FTC of LPV systems using a bank of virtual sensors:Aapplication to wind turbines

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.

[1]  Stéphane Ploix,et al.  Fault diagnosis and fault tolerant control , 2007 .

[2]  José A. De Doná,et al.  Fault-tolerant control of systems with convex polytopic linear parameter varying model uncertainty using virtual-sensor-based controller reconfiguration , 2013, Annu. Rev. Control..

[3]  Jeff S. Shamma,et al.  Analysis and design of gain scheduled control systems , 1988 .

[4]  Elkhatib Kamal,et al.  Robust Fuzzy Fault-Tolerant Control of Wind Energy Conversion Systems Subject to Sensor Faults , 2012 .

[5]  P. Gahinet,et al.  H∞ design with pole placement constraints: an LMI approach , 1996, IEEE Trans. Autom. Control..

[6]  Damiano Rotondo,et al.  Fault Tolerant Control of the Wind Turbine Benchmark using Virtual Sensors/Actuators , 2012 .

[7]  Jakob Stoustrup,et al.  Control reconfiguration of LPV systems using virtual sensor and actuator , 2012 .

[8]  Nathan van de Wouw,et al.  Reconfigurable control of piecewise affine systems with actuator and sensor faults: Stability and tracking , 2011, Autom..

[9]  Inseok Yang,et al.  Control Allocation based Compensation for Faulty Blade Actuator of Wind Turbine , 2012 .

[10]  Jeff S. Shamma,et al.  An Overview of LPV Systems , 2012 .

[11]  Ian Postlethwaite,et al.  Affine LPV modelling and its use in gain-scheduled helicopter control , 1998 .

[12]  Jos F. Sturm,et al.  A Matlab toolbox for optimization over symmetric cones , 1999 .

[13]  Ron J. Patton,et al.  Global wind turbine FTC via T-S fuzzy modelling and control , 2012 .

[14]  Peter Fogh Odgaard,et al.  Fault tolerant control of wind turbines: a benchmark model , 2009 .

[15]  Peter Fogh Odgaard,et al.  Results of a wind turbine FDI competition , 2012 .

[16]  Pascal Gahinet,et al.  H/sub /spl infin// design with pole placement constraints: an LMI approach , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[17]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[18]  Jan H. Richter,et al.  Reconfigurable Control of Nonlinear Dynamical Systems , 2011 .

[19]  J. Lunze,et al.  Control reconfiguration demonstrated at a two-degrees-of-freedom helicopter model , 2003, 2003 European Control Conference (ECC).

[20]  Ricardo Salvador Sánchez Peña,et al.  LPV control of a 6-DOF vehicle , 2002, IEEE Trans. Control. Syst. Technol..

[21]  Silvio Simani,et al.  Active actuator fault‐tolerant control of a wind turbine benchmark model , 2014 .

[22]  L.Y. Pao,et al.  Control of variable-speed wind turbines: standard and adaptive techniques for maximizing energy capture , 2006, IEEE Control Systems.

[23]  Damiano Rotondo,et al.  Fault estimation and virtual sensor FTC approach for LPV systems , 2011, IEEE Conference on Decision and Control and European Control Conference.

[24]  Jakob Stoustrup,et al.  Robust and fault-tolerant linear parameter-varying control of wind turbines , 2011 .