Antoni Escobet

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This paper deals with two of the main tasks of Fault Monitoring Systems (FMS): fault detection and fault identiication. During fault detection, the FMS should recognize that the plant behavior is abnormal, and therefore, that the plant is not working properly. During fault identiication, the FMS should conclude which type of failure has occurred. The rst(More)
This paper presents a methodology for leakage localization using FIR (Fuzzy Inductive Reasoning). A real water network situated in Barcelona has been subdivided in zones which could contain a leakage. Two sensors measure pressures on two separated points of the network. A faulty fuzzy model for each zone and sensor is generated. Test data have been used for(More)
The work presented in this paper is the rst attempt to apply the Fault Monitoring System developed in the context of the Fuzzy Inductive Reasoning methodology (FIRFMS) to a biomedical system, the human Central Nervous System (CNS) control. The CNS controls the hemodynamical system by generating the regulating signals for the blood vessels and the heart.(More)
This paper presents the low level control of an holonomic robot with four omnidirectional wheels. A robust control technique named Quantitative Feedback Theory (QFT), based on an uncertain linear model has been selected to design the PID speed controllers for the four-wheeled robot. A piecewise model has been estimated by means of the least squares(More)
1 In this research, a new platform for the Fuzzy Inductive Reasoning (FIR) methodology has been designed and developed under the Matlab environment. The new tool, named Visual-FIR, allows the identification of dynamic systems models in a user-friendly environment. FIR offers a model-based approach to modeling and predicting either univariate or multivariate(More)