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Participation in the ECC16

Presentation of seven papers in the 2016 European Control Conference head in Aalborg University, Aalborg, Denmark, the June 29 - July 1

 

Papers presented:

Abstract: In order to cope with uncertainties present in the renewable energy generation, as well as in the demand consumer, we propose in this paper the formulation and comparison of three robust model predictive control techniques, i.e., multi-scenario, tree-based, and chance-constrained model predictive control, which are applied to a nonlinear plant-replacement model that corresponds to a real laboratory-scale plant located in the facilities of the University of Seville. Results show the effectiveness of these three techniques considering the stochastic nature, proper of these systems.
Abstract: This paper presents a new method for leak localization in Water Distribution Networks (WDNs) that uses a model based approach combined with Bayesian reasoning. Probability distribution functions in model-based pressure residuals are calibrated off-line for all the possible leak scenarios using a hydraulic simulator where leak size uncertainty, demand uncertainty and sensor noise are considered. A Bayesian reasoning is applied on-line to the available residuals to determine the location of leaks present in the WDN. A time horizon method combined with the Bayesian reasoning is also proposed to improve the accuracy of the leak localization method. The Hanoi DMA case study is used to illustrate the performance of the proposed approach.
Abstract:In this paper, a decentralized fault diagnosis algorithm for large scale system is proposed. The fault diagnosis algorithm starts from obtaining of ARRs (analytical redundancy relations) from a model. These analytical redundancy relations are converted into a graph, this graph is divided into various subgraphs using partition algorithm. From various subgraphs, different fault signature matrices are obtained and finally using various fault signature matrices and applying observer method a fault or faults are detected and isolated in large scale system. Entire proposed distributed fault diagnosis algorithm is divided into 5 different blocks. In order to illustrate the application of the proposed algorithm, a case study based on the Barcelona drinking water network (DWN) is used.
Abstract:The use of false or erroneous data can lead to wrong decisions when operating a system. In case of a water distribution network, the use of incorrect data could lead to errors in the billing system, waste of energy, incorrect management of control elements, etc. This paper is focused on detecting flow meters reading abnormalities by exploiting the temporal redundancy of the demand time series by means of artificial neural networks (ANN). Communication problems with the sensor generate missing data and bad maintenance service in the flow meters produce false data. In this work, a methodology to detect the false data (validate) and replace the missing or false data (reconstruct) is proposed. As a core methodology, ANNs are used to model the time series generated from the water demand flow meters, and use the confidence intervals to validate the information. To illustrate the proposed methodology, the application to flow meters in the water distribution network of Barcelona is used.
Abstract:This papers presents the analysis and comparison of interval observer and set membership approaches for the state estimation of uncertain systems. The two approaches assume that noise and disturbances are unknown but bounded. In this paper, both approaches are compared when implemented using zonotopes. Mathematical expressions of both approaches are compared and conditions under which both approaches provide the same state estimation are derived. At the end, an example is used for showing the effect of these conditions on estimation.
Abstract: In this paper, Economic Model Predictive Control (EMPC) with periodic terminal constraints has been addressed for nonlinear differential-algebraic equation systems with application to Drinking Water Networks (DWNs). DWNs have some periodic behaviours because of the daily seasonality of water demands and electrical energy price. The periodic terminal constraint and economic terminal cost have been implemented in the EMPC controller design for the purpose of achieving the convergence. In terms of the stochastic system including the system disturbances, the feasibility of the proposed EMPC strategy has been guaranteed by means of the soft constraint instead of a hard constraint by means of adding a slack variable. Finally, the comparison results in the case study of the DTown water network has been shown when applying the EMPC strategy with or without periodic terminal constraints.
Abstract: This papers presents the analysis and comparison of interval observer and set membership approaches for the state estimation of uncertain systems. The two approaches assume that noise and disturbances are unknown but bounded. In this paper, both approaches are compared when implemented using zonotopes. Mathematical expressions of both approaches are compared and conditions under which both approaches provide the same state estimation are derived. At the end, an example is used for showing the effect of these conditions on estimation.