Fault Tolerant Control of a PEM Fuel Cell using qLPV Virtual Actuators

Abstract This paper proposes a fault tolerant control (FTC) strategy based on the use of quasi-linear parameter varying (qLPV) virtual actuators approach for proton exchange membrane (PEM) fuel cells. The overall solution relies on adding a virtual actuator in the control loop to hide the fault from the controller point of view, allowing it to see the same plant as before the fault, in this way keeping the stability and some desired performances. The proposed methodology is based on the use of a REFERENCE model, where the resulting nonlinear error model is brought to a qLPV form that is used for control design by means of linear matrix inequalities (LMI)-based techniques. The resulting closed-loop error system is stable with poles placed in some desired region of the complex plane. Simulation results are used to show the effectiveness of the proposed approach.

[1]  Józef Korbicz,et al.  Active fault-tolerant control design for Takagi-Sugeno fuzzy systems , 2011 .

[2]  M. V. Iordache,et al.  Diagnosis and Fault-Tolerant Control , 2007, IEEE Transactions on Automatic Control.

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

[4]  Anna G. Stefanopoulou,et al.  Control-Oriented Modeling and Analysis for Automotive Fuel Cell Systems , 2004 .

[5]  Thomas Steffen,et al.  Control Reconfiguration After Actuator Failures Using Disturbance Decoupling Methods , 2006, IEEE Transactions on Automatic Control.

[6]  Vicenç Puig,et al.  Fault-tolerant explicit MPC of PEM fuel cells , 2007, 2007 46th IEEE Conference on Decision and Control.

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

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

[9]  Janina Decker,et al.  Fault Tolerant Control Systems Design And Practical Applications , 2016 .

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

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

[12]  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.

[13]  J. Lofberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004, 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508).

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

[15]  Kazuo Tanaka,et al.  Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach , 2008 .

[16]  Carlos Ocampo-Martinez,et al.  A Gain-Scheduled LPV Control for Oxygen Stoichiometry Regulation in PEM Fuel Cell Systems , 2014, IEEE Transactions on Control Systems Technology.

[17]  Damiano Rotondo,et al.  FDI and FTC of wind turbines using the interval observer approach and virtual actuators/sensors , 2014 .

[18]  Vicenç Puig,et al.  LPV observer design for PEM fuel cell system: Application to fault detection , 2011 .

[19]  Diego Feroldi.  Fault Diagnosis and Fault Tolerant Control of PEM Fuel Cell Systems , 2012 .