“Como dar sentido al no sentido” en el proyecto europeo ISENSE y su aplicación al diagnóstico cognitivo de fallos de redes de agua

La misión del proyecto europeo “ISense: make sense to non sense” recientemente concluido ha sido el desarrollo de métodos inteligentes de procesamiento de datos para el análisis y la interpretación de los datos de tal manera que los fallos en complejos sistemas se detecten, se aíslen y se identifiquen tan pronto como sea posible, y permitan tomar futuras decisiones o acciones correctoras que aseguren la integridad, estabilidad y funcionamiento seguro de los sistemas en fallo.Peer ReviewedPostprint (published version

Advanced control systems research at UPC Terrassa Campus

Advanced Control Systems (SAC) is a multidiscip linary research group involving UPC professors and Spanish National Research Council (CSIC) researchers, focused on the wide subject of control and supervision of dynamic systems. The group uses theory of signal/systems tools, modelling, simulation and optimization in order to face real problems of systems and automated processes, specifically in the next subjects: Optimal/predictive control of large scale systems (mainly related with water cycle) ; Data validation ; Fault diagnosis ; Fault tolerant control system design ; Dynamic system monitoring and maintenance aiding ; Advanced control systems design, mainly focused on UAV control. The activities of research of the SAC group are framed in what today is known as TIC technologies, and their main objective is to develop tools that allow to improve the functioning of systems (aerogenerators, cars, airplanes, UAVs, etc.) and complex technological processes, (networks of water distribution, management of water quality, etc). It is understood as an improvement from the fact of achieving certain benefits of operation, until the planning of tasks in order to reduce costs or improving environmental aspectsPeer Reviewe

Data Validation and reconstruction for performance enhancement and maintenance of water networks

In a real water network, a telecontrol system must periodically acquire, store and validate data gathered by sensor measurements in order to achieve accurate monitoring of the whole network in real time. For each sensor measurement, data are usually represented by one-dimensional time series. These values, known as raw data, need to be validated before further use to assure the reliability of the results obtained when using them. In real operation, problems affecting the communication system, lack of reliability of sensors, or other inherent errors often arise, generating missing or false data during certain periods of time. These wrong data must be detected and replaced by estimated data. Thus, it is important to provide the data system with procedures that can detect such problems and assist the user in monitoring and processing the incoming data. Data validation is an essential step to improve data reliability. The validated data represent measurements of the variables in the required form where unnecessary information from raw data has been removed. In this paper, a methodology for data validation and reconstruction of sensor data in a water network is used to analyze the performance of the sectors of a water network. Finally, from this analysis several indicators of the components (sensors, actuators and pipes) and of the sectors themselves can be derived in order to organize useful plans for performance enhancement and maintenance. Nice practices have been developed during a large period in the water network of the company ATLL Concessionària de la Generalitat de Catalunya, S.A.Postprint (author's final draft

An improved tool of water data analytics for flowmeters data

This paper presents an improved tool for data validation and reconstruction of flowmeters. These sensors are installed in the Catalonia regional water network from Barcelona (Spain). Here a new time series model with exogenous variable is proposed with excellent results for data validation. It is postulated that the integration of the electronics alarms, along with other tests about the daily data accumulated and a later analysis of the data reconstruction allow to improve the results of the existing tools. This is accomplished by decreasing the false alarms and missing alarms of more than 6000 hourly data retrieved from more than 200 flowmeters each day. This new tool provides reliable information daily reliable information of the state of the water network. This information could potentially contribute to optimally control and manage this large and complex water network.Postprint (published version

TS-MPC for autonomous vehicles Including a TS-MHE-UIO estimator

© 2019 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, a novel approach is presented to solve the trajectory tracking problem for autonomous vehicles. This approach is based on the use of a cascade control where the external loop solves the position control using a novel Takagi Sugeno-Model Predictive Control (TS-MPC) approach and the internal loop is in charge of the dynamic control of the vehicle using a Takagi Sugeno-Linear Quadratic Regulator technique designed via Linear Matrix Inequalities (TS-LMI-LQR). Both techniques use a TS representation of the kinematic and dynamic models of the vehicle. In addition, a novel Takagi-Sugeno estimator-Moving Horizon Estimator-Unknown Input Observer (TS-MHE-UIO) is presented. This method estimates the dynamic states of the vehicle optimally as well as the force of friction acting on the vehicle that is used to reduce the control efforts. The innovative contribution of the TS-MPC and TS-MHE-UIO techniques is that using the TS model formulation of the vehicle allows us to solve the nonlinear problem as if it were linear, reducing computation times by 10-20 times. To demonstrate the potential of the TS-MPC, we propose a comparison between three methods of solving the kinematic control problem: Using the nonlinear MPC formulation (NL-MPC) with compensated friction force, the TS-MPC approach with compensated friction force, and TS-MPC without compensated friction force.This work was supported by the Spanish Min-istry of Economy and Competitiveness (MINECO) and FEDER through theProjects SCAV (ref. DPI2017-88403-R) and HARCRICS (ref. DPI2014-58104-R). The corresponding author, Eugenio Alcalá, is supported under FI AGAURGrant (ref 2017 FI B00433).Peer ReviewedPostprint (author's final draft

LPV-MP planning for autonomous racing vehicles considering obstacles

In this paper, we present an effective online planning solution for autonomous vehicles that aims at improving the computational load while preserving high levels of performance in racing scenarios. The method follows the structure of the model predictive (MP) optimal strategy where the main objective is to maximize the velocity while smoothing the dynamic behavior and fulfilling varying constraints. We focus on reformulating the non-linear original problem into a pseudo-linear problem by convexifying the objective function and reformulating the non-linear vehicle equations to be expressed in a Linear Parameter Varying (LPV) form. In addition, the ability of avoiding obstacles is introduced in a simple way and with reduced computational cost. We test and compare the performance of the proposed strategy against its non-linear approach through simulations. We focus on testing the performance of the trajectory planning approach in a racing scenario. First, the case of free obstacles track and afterwards a scenario including static obstacles. Simulation results show the effectiveness of the proposed strategy by reducing the algorithm elapsed time while finding appropriate trajectories under several input/state constraints.Peer ReviewedPostprint (author's final draft

LPV-MPC control of autonomous vehicles

In this work, a novel approach is presented to solve the trajectory tracking problem for autonomous vehicles. This method is based on the use of a cascade control where the external loop solves the position control using a novel Linear Parameter Varying - Model Predictive Control (LPV-MPC) approach and the internal loop is in charge of the dynamic control of the vehicle using a LPV - Linear Quadratic Regulator technique designed via Linear Matrix Inequalities (LPV-LMI-LQR). Both techniques use an LPV representation of the kinematic and dynamic models of the vehicle. The main contribution of the LPV-MPC technique is its ability to calculate solutions very close to those obtained by the non-linear version but reducing significantly the computational cost and allowing the real-time operation. To demonstrate the potential of the LPV-MPC, we propose a comparison between the non-linear MPC formulation (NL-MPC) and the LPV-MPC approach.This work has been partially funded by the Spanish Governmentand FEDER through the projects CICYT DEOCS and SCAV (refs.MINECO DPI2016-76493, DPI2017-88403-R). This work has alsobeen partially funded by AGAUR of Generalitat de Catalunyathrough the Advanced Control Systems (SAC) group grant (2017SGR 482), and by AGAUR and the Spanish Research Agencythrough the Maria de Maetzu Seal of Excellence to IRI (MDM-2016-0656).Peer ReviewedPostprint (author's final draft

Metodología de análisis y reconstrucción de datos para caudalimetros

ATLL opera una red de distribución incluida dentro de los Sistemas de Infraestructuras Críticas. Estos sistemas son complejos de gran escala, distribuidos geográficamente y descentralizados con una estructura jerárquica, que requieren sistemas de control en tiempo real altamente sofisticados que garanticen un alto rendimiento y mantenimiento cuando las condiciones no son favorables debido, por ejemplo, a mal funcionamiento de los sensores (averías, problemas de baterías, problemas de comunicación, etc.). La fiabilidad de la información es la base para tomar decisiones que optimicen el gasto de energía y reduzcan las pérdidas de agua al tiempo que garanticen un suministro adecuado a los consumidores en cantidad y calidad a pesar de las demandas cambiantes. El objetivo principal de esta metodología es validar los datos brutos de los sensores (en este caso caudalímetros) y, si los datos no son consistentes, intente estimarlos para reconstruirlos manteniendo un sistema de base de datos fiable, segura y completa. Este procedimiento permite tratar, filtrar, depurar y completar todos los datos brutos recibidos y transformarlos en información útil; primero, como diagnóstico de anomalías y, finalmente, para controlar y gestionar de manera óptima el sistema de distribución de agua.Peer ReviewedPostprint (published version

Trajectory management for aircraft noise mitigation

Comunicació convidadaThis paper gives an overview of aircraft trajectory management aimed at producing noise abatementprocedures. Area Navigation (RNAV) concepts play an important role in the design of flexible and, therefore, noise friendly depart or approach procedures. In addition, the lowest dispersion of RNAV tracks help to contain noise footprints in a smaller area if compared with footprints that are produced when conventional procedures are flown. However, RNAV turns still produce a significant amount of dispersion because of different aircraft performance and different Flight Management Systems (FMS) implementation. Noise exposure can be also mitigated if the aircraft trajectory is conveniently modified in the vertical plane. In this work, a brief overview of different lateral and vertical noise abatement strategies is given. Theoretical optimal trajectories are also assessed presenting some results of previous research done by the authors. The annoyance produced by aircraft noise in different noise sensitive locations is taken as minimization objective. This annoyance not only takes into account the measured acoustic values but also other important aspects that will affect the perceived annoyance by the population. The concept of equitable trajectories is also presented, where noise annoyance is minimized in the worst noise sensitive location and not as an average value for all locations

Leak localisation methodology and real applications

The leak localization community is very active and the Research Centre for Supervision, Safety and Automatic Control (CS2AC) is specially implicated in such an important issue. We have developed a methodology for leak localization using pressure measurements and hydraulic models. It is based on the fault detection and isolation theory and it evolved from a first version where binary residuals were generated to a correlation based method. This methodology was successfully applied in real networks. The improved leak localization approach includes contributions from other disciplines such as sensor placement, demand calibration and the accuracy assessment. This paper shows the evolution of a methodology due to the continuous work of a research team. First the algorithm is described. Results obtained in real networks are compared with those obtained in simulation. Finally, the new improvements of the methodology and the current challenges are presented and discussed