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Ahstract-In this paper, an FTC strategy using Linear Parameter Varying (LPV ) virtual sensors is proposed and applied to the IFAC wind turbine case study. The novelty of the proposed strategy consists in that virtual sensors are applied to the FTC problem in a new original fashion. Instead of hiding the fault, the virtual sensors are used to expand the set of available sensors. Then, the state observer is designed using LPV techniques based on Linear Matrix Inequalities (LMIs) taking into account a varying parameter that is introduced in order to select which sensors are used by the observer among the physical and the virtual ones. In this sense, the proposed approach can be considered as a multisensor fusion strategy that integrates data provided by various sensors in order to obtain a better estimation.In order to reduce the cost of energy generated by wind turbines, it is of high importance to increase their reliability. For this reason, research in Fault Detection and Isolation (FDI) and Fault Tolerant Control (FTC) with application to wind turbines has become a subject of interest in research. Until a few years ago, there were no FTC applications to wind turbines in the literature, the common approach being to monitor the turbine conditions and shut it down in the event a fault were detected [l]. In [2], a solution to this problem based on the design of passive and active FTC was proposed for a 4.8 MW variable-speed, variable-pitch wind turbine model with a fault in the pitch system. In [3], a multiobserver switching control strategy for robust active fuzzy FTC has been proposed for variable-speed wind energy conversion systems subject to sensor faults. Recently, a benchmark model of a wind turbine containing sensors, actuators and system faults has been presented, in order to test different detection, isolation and accommodation schemes [4]. Remarkable results were obtained in both the FDI [5] and the FTC parts [6]-[9]. The FTC approach considered in this paper uses the idea of virtual sensors. Initially developed for linear systems [10], virtual sensors have been successfully extended to piecewise affine [11], Hammerstein-Wiener [12] and LPV [13]-[15]systems. The interest in this latter class of systems lies in that LPV techniques have consolidated as an efficient solution to analysis and synthesis problems for nonlinear systems. Since their introduction by Shamma in his Ph.D. thesis [16], the LPV approach has become a standard formalism in systems and control [l7], because gain-scheduling of nonlinear systems can be performed according to the LPV paradigm, where the non-linearity is embedded in the varying parameters that depend on some endogenous signals.In this paper, the idea of virtual sensors is applied to the FTC problem in a new and original way. Instead of hiding the fault, the virtual sensor is used to expand the set of available sensors before the state observer is designed. A parameter is introduced to select the sensors to be used by the observer among the physical and the virtual ones. The pr...

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The present work proposes a Fault Tolerant Control (FTC) methodology for nonlinear discrete-time systems that can be modeled as Linear Parameter Varying (LPV) systems. The proposed approach relies on the modeling of faults as additional scheduling parameters of the LPV model for the controlled system and it uses a triple loop architecture. The inner control loop is designed by means of the standard H 2 /H ∞ control methodologies based on Linear Matrix Inequalities (LMIs). The design takes into account a prespecified set of faults and the ranges of their magnitudes that are wanted to be tolerated and it assumes the availability of on-line fault estimations provided by a Fault Detection and Isolation (FDI) module. The resulting controller tries to compensate the system faults in order to maintain a satisfactory closed-loop dynamic performance, but it does not take into account possible system input and state constraints associated to actuator saturation and other physical limitations. Thus, an intermediate control loop determines the actual compensation feasibility using set invariance theory. And, when it is needed, it applies suitable additive predictive control actions that enlarge the invariant set, trying to assure that the current state remains inside the enlarged invariant set. Finally, an outer loop implements a model reference control that allows reference tracking. The use of the proposed FTC methodology is illustrated through its application to the well-known quadruple tank system benchmark.