A bank of virtual sensors for active Fault Tolerant Control of LPV systems

In this paper, a Fault Tolerant Control (FTC) strategy based on a bank of virtual sensors is proposed for Linear Parameter Varying (LPV) systems subject to sensor faults, with the novel feature that, instead of hiding the faults as in the classical virtual sensor paradigm, the virtual sensors are used to expand the set of available sensors. An LPV state feedback controller and an LPV state observer are included in the scheme, as well as a parameter that is used by the observer to select which sensors are used among the physical and the virtual ones. An example is used to show the application of the proposed method.

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

[2]  Michel Kinnaert,et al.  Diagnosis and Fault-Tolerant Control , 2004, IEEE Transactions on Automatic Control.

[3]  Damiano Rotondo,et al.  A virtual actuator and sensor approach for fault tolerant control of LPV systems , 2014 .

[4]  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..

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

[6]  H. Werner,et al.  Automated Generation and Assessment of Affine LPV Models , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

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

[8]  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.

[9]  Christopher Edwards,et al.  Sensor fault tolerant control using sliding mode observers , 2006 .

[10]  Johan Efberg,et al.  YALMIP : A toolbox for modeling and optimization in MATLAB , 2004 .

[11]  Pierre Apkarian,et al.  Self-scheduled H∞ control of linear parameter-varying systems: a design example , 1995, Autom..

[12]  Thomas Steffen,et al.  Control Reconfiguration of Dynamical Systems: Linear Approaches and Structural Tests , 2005 .

[13]  V.F. Montagner,et al.  State feedback gain scheduling for linear systems with time-varying parameters , 2004, Proceedings of the 2004 American Control Conference.

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

[15]  Mohammed Chadli,et al.  Observer-based controller for Takagi-Sugeno models , 2002, IEEE International Conference on Systems, Man and Cybernetics.

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

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

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

[19]  Jan H. Richter,et al.  Reconfigurable Control of Nonlinear Dynamical Systems: A fault-hiding Approach , 2011 .

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

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

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

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

[24]  M. Benosman,et al.  A Survey of Some Recent Results on Nonlinear Fault Tolerant Control , 2010 .

[25]  Youmin Zhang,et al.  Bibliographical review on reconfigurable fault-tolerant control systems , 2003, Annu. Rev. Control..