Interval Methods for Real-Time Capable Robust Control of Solid Oxide Fuel Cell Systems

Reliable control strategies for complex dynamic systems have to account for stability and robustness despite the presence of both parameter uncertainty and measurement errors. In addition, such control strategies have to comply with performance specifications that can be described either by the minimization of suitable cost functions or by direct specifications of desired reference trajectories. To handle bounded uncertainty and errors in a reliable way, it is possible to include the use of interval analysis in real-time control environments. Previous work has shown that approaches based on the general methodology of sliding mode and predictive control are promising options in this context. This paper presents a comparison of the properties of interval extensions of both types of control procedures for the thermal subsystem of a high-temperature solid oxide fuel cell. Representative simulation results conclude this contribution.

[1]  Anna G. Stefanopoulou,et al.  Control of Fuel Cell Power Systems: Principles, Modeling, Analysis and Feedback Design , 2004 .

[2]  Luc Jaulin,et al.  Applied Interval Analysis , 2001, Springer London.

[3]  Andreas Rauh,et al.  Reliable control of high-temperature fuel cell systems using interval-based sliding mode techniques , 2016, IMA J. Math. Control. Inf..

[4]  Andreas Rauh,et al.  Interval Methods for Sensitivity-Based Model-Predictive Control of Solid Oxide Fuel Cell Systems , 2013, Reliab. Comput..

[5]  N. Nedialkov,et al.  Interval Tools for ODEs and DAEs , 2006, 12th GAMM - IMACS International Symposium on Scientific Computing, Computer Arithmetic and Validated Numerics (SCAN 2006).

[6]  Stefano Ubertini,et al.  Modeling Solid Oxide Fuel Cells: Methods, Procedures and Techniques , 2014 .

[7]  Andreas Gubner,et al.  Non-Isothermal and Dynamic SOFC Voltage-Current Behavior , 2006 .

[8]  Nedialko S. Nedialkov,et al.  Implementing a Rigorous ODE Solver Through Literate Programming , 2011 .

[9]  D. Limón,et al.  Robust MPC of constrained nonlinear systems based on interval arithmetic , 2005 .

[10]  Andreas Rauh,et al.  Thermal behavior of high-temperature fuel cells: reliable parameter identification and interval-based sliding mode control , 2013, Soft Comput..

[11]  Andreas Griewank,et al.  Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition , 2000, Frontiers in applied mathematics.

[12]  Andreas Rauh,et al.  Experimental validation of a sensitivity-based observer for solid oxide fuel cell systems , 2013, 2013 18th International Conference on Methods & Models in Automation & Robotics (MMAR).

[13]  Andreas Rauh,et al.  Robust Sliding Mode Techniques for Control and State Estimation of Dynamic Systems with Bounded and Stochastic Uncertainty , 2014 .

[14]  Andreas Rauh,et al.  Variable Structure Approaches for Temperature Control of Solid Oxide Fuel Cell Stacks , 2014 .

[15]  Andreas Rauh,et al.  Validated Modeling of Mechanical Systems with SmartMOBILE: Improvement of Performance by ValEncIA-IVP , 2008, Reliable Implementation of Real Number Algorithms.

[16]  Andreas Rauh,et al.  Verification Techniques for Sensitivity Analysis and Design of Controllers for Nonlinear Dynamic Systems with Uncertainties , 2009, Int. J. Appl. Math. Comput. Sci..

[17]  Andreas Rauh,et al.  Sensitivity-based feedforward and feedback control for uncertain systems , 2011, Computing.