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Fuel cells are electrochemical devices with some internal variables that cannot be measured, but have to be monitored in real-time. That is the case for the liquid water inside the fuel cell catalyst layer. This motivates the development of online algorithms, i.e., observers, able to estimate such variables. Nonetheless, fuel cell dynamics are strongly nonlinear, with significant parametric uncertainty and significant sensor noise. Therefore, typical observers, the extended Kalman filter (EKF), usually underperform or are unstable in such systems. To overcome such limitations, this work proposes a novel nonlinear observer to estimate the liquid water saturation in fuel cells based on an inherent differential detectability of the liquid water dynamics. The stability of the proposal is formally analyzed and it is validated through numerical simulations and in an experimental prototype, where noise and uncertainty are considered., This work has been partially funded by the Spanish Ministry of Universities funded by the European Union-Next Generation EU (2022UPC-MSC-93823). This work is part of the Project MAFALDA (PID2021-126001OB-C31) funded by MCIN/AEI /10.13039/501100011033 and by "ERDFA way of making Europe".This work is part of the project MASHED (TED2021-129927B-I00), funded by MCIN/AEI/10.13039/501100011033 and by the European Union Next Generation EU/PRTR., Peer Reviewed, Postprint (author's final draft)

A novel fault diagnosis procedure is proposed in this paper to estimate faults using a linear parameter varying (LPV) model whose scheduling parameters depend on the fault. A wrong determination of the operating conditions could lead the system to an undesired performance or even to an unstable situation, when classical fault diagnosis approaches are applied. This paper addresses this issue by formulating fault diagnosis as a dynamic optimization problem, solved by using a novel hybrid technique that combines a Luenberger-based observer with artificial intelligent (AI) optimization-based algorithms. The observer supervises the health of the system, while AI-based algorithms are able to reconstruct the faulty signal in real-time when the observer determines that the system is under a fault. The efficiency of the proposed fault diagnosis scheme, the three AI-based algorithms based on artificial bee colony and particle swarm optimization, and the gradient-based algorithm developed in this paper, are assessed using a numerical example., This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020-114244RB-100), by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014-2020 (ref. 001-P-001643 Looming Factory) and by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482)., Peer Reviewed, Postprint (author's final draft)

Parameter estimation is crucial for polymer electrolyte membrane fuel cell monitoring and control. Nonetheless, most parameter estimation algorithms rely on a persistence of excitation condition, which is rarely satisfied and not convenient in fuel cell systems. For this reason, this work presents and compares three algorithms to estimate in real-time some critical PEMFC parameters in the voltage equation: the ohmic resistance, the charge transfer coefficient and the oxygen activity of a proton exchange fuel cell. The first algorithm is a standard gradient descent, while the other two are based on a set of pre-preprocessing dynamics. It is shown that, while the gradient descent requires the persistence of excitation condition, the addition of the pre-processing dynamics ensures reliable estimation under significantly weaker excitation assumptions. Moreover, it is shown that the pre-processing dynamics improves the transient behaviour and noise performance of the estimators. The results are validated through a set of numerical simulations and in an experimental prototype, where sensor noise and unmodelled disturbances are considered., This work has been supported by the Spanish Ministry of Universities funded by the European Union - NextGenerationEU (2022UPC-MSC-93823). This work is part of the Project MAFALDA (PID2021- 126001OB-C31) funded by MCIN/ AEI /10.13039/501100011033 and by "ERDF A way of making Europe" This work is part of the project MASHED (TED2021-129927BI00), funded by MCIN/ AEI/10.13039/501100011033 and by the European Union Next GenerationEU/PRTR. This research has been developed within the CSIC Interdisciplinary Thematic Platform (PTI+) Transición Energética Sostenible+ (PTI-TRANSENER+) as part of the CSIC program for the Spanish Recovery, Transformation and Resilience Plan funded by the Recovery and Resilience Facility of the European Union, established by the Regulation (EU) 2020/2094., Peer Reviewed, Preprint

This paper presents the design of a model predictive control (MPC) for the Calais canal, located in the north of France for satisfactory management of the system. To estimate the unknown inputs/outputs arising from the uncontrolled pumps, a digital twin (DT) in the framework of a Matlab- is used to reproduce the dynamics of the canal, and the real database corresponding to a period of three days is employed to evaluate the control strategy. The canal is characterized by two operating modes due to high and low tides. As a consequence of this, time-varying constraints on the use of gates must be considered, which leads to the design of two multiobjective control problems, one for the high tide and another for the low tide. Furthermore, a moving horizon estimation (MHE) strategy is used to provide the MPC with unmeasured states. The simulation results show that the different objectives are met satisfactorily., Peer Reviewed, Postprint (author's final draft)

This paper proposes a learning-based control approach for autonomous vehicles. An explicit Takagi–Sugeno (TS) controller is learned using input and output data from a preexisting controller, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. At the same time, the vehicle model is identified in the TS model form for closed-loop stability assessment using Lyapunov theory and LMIs. The proposed approach is applied to learn the control law from an MPC controller, thus avoiding the use of online optimization. This reduces the computational burden of the control loop and facilitates real-time implementation. Finally, the proposed approach is assessed through simulation using a small-scale autonomous racing car., Peer Reviewed, Postprint (published version)

This paper proposes a set-membership state estimation method for Switched Linear Parameter Varying (SLPV) systems subject to unknown but bounded parametric uncertainties, disturbances and noises. A zonotopic outer approximation of the state estimation domain is computed at every time iteration. The size of this zonotope is designed to be convergent and bounded by satisfying P-radius-based and Average Dwell Time (ADT) conditions that are formulated in the Linear Matrix Inequality (LMI) framework. An extension of the state estimation method is presented to address the fault/disturbance estimation problem for SLPV systems. By using the state augmentation technique, the fault/disturbance estimation problem is transformed into a state estimation problem of the generated augmented descriptor switched LPV system. An application to vehicle lateral dynamics fault estimation is used for assessment purposes. Simulation results demonstrate the effectiveness of the proposed algorithm and highlight its advantages over the existing methods., Peer Reviewed, Postprint (published version)

Data-driven prognostics and health management is an important part of the future industry. It allows the detection of system faults and estimation of its remaining useful life (RUL) to anticipate failures and schedule appropriate prescriptive maintenance actions. Moreover, with the development of data acquisition tools in such industries, it is possible to collect large amount of data from similar systems, which prompts the use of machine learning algorithms for efficient estimation of the system RUL. However, due to different type of faults and degradation rates that can occur in real processes, there exists high variability of the end-of-life (¿¿¿) time of each system degradation trajectory, making more difficult to fix a failure threshold and consequently generate several uncertainties in the estimation of RUL. To address this situation, this paper proposes a new data-driven approach for estimating the system RUL when dealing with the variability of degradation trends and unknown failure thresholds. Particularly, the proposed approach combines two RUL techniques, recursive and direct RUL estimation. First, the historical collected raw data are fed into a processing algorithm to construct prognostic health indicators (HIs) and choose the one that characterizes well the system’s degradation trajectory. This latter indicator allows identifying the high and low ¿¿¿ amplitude values from the historical data and is used to build a recursive prediction model that estimates in long-term the degradation trend evolution. After that, the trained model forecasts every degradation trend which ¿¿¿ amplitude is less than the predefined high value. Thus, a set of possible RULs of each trajectory can be predicted. Finally, the ensemble of the derived RULs and their HI trajectories are fused to directly estimate the final RUL. The proposed approach is applied to a subway door system with multiple degradation scenarios while taking into account different operating conditions., The project has been 65% cofinanced by the European Regional Development Fund (ERDF) through the Interreg V-A Spain France Andorra programme (POCTEFA 2014–2020). POCTEFA aims to reinforce the economic and social integration of the French-Spanish-Andorran border. Its support is focused on developing economic, social and environmental cross-border activities through joint strategies favoring sustainable territorial development. Also, this work was supported by the ANR DATAZERO (contract ‘‘ANR-15-CE25-0012’’) project and by the EIPHI Graduate school (contract "ANR-19-CE25-0016")., Postprint (published version)

This paper addresses the post-fault selection of an actuators configuration for flow-based networks with back-up components. The proposed reconfiguration methodology consists of an offline and an online phase. On the one hand, an offline analysis looks for the minimal configurations for which the economic cost of the (best) steady-state trajectory that can be achieved using a robust model predictive control (MPC) policy is admissible. On the other hand, at fault detection time, an online search for the best actuators configuration to cope with the transient induced by the fault is conducted in the superset of each minimal configuration calculated offline. With this strategy, the final new configuration is computed by sequentially solving a set of mixed-integer programs whose constraints are derived from single-layer robust MPC schemes coupled with local controllers designed for the a priori minimal configurations identified offline. A portion of a water transport network is used to show the performance the proposed solution., Peer Reviewed, Postprint (author's final draft)

Parabolic-trough solar collector fields are large-scale systems, so the application of centralized optimizationbased control methods to these systems is often not suitable for real-time control. As such, this paper formulates a novel coalitional control approach as an appropriate alternative to the centralized scheme. The key idea is to split the overall solar collector field into smaller subsystems, each of them governed by a local controller. Then, controllers are clustered into coalitions to solve a local optimization-based problem related to the corresponding subset of subsystems, so that an approximate solution of the original centralized problem can be obtained in a decentralized fashion. However, the operational constraints of the solar collector field couple the optimization problems of the multiple coalitions, thus limiting the ability to solve them in a fully decentralized manner. To overcome this issue, a novel population-dynamics-assisted resource allocation strategy is proposed as a mechanism to decouple the local optimization problems of the multiple coalitions. The proposed coalitional methodology allows to solve the multiple local subproblems in parallel, hence reducing the overall computational burden, while guaranteeing the satisfaction of the operational constraints and without significantly compromising the overall performance. The effectiveness of proposed approach is shown through numerical simulations of a 10- and 100-loop version of the ACUREX solar collector field of Plataforma Solar de Almería, Spain., Peer Reviewed, Postprint (published version)

© 2023 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 work, the trajectory tracking problem of an autonomous racing electrical vehicle is addressed. Accordingly, a two-layer control scheme is developed, such that stability, recursive feasibility, and constraint satisfaction are guaranteed. The outer layer includes a zonotopic tube-based predictive control to ensure trajectory tracking while minimizing energy consumption considering the state of charge of the vehicle’s battery. Meanwhile, the inner layer combines a linear quadratic zonotopic controller with a zonotopic Kalman filter to reduce the effect of exogenous disturbances and modeling errors. Moreover, for control and estimation purposes, a data-driven Takagi–Sugeno (TS) model trained by an adaptive neuro-fuzzy inference system (ANFIS) is employed. To illustrate the performance of the proposed control scheme, a simulated 1/10 Scale RC car is used., This work was in part by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020- 114244RB-I00), in part by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014-2020 (ref. 001-P-001643 Looming Factory), and in part by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482), and in part by the FI AGAUR under Grant 2021FI-B1 00097., Peer Reviewed, Postprint (author's final draft)

Remaining useful life (RUL) is the crucial element in predictive maintenance, helping to reduce significant costs in factories and avoiding production downtime. This work contributes to a non-intrusive condition monitoring to estimate the RUL of the most critical component in an electromechanical system, which does not depend on previous historical run-to-failure data. Although most of the approaches characterize the behavior of the mechanical components from a vibration analysis, this work is focused on monitoring the characteristic frequencies from the torque oscillations that are transmitted via the three-phase stator currents. In this way, several features can be extracted by processing the current signals. Modeling the behavior of the features in a healthy stage, a health indicator is proposed that measures how well a new sample fits the healthy model. This indicator is processed to ensure an indicator with a monotonically increasing trend. Therefore, a procedure is proposed to estimate the RUL by calculating multiple exponential regressions at each sampling time, considering only incremental samples. Based on a defined failure threshold and exponential regressions, a time-to-failure (TTF) non-parametric distribution is updated online, as more samples are processed, the most likely TTF is revealed over time and used to estimate RUL along with its confidence bounds. The proposed approach has been validated with three experiments performed on a run-to-failure ball-bearing testbed, lasting 65 hours, 30 hours and 180 hours. As a result, the methodology achieved high accuracy in anticipating bearing failures 50 hours, 26 hours, and 100 hours before failure; with an accuracy of 93.78%, 89.49% and 64.31%, respectively. A comparative assessment with reported approaches was carried out using the PRONOSTIA-FEMTO datasets, demonstrating the suitable performance of the proposed approach to converge faster to the real RUL with high accuracy., Peer Reviewed, Postprint (author's final draft)

This article presents a novel response methodology for the operational recovery of a drinking water network after an incident causes an interruption of service. The proposed optimization-based methodology allows computing the optimal set of interventions to be performed in order to mitigate, or even prevent, the impact of the incident on the network operation. Besides, a proof-of-concept scheme has been designed for the automatic generation of failure scenarios and the systematic implementation and validation of the proposed response methodology. Several results are presented to demonstrate the capability of the methodology to mitigate harmful incidents, as well as the performance improvements derived from the application of the obtained interventions., Peer Reviewed, Postprint (author's final draft)

© 2023 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., We consider a large population of decision makers that choose their evolutionary strategies based on simple pairwise imitation rules. We describe such a dynamic process by the replicator dynamics. Differently from the available literature, where the payoffs signals are assumed to be updated continuously, we consider a more realistic scenario where they are updated occasionally. Our main technical contribution is to devise two event-triggered communication schemes with asymptotic convergence guarantees to a Nash equilibrium. Finally, we show how our proposed approach is applicable as an efficient distributed demand response mechanism., This research was supported by the project PID2020-115905RB-C21 (L-BEST) funded by MCIN/ AEI /10.13039/501100011033, the ERC under research project COSMOS (802348), and the project BPIN 2021000100499 funded by CTeI - SGR and MinCiencias, Colombia., Peer Reviewed, Postprint (author's final draft)

This article develops a hybrid approach to fault detection and isolation (FDI) based on a machine learning technique and quasi-Linear Parameter Varying (qLPV) zonotopic observers. First, the dynamical model of a wind turbine is identified using an adaptive network-based fuzzy inference system (ANFIS), which results in a set of qLPV polytopic models whose form is derived using structural analysis. Second, a bank of qLPV zonotopic observers is implemented to detect sensor and actuator faults. Unlike other works that consider different fault scenarios to train a neuronal network, in this work, only fault-free data is considered for the ANFIS. The FDI is based on the residual generation obtained by a bank of qLPV zonotopic observers of the identified models. Disturbances related to aerodynamic loads and measurement noise are considered to guarantee the robustness of the proposed method. The effectiveness of the proposed method is tested in a 5 MW WT well-known benchmark simulator based on fatigue, aerodynamics, structures, and turbulence under different fault scenarios., Peer Reviewed, Postprint (author's final draft)

This study presents an observer-based methodology to estimate, in real-time, the state of charge of a Vanadium redox flow battery. Different from the available results in the literature, this work presents a new estimator that distinguishes between the concentration in the tank and cell parts of the system. Moreover, it presents an estimation of the state of charge that can deal with both balanced and unbalanced situations. The model used for the observer design is a nonlinear electrochemical model. A nonlinear observer is proposed, designed and validated through simulation and in an experimental prototype., Peer Reviewed, Postprint (published version)

This study presents a vanadium redox flow battery model that considers the most important variables that have a crucial role in the performance of the system. A complete model divided in an electrochemical, thermal, hydraulic and voltage submodels is presented. The analytic analysis of the model is carried out to reduce the system order according to some conservation laws. Based on this analysis, a subsequent calibration of the model parameters is developed using real experimental data. The validation is performed comparing the real measured voltage and the one estimated with the model. To calibrate the model an algorithm based on the implementation of a particle swarm optimizer is used. Results obtained in both short and long-term operation are presented, in order to compare and validate if the model can be used for both state of charge and state of health estimation., Peer Reviewed, Postprint (author's final draft)

This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller connected in cascade with a P-PI attitude controller. Three different control approaches (classical PID, Super Twisting, and Adaptive Sliding Mode) are compared for the translation control. The parameters of such controllers are hard to tune by using a trial-and-error procedure, so we use an automated tuning procedure based on the Particle Swarm Optimization (PSO) method. The controllers were simulated in scenarios with wind gust disturbances, and a performance comparison was made between the different controllers with and without optimized gains. The results show a significant improvement in the performance of the PSO-tuned controllers., Peer Reviewed, Postprint (published version)

The aim of this research is to manage the voltage of an active distribution grid with a low X/R ratio and multiple Photovoltaic Distributed Generators (PVDGs) operating under varying conditions. This is achieved by providing a methodology for coordinating three voltage-based controllers implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The first controller is for the On-Line Tap Changer (OLTC), which computes its adequate voltage reference. Whereas the second determines the required Active Power Curtailment (APC) setpoint for PVDG units with the aim of regulating the voltage magnitude and preventing continuous tap operation (the hunting problem) of OLTC. Finally, the last component is an auxiliary controller designed for reactive power adjustment. Its function is to manage voltage at the Common Coupling Point (CCP) within the network. This regulation not only aids in preventing undue stress on the OLTC but also contributes to a modest reduction in active power generated by PVDGs. The algorithm coordinating between these three controllers is simulated in MATLAB/SIMULINK and tested on a modified IEEE 33-bus power distribution grid (PDG). The results revealed the efficacy of the adopted algorithm in regulating voltage magnitudes in all buses compared to the traditional control method., Peer Reviewed, Postprint (published version)

In this paper, the two-simultaneous-leak isolation problem in water distribution networks is addressed. This methodology relies on optimal sensor placement together with a leak location strategy using two well-known classifiers: k-NN and discriminant analysis. First, zone segmentation of the water distribution network is proposed, aiming to reduce the computational cost that involves all possible combinations of two-leak scenarios. Each zone is composed of at least two consecutive nodes, which means that the number of zones is at most half the number of nodes. With this segmentation, the leak identification task is to locate the zones where the pair of leaks are occurring. To quantify the uncertainty degree, a relaxation node criterion is used. The simulation results evidenced that the outcomes are accurate in most cases by using one-relaxation-node and two-relaxation-node criteria., The APC was funded by Tecnológico de Monterrey, Peer Reviewed, Postprint (published version)

© 2023 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, an assessment of two methods of uncertainty quantification in prognostics is undertaken. The two methods, the Inverse First Order Reliability Method (IFORM) and set-based reachability analysis for prognostics are considered. The IFORM approach permits the generation of confidence bounds that allows for the calculation of RUL values corresponding to the specified user-defined probability levels. On the other hand, uncertainty quantification can be achieved by means of set-based reachability analysis. A Zono-topic Kalman filter (ZKF) is proposed to take into account a damage-model such that at each propagation time, with the estimated state (degradation) and its uncertainty, a propagation of zonotopic sets can be produced. Coming from two different schools of thought, the statistical and set-based theory, both schemes are explored and tested on a case study in simulation., This work has been co-financed by the Spanish Research Agency (AEI) through the project SaCoAV (ref. MINECO PID2020-114244RB-I00) and MASHED (TED2021-129927B-I00)., Peer Reviewed, Postprint (author's final draft)

En este artículo, consideramos el marco de dinámicas evolutivas y juegos poblacionales. Para una clase de dinámicas evolutivas y una familia de juegos poblacionales contractivos, desarrollamos un método novedoso para la búsqueda distribuida de equilibrios de Nash bajo información parcial respecto a las decisiones de los demás agentes. La principal contribución es que establecemos condiciones suficientes para garantizar la estabilidad asintótica del conjunto de equilibrios de Nash del juego poblacional subyacente. Además, ilustramos nuestros resultados teóricos en una aplicación numérica de un juego de congestión de gran escala., Juan Martinez-Piazuelo agradece a la Universitat Politécnica de Catalunya y al Banco Santander, España, por la financiación de su beca FPI-UPC. Adicionalmente, los autores agradecen a los proyectos PID2020-115905RB-C21 (L-BEST) y TED2021-129927B-I00 (MASHED), financiados por MCIN/ AEI/10.13039/501100011033 y por la European Union Next GenerationEU/PRTR, por financiar esta investigación. Finalmente, Nicanor Quijano agradece el proyecto BPIN2021000100499, de la asignación para CTeI-SGR y Minciencias., Peer Reviewed, Postprint (published version)

© 2023 The Authors. This is an open access article under the CC BY-NC-ND license. Peer review under responsibility of International Federation of Automatic Control., In this paper an optimization-based solution to the collision avoidance challenge for autonomous vehicles is proposed. The presented approach consists in an online motion planner designed to define a feasible and efficient path which implicitly guarantees safety manoeuvres in dynamic surroundings. The fact of considering moving obstacles inside the motion planner increases the complexity of the problem while forces it to be executed more frequently as others. To reduce its computational complexity, this approach proposes a two stages translation of the commonly used non-linear optimization-based structure into a QP formulation which can be easily solved. The first stage is based on the use of LPV matrices in the dynamic constraints of the vehicle. The second stage consists in computing linear expressions by set propagation to obtain the set of permitted inputs and reachable states which guarantee safety conditions., This work has been co-financed by the Spanish State Research Agency (AEI) through the projects SaCoAV (ref. MINECO PID2020-114244RB-I00) and MASHED (TED2021-129927B-I00)., Peer Reviewed, Postprint (published version)

During photovoltaic (PV) conversion in solar panels, a part of the solar radiation is not converted to electricity by the cells, producing heat that could increase their temperature. This increase in temperature deteriorates the performance of the PV panel. In this paper, a hybrid PV/thermal (PV/T) water system is proposed to mitigate this problem. This system combines a PV panel and a thermal collector. In this paper, we focused on the modeling and control of this hybrid system in the linear parameter varying (LPV) framework. An optimal linear quadratic regulator (LQR) is proposed to control the PV cell temperature around an optimal value that maximises electricity generation. Since the system model is nonlinear, an optimal LQR gain-scheduling state-feedback control approach based on an LPV representation of the nonlinear model is designed using the Linear Matrix Inequality (LMI) method. The goal is to obtain the maximum electrical power for each solar panel. Since a reduced number of sensors is available, an LPV Kalman filter is also proposed to estimate the system states required by the state-feedback controller. The obtained results in a laboratory setup in simulation are used to assess the proposed approach, showing promise in terms of control performance of the PV/T system., This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020-114244RB-I00), by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014–2020 (ref. 001-P-001643 Looming Factory), and by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482). The author is also supported by an Industrial PhD AGAUR grant (Ref. 2019 DI 040)., Peer Reviewed, Postprint (published version)

This paper proposes a coalitional model predictive control method for temperature regulation in parabolic-trough solar fields. The global optimization problem is divided into a set of local subproblems that will be solved in parallel by a set of coalitions. However, these local (smaller) problems remain coupled by a common global resource constraint. In this regard, we present a population-dynamics-assisted resource allocation approach to fully decouple the local optimization problems. By doing this, each coalition can address its corresponding optimization problem without relying on the solutions of the other coalitions. To illustrate the proposed methodology, we provide simulation results for a 100-loop parabolic-trough solar collector field., The authors gratefully acknowledge the financial support by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (OCONTSOLAR, grant agreement No 789051). In addition, the authors would like to thank the projects PID2020-119476RB-I00 (C3PO-R2D2) and TED2021-129927B-I00 (MASHED) funded by MCIN/ AEI /10.13039/501100011033 and by the “European Union NextGenerationEU/ PRTR” for supporting this research. Finally, Juan Martinez- Piazuelo gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the financial support of his predoctoral grant FPI-UPC., Peer Reviewed, Postprint (published version)

In this paper, a two-layer supervisory control scheme is proposed for discrete-time linear systems with state/input constraints, including multiple state time-delays, parametric uncertainties, and exogenous disturbance. The inner control layer is designed to achieve robust H8 tracking performance considering the delays as extra states but neglecting the input/state constraints. On the other hand, in the outer control layer, a command governor (CG) is designed to robustly guarantee the satisfaction of state and input constraints by computing a minimal Robust Positively Invariant (mRPI). To this end, the CG manipulates reference inputs by generating the nearest admissible value to be applied to the closed-loop system in both transient and steady-state response through the computation of the Maximal Output Admissible Set (MOAS). Finally, the validity of the proposed scheme is assessed in simulation using a numerical example and a continuous stirred tank reactor (CSTR) system., Peer Reviewed, Postprint (author's final draft)

© 2023 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., This paper presents the design of a model predictive scheduling strategy to address the inland waterborne transport (IWT) problem considering bridges that must open to enable vessel passage. The main contribution is the formulation of a control-oriented model of the problem, including propositional logic expressions that characterize system behavior and their conversion into (in)equality constraints. The resulting model is embedded into a predictive scheduling approach to determine bridge opening timetables and vessel passage times in a coordinated manner. The effectiveness of the strategy is demonstrated on a realistic case study based on the Rhine-Alpine corridor., This work was supported in part by the Researchlab Autonomous Shipping (RAS) of Delft University of Technology, in part by the project ”Novel inland waterway transport concepts for moving freight effectively (NOVIMOVE)” (this project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 858508), and in part by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020-114244RB-I00)., Peer Reviewed, Postprint (author's final draft)

In open-channel irrigation systems (OCIS), most of the water is lost by seepage and leaks. However, the reported control strategies for OCIS are not focused on leaks and seepage minimization. Therefore, aiming to contribute to the reduction of waste of water, in this paper, an efficient control approach for OCIS, capable to reduce the waste of water by up to 50%, is presented. The proposed strategy is a nonlinear model predictive control that operates under request mode, which has been designed using a simplified modeling approach capable to describe the behavior of the OCIS along a broad operation region. The proposed strategy has been tested using a well-known test bed of the literature, and the results show that, by using this strategy, an appropriate amount of water can be supplied to the users, and the waste of water can be significantly reduced., This research has been supported by Séptima Convocatoria Interna de Investigación de la Universidad Central, Convocatoria Proyectos de Investigación Conjunta Universidad de IbaguéUniversidad de los Andes, and project PID2020-115905RB-C21 (LBEST) funded by MCIN/ AEI /10.13039/501100011033., Peer Reviewed, Postprint (published version)

© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission., This brief contributes to the application of model predictive control (MPC) to address the combined sewer overflow (CSO) problem in urban drainage systems (UDSs) with uncertainty. In UDS, dealing with uncertainty in rain forecast and dynamic models is crucial due to the possible impact on the UDS control performance. Two different MPC approaches are considered: tube-based MPC (T-MPC) and chance-constrained MPC (CC-MPC), which represent uncertainty in deterministic and stochastic manners, respectively. This brief presents how to apply T-MPC to UDS, by establishing a mathematical relation with CC-MPC, and a rigorous mathematical comparison. Based on simulations using the Astlingen benchmark UDS, the strengths and weaknesses of the performance of T-MPC and CC-MPC in UDS were compared. Differences in the involved mathematical computations have also been analyzed. Moreover, the comparison in performance also indicates the applicability of each MPC approach in different uncertainty scenarios., This work was supported in part by the Supervision and fault-tolerant control of smart infrastructures based on advanced learning and optimization (L-BEST) Project of the Spanish Agency of Research under Grant PID2020-115905RB-C21 and in part by the Real-time pollution-based control of urban drainage and sanitation systems for protection of receiving waters (RUBIES) Project of the European Commission under Grant LIFE20 ENV/FR/000179, Peer Reviewed, Postprint (author's final draft)

© 2023 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, Practical implementation of data analytics in industrial environments has always been a problematic area because of data availability and quality. In this paper, a Gaussian sampling methodology is proposed to address the problem of imbalanced telemetry datasets that is one of the root causes that make modelling less reliable. By generating subsets that achieve homogeneous density distributions this problem is addressed. By comparing the impact of this method with the baseline case of random sampling, this paper aims to address this problem and propose a practical solution. A case study based on an industrial cooling device is used to assess and illustrate the proposed approach., This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020-114244RB-I00), Peer Reviewed, Postprint (author's final draft)

In recent years there has been a revolution in the development of interactive applications for teaching control theory. In parallel, much work has been done on the development of experimental and virtual control laboratories. In this work, different interactive tools are presented that aim to link both worlds with the aim of promoting the link between theory and practice, using the most common equipment that can be found in automatic control laboratories., This research has been partially funded through the project RECOVERY (PID2020-112658RB-I00). This work is part of the Project MAFALDA (PID2021-126001OB-C31) funded by MCIN/ AEI /10.13039/501100011033 and by ”ERDF A way of making Europe”. This work is part of the project MASHED (TED2021-129927B-I00), funded by MCIN/ AEI/10.13039/501100011033 and by the European Union Next GenerationEU/PRTR., Peer Reviewed, Postprint (published version)

This paper addresses the problem of distributed Nash equilibrium (NE) seeking in strongly contractive aggregative multi-population games subject to partialdecision information. In particular, we consider the scenario where the so-called payoff providers of the multiple populations communicate through a possibly noncomplete network, and we formulate some consensus-like dynamics for the payoff providers to distributedly compute their payoff signals using local information only. Moreover, by exploiting the notions of d-passivity and d-antipassivity, we provide a unified analysis for several classes of evolutionary game dynamics. As the main contributions, we provide sufficient conditions to guarantee the d-antipassivity of a class of continuous-time dynamical systems, and we exploit such results to design distributed NE seeking dynamics for strongly contractive aggregative population games, as well as for a class of merely contractive aggregative population games. To the best of our knowledge, this is the first paper to consider the problem of distributed NE seeking for such classes of population games and from a unifying passivity-based perspective., This research has been supported by the projects PID2020-115905RB-C21 (LBEST) and TED2021-129927B-I00 (MASHED) funded by MCIN/AEI /10.13039/501100011033, and by the project BPIN2021000100499 funded by CTeI-SGR and MinCiencias,Colombia., Peer Reviewed, Postprint (published version)

© 2023 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, Robotic cloth manipulation is an increasingly relevant area of research, challenging classic control algorithms due to the deformable nature of cloth. While it is possible to apply linear model predictive control to make the robot move the cloth according to a given reference, this approach suffers from a large dimensionality of the state-space representation of the cloth models. To address this issue, in this work we study the application of an input-output model predictive control strategy, based on quadratic dynamic matrix control, to robotic cloth manipulation. To account for uncertain disturbances on the cloth's motion, we further extend the algorithm with suitable chance constraints. In extensive simulated experiments, involving disturbances and obstacle avoidance, we show that quadratic dynamic matrix control can be successfully applied in different cloth manipulation scenarios, with significant gains in optimization speed compared to standard model predictive control strategies. The experiments further demonstrate that the closed-loop model used by quadratic dynamic matrix control can be beneficial to the tracking accuracy, leading to improvements over the standard predictive control strategy. Moreover, a preliminary experiment on a real robot shows that quadratic dynamic matrix control can indeed be employed in real settings., This work was supported by the project CLOTHILDE (“CLOTH manIpulation Learning from DEmonstrations”), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Advanced Grant agreement No 741930)., Peer Reviewed, Postprint (author's final draft)

This paper studies the joint production and maintenance scheduling in microalgae manufacturing systems comprised of multiple machines, which are subject to coupled production demand agreements and operational maintenance constraints. Namely, there are some microalgae production demands to be met over a given horizon, and the maintenance of each microalgae manufacturing unit must be done before a given deadline. Moreover, the number of units whose maintenance can be done simultaneously over the same day is limited, and the units that undergo maintenance cannot contribute to microalgae production during their maintenance day. To solve the considered problem, we design a mixed-integer nonlinear model predictive controller, which is implemented in two optimization stages. The former regards a mixed-integer model predictive control problem, while the latter considers a nonlinear model predictive control problem. The proposed approach allows us to decouple the mixed-integer and nonlinear parts of the whole problem, and thus provides more flexibility on the optimization solvers that can be employed. In addition, the first stage also evaluates the attainability of the demand agreements, and provides a mechanism to minimally adjust such constraints so that their satisfaction can be guaranteed at the second stage. The overall model predictive control approach is based on experimental data collected at VAXA Technologies Ltd., and the effectiveness of the proposed method is validated through numerical simulations including multiple manufacturing units and uncertainties., Juan Martinez-Piazuelo gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the financial support of his predoctoral grant FPI-UPC. In addition, the authors would like to thank VAXA Technologies Ltd. as well as the project PID2020-115905RB-C21 (L-BEST) funded by MCIN/ AEI /10.13039/501100011033 for supporting this research., Peer Reviewed, Postprint (published version)

Model Predictive Control (MPC) has recently gained increasing interest in the adaptive management of water resources systems due to its capability of incorporating disturbance forecasts into real-time optimal control problems. Yet, related literature is scattered with heterogeneous applications, case-specific problem settings, and results that are hardly generalized and transferable across systems. Here, we systematically review 149 peer-reviewed journal articles published over the last 25 years on MPC applied to water reservoirs, open channels, and urban water networks to identify common trends and open challenges in research and practice. The three water systems we consider are inter-connected, multi-purpose and multi-scale dynamical systems affected by multiple hydro-climatic uncertainties and evolving socioeconomic factors. Our review first identifies four main challenges currently limiting most MPC applications in the water domain: (i) lack of systematic benchmarking of MPC with respect to other control methods; (ii) lack of assessment of the impact of uncertainties on the model-based control; (iii) limited analysis of the impact of diverse forecast types, resolutions, and prediction horizons; (iv) under-consideration of the multi-objective nature of most water resources systems. We then argue that future MPC applications in water resources systems should focus on addressing these four challenges as key priorities for future developments., Andrea Castelletti, Andrea Ficchì, and Matteo Giuliani acknowledge support from the EU-funded Climate Intelligence (CLINT) project [grant number: 101003876; DOI: 10.3030/101003876]. Andrea Ficchì also acknowledges support from the AXA Research Fund Fellowship on Coastal Livelihoods. Wenyan Wu acknowledges support from the Australian Research Council via the Discovery Early Career Researcher Award [grant number: DE210100117]., Peer Reviewed, Postprint (published version)

This paper presents an approach to ensure conditions on Variable Impedance Controllers through the off-line tuning of the parameters involved in its description. In particular, we prove its application to term modulations defined by a Learning from Demonstration technique. This is performed through the assessment of conditions regarding safety and performance, which encompass heuristics and constraints in the form of Linear Matrix Inequalities. Latter ones allow to define a convex optimisation problem to analyse their fulfilment, and require a polytopic description of the VIC, in this case, obtained from its formulation as a discrete-time Linear Parameter Varying system. With respect to the current state-of-art, this approach only limits the term definition obtained by the Learning from Demonstration technique to be continuous and function of exogenous signals, i.e. external variables to the robot. Therefore, using a solution-search method, the most suitable set of parameters according to assessment criteria can be obtained. Using a 7-DoF KinovaGen3 manipulator, validation and comparison against solutions with relaxed conditions are performed. The method is applied to generate Variable Impedance Controllers for a pulley belt looping task, inspired by the Assembly Challenge for Industrial Robotics in World Robot Summit 2018, to reduce the exerted force with respect to a standard (constant) Impedance Controller. These controllers fulfil a set of safety constraints, namely stability, bounds on task variables and maximum response overshooting; and their performance is determined by the User Preference heuristic, which allows to intuitively define the desired compliant behaviour along the task. In the context of the task, this is used to generate new controllers for one-off modifications of the nominal belt looping task setup without new demonstrations., This work is partially supported by MCIN/ AEI /10.13039/501100011033 and by the ”European Union NextGenerationEU/PRTR” under the project ROB-IN (PLEC2021-007859); and by MCIN/ AEI /10.13039/ 501100011033, Spain, under the project CHLOE-GRAPH (PID2020- 119244GB-I00). Authors also want to thank Adriá Colomé and Edoardo Caldarelli for their comments and help throughout this work., Peer Reviewed, Postprint (published version)

Lithium-ion batteries are widely recognised as the leading technology for electrochemical energy storage. Their applications in the automotive industry and integration with renewable energy grids highlight their current significance and anticipate their substantial future impact. However, battery management systems, which are in charge of the monitoring and control of batteries, need to consider several states, like the state of charge and the state of health, which cannot be directly measured. To estimate these indicators, algorithms utilising mathematical models of the battery and basic measurements like voltage, current or temperature are employed. This review focuses on a comprehensive examination of various models, from complex but close to the physicochemical phenomena to computationally simpler but ignorant of the physics; the estimation problem and a formal basis for the development of algorithms; and algorithms used in Li-ion battery monitoring. The objective is to provide a practical guide that elucidates the different models and helps to navigate the different existing estimation techniques, simplifying the process for the development of new Li-ion battery applications., This research received support from the Spanish Ministry of Science and Innovation under projects MAFALDA (PID2021-126001OB-C31 funded by MCIN/AEI/10.13039/501100011033/ ERDF,EU) and MASHED (TED2021-129927B-I00), and by FI Joan Oró grant (code 2023 FI-1 00827), cofinanced by the European Union., Peer Reviewed, Postprint (published version)

© 2023 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., Toyota Mirai is a widely known fuel cell hybrid vehicle and its powertrain represents a benchmark for engineers and researchers. This work presents a lumped-parameter low-order model of Toyota Mirai powertrain and an assessment of the energy management focused on fuel economy and battery and fuel cell degradation. In addition, a rule-based strategy is proposed to reproduce the behavior of Toyota Mirai energy management strategy. The powertrain model and the proposed strategy are validated against experimental data. The assessment of the energy management is performed by comparing the Toyota Mirai strategy with optimal strategies obtained offline via dynamic programming. Different optimal strategies are computed by including the demand of the fuel cell and the battery in the cost function. Simulation results show a high correlation between the proposed model and strategy compared to the experimental data. Comparison of Toyota Mirai strategy with the optimal strategies reveals a small margin for improvement in fuel economy and higher margins in terms of demand of the fuel cell and battery., Peer Reviewed, Postprint (author's final draft)

A simple proportional integral (PI) controller with scheduled gain has been developed and implemented in a catalytic membrane reactor (CMR) to obtain pure hydrogen from a methanol steam reforming process. The controller is designed to track the setpoint of the pure hydrogen flow rate in the permeate side of the CMR via the manipulation of the fuel inlet flow rate. Therefore, the controller actuator is the liquid pump that provides the mixture of methanol and water to the reactor. Within the CMR, the catalytic pellets of PdZn/ZnAl2O4/Al2O3 have been used to facilitate the methanol steam-reforming reaction under stoichiometric conditions (S/C = 1), and Pd–Ag metallic membranes have been employed to simultaneously separate the generated hydrogen. The PI controller design is based on a mathematical model constructed using transfer functions acquired from dynamic experiments conducted with the CMR. The controller has been successfully implemented, and experimental validation tests have been carried out at 450 °C and relative pressures of 6, 8, 10, and 12 bar., Peer Reviewed, Postprint (published version)

This paper presents a novel observer architecture capable to estimate online the concentrations of the four vanadium species present in a vanadium redox flow battery (VRFB). The proposed architecture comprises three main stages: (1) a high-gain observer, to estimate the output voltage and its derivatives; (2) a dynamic inverter, to obtain a set of concentration candidate solutions; and (3) a static selector, to determine the actual concentrations. The methodology does not rely on the classic assumption of balanced electrolytes, thus significantly widening the application range in comparison with most of the literature previous studies. Furthermore, to perform the estimation, only a single voltage and current measurements are required, which eliminates the need of including complex and costly additional sensors. To validate the proposal, comprehensive simulation tests are conducted. These tests take into account typical side reactions that cause imbalance in VRFB systems, such as vanadium crossover and oxidation. The observer shows a remarkable performance when dealing with these realistic conditions, allowing to estimate with high accuracy and robustness the four vanadium concentrations, the State of Charge and the State of Health with a relative error below 2%., The project that gave rise to these results received the support of a fellowship from ”la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI21/11860023. This research was also supported by the Spanish Ministry of Science and Innovation, under the projects MAFALDA (PID2021-126001OB-C31) and MASHED (TED2021-129927B-I00). This work has been supported by the Spanish Ministry of Universities funded by the European Union - NextGenerationEU (2022UPC-MSC-93823)., Peer Reviewed, Postprint (published version)

The uncertain parameters estimation problem for linear discrete-time systems modeled in regression form and assuming an unknown but bounded description of the noise, is the main subject of this work. Particularly, zonotopic sets for bounding the parametric uncertainty are considered. Then, a zonotopic recursive least-squares (ZRLS) estimator is proposed and compared with its stochastic counterpart (RLS), as well as with the set-membership (SM) approach. Likewise, the parameter estimation problem is addressed from the Bayesian general framework to define the relationship between stochastic and deterministic approaches. Moreover, both ZRLS and SM applications applied to fault detection are assessed taking as a reference the minimum detectable fault in the worst case. Finally, a well-known quadruple-tank process is used to illustrate the estimation and fault detection results., This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020-114244RB-I00), by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014–2020 (ref. 001-P-001643 Looming Factory) and by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482). The author is also supported by a FI AGAUR grant (ref. 2021FI-B1-00097)., Peer Reviewed, Postprint (author's final draft)

This paper presents a switched linear system representation of water canal dynamics to incorporate different operating modes, which arise due to the occurrence of extreme weather phenomena such as flooding and drought episodes. To guarantee the stability during mode switching, a proper analysis on permanence regions–given by a collection of equilibrium states–for the switched linear system is presented. The permanence region is computed within a compact set, which depends on an adequate level region for the canals. A suitable algorithm is used to formulate an asymptotic stable Model Predictive Control (MPC) that steers and maintains the states of the system inside the target region indefinitely in a feasible manner. This strategy is successfully tested in a simulation using a realistic model of a canal., Peer Reviewed, Postprint (author's final draft)

Implementing monitoring electricity consumption strategies in industrial environments provides improvements in both the maintenance process and energy efficiency. The contribution of this work is an industry-oriented non-intrusive load monitoring approach based on an unsupervised algorithm, encompassing a method of event detection, functional data clustering, and condition monitoring. With this method, multiple devices can be monitored by only one electric meter in industrial environments, enhancing the early detection of anomalies and energy inefficiencies. The proposed approach presents a robust event detection to deal with different industrial contexts due to its intuitive parameters, which allows adapting the detection to be more sensitive or to filter out higher noise variations. Unlike other feature-based clusterings, the proposed functional data clustering enables high-precision identification of transient state patterns, characterizing specific shapes for each pattern to properly cluster them, and provides higher reliability during load detection. Thus, this load detection segments the power consumption to extract transient states and identify which load acts on each event based on functional data clusters. By detecting when loads start to consume, the proposed energy disaggregation extracts the frequency spectrum of each device from the aggregate current consumption, which is used in a condition monitoring strategy to track the spectrum behavior of each device. In this way, the condition of multiple loads can be monitored using a single electric meter, whose information can be relevant to accurately schedule maintenance interventions and detect anomalies or inefficiencies early. The proposed approach was validated in three industrial contexts: The load detection accuracy was verified in an industrial testbed, giving a precision higher than 99% for monitoring five devices. The second and third industrial scenarios validate the accuracy of the proposed condit, Peer Reviewed, Postprint (author's final draft)

Leaks represent one of the most relevant faults in water distribution networks (WDN), resulting in severe losses. Despite the growing research interest in critical infrastructure monitoring, most of the solutions present in the literature cannot completely address the specific challenges characterizing WDNs, such as the low spatial resolution of measurements (flow and/or pressure recordings) and the scarcity of annotated data. We present a novel integrated solution that addresses these challenges and successfully detects and localizes leaks in WDNs. In particular, we detect leaks by a sequential monitoring algorithm that analyzes the inlet flow, and then we validate each detection by an ad hoc statistical test. We address leak localization as a classification problem, which we can simplify by a customized clustering scheme that gathers locations of the WDN where, due to the low number of sensors, it is not possible to accurately locate leaks. A relevant advantage of the proposed solution is that it exposes interpretable tuning parameters and can integrate knowledge from domain experts to cope with scarcity of annotated data. Experiments, performed on a real dataset of the Barcelona WDN with both real and simulated leaks, show that the proposed solution can improve the leak detection and localization performance with respect to methods proposed in the literature. on classical benchmarks., Peer Reviewed, Postprint (author's final draft)

In this paper, we propose a model-based design approach for the effective control of a drinking water system to reduce the effect of hydraulic transients and meet pressure service requirements regardless of the demand pattern. We model pressure drops along a drinking water system according to a finite-difference approximation of the classical water hammer equations (WHE), and then apply the proposed control strategy to a section of the distribution network located in the metropolitan city of Barcelona. First, a model of the case study network is derived to reproduce the sustained pressure oscillations measured in the real system. Second, a decentralized control scheme composed of an optimal proportional–integrative (PI) controller with anti-windup and a series of model predictive controllers are designed. This control architecture allows to optimally regulate the service pressure while satisfying safety constraints, regardless of water demand fluctuations. Numerical simulations are used to assess the effectiveness of the proposed solution., Peer Reviewed, Postprint (author's final draft)

Mechanical unbalance is an important source of vibrations that can cause malfunctions in rotodynamic machinery. In industrial applications, unbalance is a critical issue for mass production machines. Previous studies for detecting and monitoring unbalance are based on balancing machines, trial weights, and intrusive actuators, while other studies rely on signal processing techniques, finite element analysis and physical modeling. These methodologies have some critical drawbacks, especially when non-intrusive monitoring is required, such as having to trial weights or determine constructive parameters such as mass and stiffness. The proposed approach is based on detecting and monitoring the unbalance condition in rotatory machines using data extracted from vibration sensors and a rotation sensor fitted to the system supports. The methodology comprises two main steps: identifying the appropriate speed range for unbalance monitoring and the modal parameters of the rotor, and determining and continuously monitoring the unbalance condition. Signal processing and system identification techniques are used to estimate unbalance in the rotatory machine. Experimental results for two rotodynamic systems demonstrate satisfactory performance in identifying and monitoring different unbalance conditions., Peer Reviewed, Postprint (author's final draft)

© 2022 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, This paper suggests the use of zonotopes for the design of watermark signals. The proposed approach exploits the recent analogy found between stochastic and zonotopic-based estimators to propose a deterministic counterpart to current approaches that study the replay attack in the context of stationary Gaussian processes. In this regard, the zonotopic analogous case where the control loop is closed based on the estimates of a zonotopic Kalman filter (ZKF) is analyzed. This formulation allows to propose a new performance metric that is related to the Frobenius norm of the prediction zonotope. Hence, the steady-state operation of the system can be related with the size of the minimal Robust Positive Invariant set of the estimation error. Furthermore, analogous expressions concerning the impact that a zonotopic/Gaussian watermark signal has on the system operation are derived. Finally, a novel zonotopically bounded water-mark signal that ensures the attack detection by causing the residual vector to exit the healthy residual set during the replay phase of the attack is introduced. The proposed approach is illustrated in simulation using a quadruple-tank process., Peer Reviewed, Postprint (author's final draft)

© 2022 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, The inverted pendulum is a mechanical system with a simple configuration that carries a non-linear, unstable nature widely used in control theory as a benchmark for research. The current research presents the modeling of a low-cost commercially available inverted pendulum and the design of a robust state-feedback controller and a robust observer, following the H 8 principle and using a low-cost commercially available inverted pendulum system as a reference. Simulated results compare the performance of the designed controller and observer with a conventional LQR controller and observer., Peer Reviewed, Postprint (author's final draft)

In this paper, a high-gain interval observer (HGIO) for a class of partially linear continuous-time systems with sampled measured outputs in the presence of bounded noise and additive disturbances is proposed. The design of the HGIO is formulated in the Linear Matrix Inequality (LMI) framework. The gain of the HGIO is designed to satisfy the cooperative property using a time-varying change of coordinates based on pole placement in separate LMI regions. Moreover, a procedure for designing the HGIO gain to minimise the effect of the noise and disturbance in the estimation is provided. The stability of the proposed HGIO is also guaranteed. The proposed approach is assessed in simulation using a numerical example., Peer Reviewed, Postprint (author's final draft)

© 2022 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, Many traditional control solutions in urban drainage networks suffer from unmodelled nonlinear effects such as rain and wastewater infiltrating the system. These effects are challenging and often too complex to capture through physical modelling without using a high number of flow sensors. In this article, we use level sensors and design a stochastic model predictive controller by combining nominal dynamics (hydraulics) with unknown nonlinearities (hydrology) modelled as Gaussian processes. The Gaussian process model provides residual uncertainties trained via the level measurements and captures the effect of the hydrologic load and the transport dynamics in the network. To show the practical effectiveness of the approach, we present the improvement of the closed-loop control performance on an experimental laboratory setup using real rain and wastewater flow data., Peer Reviewed, Postprint (author's final draft)

Recent years have seen a great deal of innovation in the field of systems prognostics and health management. However, even with these advancements, some pertinent issues related with uncertainty in remaining useful life predictions are still open for investigation. One such area of interest is on how to account for the distribution of these predictions such that all uncertainty sources are duly captured and represented. Practically, these uncertainty quantification procedures must be computationally feasible for real-life deployment and reflect real-life situations devoid of strong assumptions. This article thus, proposes a data-based prognostics technique that uses a set-based quantification of uncertainty based on the set-membership paradigm, the interval predictor approach. The methodology is applied in the framework of the Evolving Ellipsoidal Fuzzy Information Granule which has recently proven its potency in prognostics applications. As a case study, the method is tested on the prognostics of insulated bipolar transistors utilising an accelerated aging IGBT dataset from the NASA Ames Research Center., Peer Reviewed, Postprint (author's final draft)