Juan Carlos Tudón-Martínez

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A novel Fault-Tolerant Controller is proposed for an automotive suspension system based on a Quarter of Vehicle (QoV) model. The design is divided in a robust Linear Parameter-Varying controller used to isolate vibrations from external disturbances and in a compensation mechanism used to accommodate actuator faults. The compensation mechanism is based on a(More)
A comparison of fault diagnosis systems based on Dynamic Principal Component Analysis (DPCA) method and Artificial Neural Networks (ANN) under the same experimental data is presented. Both approaches are process history based methods which do not assume any form of model structure, and rely only on process historical data. The comparative analysis shows the(More)
Semi-active suspension systems aim to improve the stability and comfort of vehicles. Several semi-active suspension control strategies require the vertical velocities; however, the instrumentation of the vehicle normally does not include sensors for these variables, typically only accelerometers are available. Direct integration to estimate the(More)
Dynamic Principal Component Analysis (DPCA) and Artificial Neural Networks (ANN) are compared in the fault diagnosis task. Both approaches are process history based methods, which do not assume any form of model structure, and rely only on process historical data. Faults in sensors and actuators are implemented to compare the online performance of both(More)
A model for a Magneto-Rheological (MR) damper based on Artificial Neural Networks (ANN) is proposed. The design of the ANN model is focused to get the best architecture that manages the trade-off between computing cost and performance. Experimental data provided from two commercial MR dampers with different properties have been used to validate the(More)