear matrix inequality (LMIs) feasibility problem, ensures that the QB property holds for an invariant ellipsoid which contains a predefined polytopic region of the state space. An example is used to illustrate the main characteristics of the proposed approach and to confirm the validity of the theoretical results.

The energy management strategy plays a major role in hybrid platforms powered by fuel cells (FCs) and batteries. This paper presents an assessment of energy management focused on fuel economy and battery degradation. Particularly, a proposed heuristic strategy and the widely known equivalent consumption minimization strategy are compared with the optimal solution obtained offline via dynamic programming. The case study is based on a real FC hybrid vehicle. Accordingly, the powertrain model of the vehicle used for the simulations is validated experimentally, and the profile of the power demand is measured from the real application. The results show that the proposed strategy offers the same performance as the equivalent consumption minimization strategy when the battery degradation is prioritized, and in comparison with the optimal off-line solution, it can be seen that there is still margin for improvement in terms of battery degradation., This work was supported in part by the Scholarship Program BECAR of Ministerio de Modernizacion of Argentina, under Project DPI2015-69286-C3-1-R and Project DPI2015-69286-C3-2-R (MINECO/FEDER) of the Spanish Government, in part by the European Commission H2020 through the Fuel Cell and Hydrogen Joint Undertaking Project under Project INN-BALANCE 735969 and Project REWIND LIFE13 ENV/ES/000280, in part by the Regional Government of Aragon to the Fluid Mechanics for a Clean Energy Research Group of LIFTEC under Grant T01_17R, in part by the Spanish State Research Agency through the Marí de Maeztu Seal of Excellence to IRI under Grant MDM-2016-0656, and in part by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) Group under Grant 2017 SGR 482.

Trabajo presentado en la 25th Ka and Broadband Communications Conference, celebrada en Sorrento (Italia), del 30 de septiembre al 2 de noviembre de 2019, GAUSS (Galileo-EGNOS as an Asset for UTM Safety and Security) is a H2020 project1 that aims at designing and developing high performance positioning systems for drones within the U-Space framework focusing on UAS (Unmanned Aircraft System) VLL (Very Low Level) operations. The key element within GAUSS is the integration and exploitation of Galileo and EGNOS exceptional features in terms of accuracy, integrity and security, which will be key assets for the safety of current and future drone operations. More concretely, high accuracy, authentication, precise timing (among others) are key GNSS (Global Navigation Satellite System) enablers of future integrated drone operations under UTM (UAS Traffic Management) operations, which in Europe will be deployed under U-Space [1]. The U-Space concept helps control, manage and integrate all UAS in the VLL airspace to ensure the security and efficiency of UAS operations. GAUSS will enable not only safe, timely and efficient operations but also coordination among a higher number of RPAS (Remotely Piloted Aircraft System) in the air with the appropriate levels of security, as it will improve anti-jamming and anti-spoofing capabilities through a multi-frequency and multi-constellation approach and Galileo authentication operations. The GAUSS system will be validated with two field trials in two different UTM real scenarios (in-land and sea) with the operation of a minimum of four UTM coordinated UAS from different types (fixed and rotary wing), manoeuvrability and EASA (European Aviation Safety Agency) operational categories. The outcome of the project will consist of Galileo-EGNOS based technological solutions to enhance safety and security levels in both, current UAS and future UTM operations. Increased levels of efficiency, reliability, safety, and security in UAS operations are key enabling features to foster the EU UAS regulation, market development and full acceptance by the society., This project receives funding from the EU H2020 Research & Innovation Programme under grant agreement No 776293

Distributed energy management of interconnected microgrids that is based on model predictive control (MPC) relies on the cooperation of all agents (microgrids). This paper discusses the case in which some of the agents might perform one type of adversarial actions (attacks) and they do not comply with the decisions computed by performing a distributed MPC algorithm. In this regard, these agents could obtain a better performance at the cost of degrading the performance of the network as a whole. A resilient distributed method that can deal with such issues is proposed in this paper. The method consists of two parts. The first part is to ensure that the decisions obtained from the algorithm are robustly feasible against most of the attacks with high confidence. In this part, we formulate the economic dispatch problem, taking into account the attacks as a chance-constrained problem, and employ a two-step randomization-based approach to obtain a feasible solution with a predefined level of confidence. The second part consists in the identification and mitigation of the adversarial agents, which utilizes hypothesis testing with Bayesian inference and requires each agent to solve a mixed-integer problem to decide the connections with its neighbors. In addition, an analysis of the decisions computed using the stochastic approach and the outcome of the identification and mitigation method is provided. The performance of the proposed approach is also shown through numerical simulations., This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska‐Curie Grant 675318 (INCITE) and has been also supported by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM‐2016‐0656). Financial support by the Spanish MINECO project DPI2017‐86918‐R and the Japanese Society for the Promotion of Science (scholarship PE16048) is also gratefully acknowledged. The authors also acknowledge JST CREST Grants JPMJCR15K3 and JPMJCR15K5.

This paper presents a bibliographical review of definitions, classifications and applications concerning cyber attacks in networked control systems (NCSs) and cyber-physical systems (CPSs). This review tackles the topic from a control-oriented perspective, which is complementary to information or communication ones. After motivating the importance of developing new methods for attack detection and secure control, this review presents security objectives, attack modeling, and a characterization of considered attacks and threats presenting the detection mechanisms and remedial actions. In order to show the properties of each attack, as well as to provide some deeper insight into possible defense mechanisms, examples available in the literature are discussed. Finally, open research issues and paths are presented., This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the projects SCAV (ref. MINECO DPI2017-88403-R) and DEOCS (ref. MINECO DPI2016-76493), and also by AGAUR ACCIO RIS3CAT UTILITIES 4.0 P7 SECUTIL. This work has been also supported by the AEI through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016-0656) and the grant Juan de la Cierva-Formacion (FJCI-2016-29019).

Trabajo presentado en el 18th European Control Conference (ECC), celebrado en Nápoles (Italia), del 25 al 28 de junio de 2019, This paper addresses the problem of state observation in quadratic parameter varying (QPV) systems. In particular, a state observer is designed in such a way that the estimation error converges to zero with a desired rate of convergence in a given polytopic region of the error space. Under some assumptions, it is shown that design conditions can be given in the form of a set of bilinear matrix inequalities (BMIs), which can be reduced to linear matrix inequalities (LMIs), which are computationally more tractable. The main characteristics of the proposed approach are illustrated by means of an example, which confirms the validity of the theoretical results., This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the projects SCAV (ref. MINECO DPI2017-88403-R) and DEOCS (ref. MINECO DPI2016-76493). This work has been also supported by the AEI through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016- 0656) and the grant Juan de la Cierva-Formacion (FJCI-2016-29019), and by the Research Council of Norway through the AMOS Center, grant number 223254.

© 2020 by the authors., This paper addresses the energy management of a standalone renewable energy system. The system is configured as a microgrid, including photovoltaic generation, a lead-acid battery as a short term energy storage system, hydrogen production, and several loads. In this microgrid, an energy management strategy has been incorporated that pursues several objectives. On the one hand, it aims to minimize the amount of energy cycled in the battery, in order to reduce the associated losses and battery size. On the other hand, it seeks to take advantage of the long-term surplus energy, producing hydrogen and extracting it from the system, to be used in a fuel cell hybrid electric vehicle. A crucial factor in this approach is to accommodate the energy consumption to the energy demand and to achieve this, a model predictive control (MPC) scheme is proposed. In this context, proper models for solar estimation, hydrogen production, and battery energy storage will be presented. Moreover, the controller is capable of advancing or delaying the deferrable loads from its prescheduled time. As a result, a stable and efficient supply with a relatively small battery is obtained. Finally, the proposed control scheme has been validated on a real case scenario., This work has been partially funded by the Spanish national project DOVELAR ref. RTI2018-096001-B-C32 (MCIU/AEI/FEDER, UE). This work is supported by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). This work is partially funded by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482). This work has been done with the support of ACCIÓ (Operational Program FEDER Catalunya 2014-2020) through the REFER project (COMRDI15-1-0036-11).

Detecting and tracking moving objects (DATMO) is an essential component for autonomous driving and transportation. In this paper, we present a computationally low-cost and robust DATMO system which uses as input only 2D laser rangefinder (LRF) information. Due to its low requirements both in sensor needs and computation, our DATMO algorithm is meant to be used in current Autonomous Guided Vehicles (AGVs) to improve their reliability for the cargo transportation tasks at port terminals, advancing towards the next generation of fully autonomous transportation vehicles. Our method follows a Detection plus Tracking paradigm. In the detection step we exploit the minimum information of 2D-LRFs by segmenting the elements of the scene in a model-free way and performing a fast object matching to pair segmented elements from two different scans. In this way, we easily recognize dynamic objects and thus reduce consistently by between two and five times the computational burden of the adjacent tracking method. We track the final dynamic objects with an improved Multiple-Hypothesis Tracking (MHT), to which special functions for filtering, confirming, holding, and deleting targets have been included. The full system is evaluated in simulated and real scenarios producing solid results. Specifically, a simulated port environment has been developed to gather realistic data of common autonomous transportation situations such as observing an intersection, joining vehicle platoons, and perceiving overtaking maneuvers. We use different sensor configurations to demonstrate the robustness and adaptability of our approach. We additionally evaluate our system with real data collected in a port terminal the Netherlands. We show that it is able to accomplish the vehicle following task successfully, obtaining a total system recall of more than 98% while running faster than 30 Hz, This work has been partly funded by the EU project CargoANTs FP7-SST-2013-605598, the Spanish MINECO projects DPI2016-78957-R (AEI/FEDER EU) and Unidad de Excelencia Maria de Maeztu 2016 (MDM-2016-0656).

Information sharing among local controllers is the key feature of any distributed model predictive control (DMPC) strategy. This study addresses the problem of communication failures in DMPC strategies and proposes a distributed solution to cope with them. The proposal consists in an information-exchange protocol that is based on distributed projection dynamics. By applying this protocol as a complementary plug-in to a DMPC strategy, the controllers improve the resilience against communication failures and relax the requirements of the communication network. Furthermore, a reconfiguration algorithm, which is a contingency procedure to maintain the connectivity of the network, and a discussion on the selection criteria of the information-sharing network are also presented. In order to demonstrate the performance and advantages of the proposed approach when it is applied to a DMPC strategy, a case study of a power-network control problem is provided., The work of W. Ananduta has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 675318 (INCITE). J. BarreiroGomez acknowledges U.S. Air Force Office of Scientific Research under grant number FA9550-17-1-0259. N. Quijano acknowledges SGR C/marca project 2015-2017.

In this study, an algebraic-observer-based output-feedback controller is proposed for a proton exchange membrane fuel cell (PEMFC) air-supply subsystem, based on both algebraic differentiation and sliding-mode control approaches. The goal of the design is to regulate the oxygen excess ratio (OER) towards its optimal set point value in the PEMFC air-supply subsystem. Hence, an algebraic estimation approach is used to reconstruct the OER based on a robust differentiation method. The proposed observer is known for its finite-time convergence and low computational time compared to other observers presented in the literature. Then, a twisting controller is designed to control the OER by manipulating the compressor motor voltage. The parameters of the twisting controller have been calculated by means of an off-line tuning procedure. The performance of the proposed algebraic-observer-based output-feedback controller is analysed through simulations for different stack-current changes, for parameter uncertainties, and for noise rejection. Results show that the proposed approach properly estimates and regulates the OER in finite-time., This work was supported by Laboratoire d’Analyse et de Commande des Systèmes d’Énergies et Réseaux Électriques. Université Amar Telidji de Laghouat, Laghouat 03000, Algérie. The work of C. Ocampo-Martinez is partially supported by the project DEOCS (Ref. DPI2016-76493-C3-3-R) from the Spanish MINECO/FEDER

This paper studies a set-based unknown input observer based on zonotopes for discrete-time descriptor systems affected by uncertainties with application to state estimation and robust fault detection. In this paper, two types of uncertainties are considered: (i) disturbances and noise both bounded by zonotopes; (ii) unknown inputs that can be decoupled. In terms of different applications, the observer gain for state estimation is designed to minimize the effects of unknown-but-bounded disturbances and noise as well as state estimation errors. On the other hand, for robust fault detection, in addition to attenuating uncertainties, the designed observer gain is also expected to be sensitive to faults. To achieve this goal, we propose an iterative algorithm to design the fault detection gain. Finally, some illustrative results in an application example show the effectiveness of the proposed algorithms., This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project DEOCS (ref. DPI2016-76493) and SCAV (ref. DPI2017-88403-R), the FPI grant (ref. BES-2014-068319), and by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (ref. 2017-SGR-482).

Hybrid controllers are capable of improved performance over their linear counterparts. In particular, reset controllers like the PI+CI are capable of fast flat response for lag dominant plants. Grid connected power converters especially interfacing energy storage systems to grids are required to have fast response to varying load demands to ensure minimum variation in grid parameters. Application of PI+CI controllers in such systems can improve their performance. In this work the improvement brought about by use of PI+CI controller employed for energy storage system power converters is highlighted by comparing it with PI controller based system under load variations. A DC microgrid with Fuel cell-supercapacitor based storage elements are considered here. The design criteria and simulation results are presented here., This work was partially supported, including FEDER co-funding, by the Spanish Ministerio de Educacin project DPI2015-69286-C3-2-R (MINECO/FEDER), project DPI2013-47100-C2-1-P, and project DPI2016- 79278-C2-1-R. This work is supported by the Spanish State Research Agency through the Mara de Maeztu Seal of Excellence to IRI (MDM-2016-0656). This work is partially funded by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482)

Trabajo presentado en la IEEE Conference on Decision and Control (CDC), celebrada en Miami Beach (FL, USA), del 17 al 19 de diciembre de 2018, This paper proposes a distributed set-membership approach based on zonotopes for interconnected systems with coupled states and unknown-but-bounded uncertainties (both state disturbances and measurement noise). The objective of the distributed set-membership approach is to find a sequence of distributed zonotopes to bound uncertain states of each subsystem (called agent) instead of making use of a single zonotope to bound all the uncertain states. In the proposed approach, these distributed state bounding zonotopes are only corrected by their own measurement outputs. To predict the state at the next sampling time, each agent sends its own state-bounding zonotope to its neighbors. For achieving robust state estimation, we propose a novel optimization problem based on the P-radius minimization criterion. Finally, the effectiveness of the proposed approach is provided with a numerical example., This work has been partially supported by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project DEOCS (ref. DPI2016-76493) and SCAV (ref. DPI2017-88403-R), the FPI grant (ref. BES-2014-068319) and by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (ref. 2017-SGR-482).

Trabajo presentado en la IEEE/CVF Conference on Computer Vision and Pattern Recognition, celebrada en Salt Lake City (UT, USA), del 18 al 23 de junio de 2018, This paper introduces an approach to simultaneously estimate 3D shape, camera pose, and object and type of deformation clustering, from partial 2D annotations in a multi-instance collection of images. Furthermore, we can indistinctly process rigid and non-rigid categories. This advances existing work, which only addresses the problem for one single object or, if multiple objects are considered, they are assumed to be clustered a priori. To handle this broader version of the problem, we model object deformation using a formulation based on multiple unions of subspaces, able to span from small rigid motion to complex deformations. The parameters of this model are learned via Augmented Lagrange Multipliers, in a completely unsupervised manner that does not require any training data at all. Extensive validation is provided in a wide variety of synthetic and real scenarios, including rigid and non-rigid categories with small and large deformations. In all cases our approach outperforms state-of-the-art in terms of 3D reconstruction accuracy, while also providing clustering results that allow segmenting the images into object instances and their associated type of deformation (or action the object is performing)., This work is supported in part by a Google Faculty Research Award, by the Spanish Ministry of Science and Innovation under projects HuMoUR TIN2017- 90086-R, and Mar´ıa de Maeztu Seal of Excellence MDM2016-0656.

Combined urban drainage system (CUDS) collect both wastewater and raining water through sewer networks to wastewater treatment plants (WWTP) before releasing to the environment. During storm weather, rain and wastewater can overload the capacity of the CUDS and/or the WWTPs, producing combined sewer overflows (CSO). In order to improve the management efficiency of CUDS, advanced real-time control (RTC) of detention and diversion infrastructures in the sewer systems has been proven to contribute to reducing the CSO volumes. This work considers the integrated RTC of sewer network and WWTPs based on model predictive control (MPC) and taking into account the water quality as well as quantity, with the objective of minimizing the environmental impact of CSO on receiving waters. The control approach is validated using a real pilot Badalona sewer network in Spain. The first results, discussion and conclusions are also provided.

Trabajo presentado en la IEEE Conference on Decision and Control (CDC), celebrada en Miami Beach (FL, USA), del 17 al 19 de diciembre de 2018, A model predictive control (MPC) framework is developed in the present study, with the final objective to improve the energy flexibility of building thermal loads through demand-side management. Three different configurations are tested and tuned, with the following objective functions: minimizing the delivered energy to the building, the electrical energy used by the HVAC system (heat pump) or the cost of this electricity use. To validate these MPC configurations, a Matlab-Trnsys co-simulator is also created, in order to run the MPC on a virtual plant composed of a detailed building model. The MPC strategy manages to run effectively on the chosen study case (a residential building with heat pump in Spain), and the differences between configurations are discussed., This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 675318 (INCITE). Part of this work stems from the activities carried out in the framework of the IEA-EBC Annex 67 (International Energy Agency – Energy in Buildings and Communities program) about Energy-Flexible Buildings.

Trabajo presentado en la Annual American Control Conference (ACC), celebrada en Milwaukee (WI, USA), del 27 al 29 de junio de 2018, In this paper, a distributed control scheme based on population games is proposed. The controller is in charge of dealing with the energy consumption problem in a Heterogeneous Cellular Network (HetNet) powered by hybrid energy sources (grid and renewable energy) while guaranteeing appropriate quality of service (QoS) level at the same time. Unlike the conventional approach in population games, it considers both atomicity and non-anonymity. Simulation results show that the proposed population-games approach reduces grid consumption by up to about 12% compared to the traditional best-signal level association policy.

This paper proposes a set‐based approach for robust fault estimation of discrete‐time descriptor systems. The considered descriptor systems are subject to unknown‐but‐bounded uncertainties (state disturbances and measurement noise) in predefined zonotopes and additive actuator faults. The zonotopic fault estimation filter for descriptor systems is built based on fault detectability indices and matrix to estimate fault magnitude in a deterministic set. The zonotopic fault estimation filter gain is designed in a parameterized form. Within a set‐based framework, following the zonotopic Kalman filter, the optimal filter gain is computed by minimizing the size of the corresponding zonotopes to achieve robustness against uncertainties and the identification of occurred actuator faults. Besides, boundedness of the proposed zonotopic fault estimation is analyzed, which proves that the size of obtained fault estimation bounds is not growing in time. Finally, the simulation results with two application examples are provided to show the effectiveness of the proposed approach., This research was supported by the Spanish State Research Agency (AEI)and the European Regional Development Fund (ERFD) through projects DEOCS (DPI2016-76493-C3-3-R) and SCAV(DPI2017-88403-R), through the Formación de Personal Investigador (FPI) grant (BES-2014-068319), and by AGAUR ofGeneralitat de Catalunya through the Advanced Control Systems (ACS) group grant (2017-SGR-482).

A data-driven methodology that includes the unfalsified control concept in the framework of fault diagnosis and isolation (FDI) and fault-tolerant control (FTC) is presented. The selection of the appropriate controller from a bank of controllers in a switching supervisory control setting is performed by using an adequate FDI outcome. By combining simultaneous online performance assessment of multiple controllers with the fault diagnosis decision from structured hypothesis tests, a diagnosis statement regarding what controller is most suitable to deal with the current (nominal or faulty) mode of the plant is obtained. Switching strategies that use the diagnosis statement are also proposed. This approach is applied to a nonlinear experimentally validated model of the breathing system of a polymer electrolyte membrane fuel cell. The results show the effectiveness of this FDI–fault-tolerant control data-driven methodology

The use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Thus, to determine the optimal operation of these systems in dynamic energy-market scenarios, operational constraints and the time-varying price profiles for both electricity and the required resources should be taken into account. In order to maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller designed according to the Economic Model Predictive Control (EMPC) approach, which uses a non-constant time step along the prediction horizon to get a shorter step size at the beginning of that horizon while a lower resolution for the far instants. Besides, a softening of related constraints to meet the market requirements related to the sale of electric power to the grid point is proposed. Simulation results show that the computational burden to solve optimization problems in real time is reduced while minimizing operational costs and satisfying the market constraints. The proposed controller is developed based on a real CHP plant installed at the ETA research factory in Darmstadt, Germany., Authors would like to thank Ikergune (Exte-Tar Group), the project IKERCON (Ref. c-10683) and the FI-AGAUR scholarship of the Catalan government for their scientific support in this work. Besides, parts of this work are funded by the German Federal Ministry of Education and Research (BMBF) in the project SynErgie and the German Federal Ministry of Economic Affairs and Energy (BMWi) in the project PHI-Factory.

This paper presents a nonlinear observer design for Proton Exchange Membrane Fuel-Cell (PEMFC) systems. The aim of the proposed observer is to reconstruct the oxygen excess ratio through the estimation of their relevant states in real time from the measurement of the supply manifold air pressure. A First-Order Sliding Mode (FOSM) differentiation method is adopted to estimate, in finite time, the time derivative of the supply manifold air pressure. By means of the output-state inversion model, the relevant states are reconstructed. The objective of the proposed appproach is to minimize the use of additional sensors in order to reduce the costs and enhance the system accuracy. The performance of the proposed observer is analyzed through simulations considering measurement noise and different stack-current variations. The results show that the nonlinear observer properly estimates in finite time and robustly the oxygen excess ratio.

With the aim of distributing the total power required at the wind farm control level for each wind turbine, we proposed a multi-objective model predictive control scheme able to (i) ensure the tracking of the power demanded by the electrical grid; (ii) provide primary frequency control by de-loading the wind turbines; and (iii) maximize the total power reserve when the available power is higher than the power demand.

Trabajo presentado a la Conference on Control Technology and Applications, celebrada en Hawai (US) del 27 al 30 de agosto de 2017., The presented study deals with hierarchical and cooperative model predictive control (MPC) of electrical grids. The aim of this study is minimizing electrical frequency deviation while ensuring power levels do not rise too much. The original system is a simply interconnected one divided in several areas and, in order to control eventually disconnected areas due to communication blackouts, an expansion of the original system to a hierarchical version of itself by overlapping original system¿s areas., This work was supported by the partnership between Keio University and UPC-ETSEIB. This work has been supported by the project DPI2015-69286-C3-2-R of the Spanish Ministerio de Educacion de España and 2014 SGR 267 of the AGAUR agency of the Generalitat de Catalunya.

Trabajo presentado a la IEEE 3rd Colombian Conference on Automatic Control (CCAC), celebrada en Cartagena (Colombia) del 18 al 20 de octubre de 2017., In this paper, the performance of a middle voltage (MV) distributed power generation system under a Model Predictive Control (MPC) strategy is tested. The designed controller receives process information from an state estimation unit, composed of a Weighted Least-Squares strategy (WLS). The contribution is focused on performing the frequency and voltage regulation of the system, considering moreover the dynamic behavior of storage units and capacitors, and feeding back information to the control system considering the transmission delay. The results show that the closed loop based on the proposed strategy achieves the required performance and can be useful to be applied to more complex systems such as low-voltage (LV) generation systems.

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this paper, the computation of robust invariant sets for discrete-time uncertain descriptor systems is investigated. The studied descriptor systems are assumed to be regular and stable subject to unknown but bounded disturbances. The robust invariant sets of both causal and non-causal descriptor systems are studied. Transformations for causal and non-causal descriptor systems are used in the characterization of the effect of the disturbances. For causal descriptor systems, two robust positively invariant (RPI) sets are computed separately. For non-causal descriptor systems, an RPI set and a robust negatively invariant (RNI) set can be found. As a result, the general RPI set of a descriptor system can be obtained from a linear projection image of these two sets. Besides, active mode detection method is proposed based on set invariance theory.