MACPERCON: Enhanced management topologies based on unfalsified control for PEM fuel cells performance improvement
MACPERCON is framed within the field of advanced automatic control systems applied to energy conversion systems. The project explores the way of analysing, designing and implementing novel controllers not used so far in the field of highly complex systems such as fuel cells. The particular strategy corresponds with the unfalsified control (UC), which is based on the idea of commutation of controllers depending on a desired system performance. Additionally, it is expected to complement the proposed control designs with other modern control strategies such as model predictive control (MPC) in order to manage control objectives that suit new performance indices related to the system behaviour. The idea of joining these two control techniques in complex systems has not been reported in the literature so far. MACPERCON also aims at providing tailored solutions to advance on purely theoretical issues as well as in current implementation topics.
1. Study and analyse the application of UC techniques in complex dynamical systems with different operating modes, model uncertainty, external disturbances and composed by subsystems of different physical nature.
2. Analyse and simulate the UC concepts applied to engineering problems. As a special case study, the use of UC in PEM fuel cell based systems will be considered.
3. Implement the resulting UC strategies over the real fuel cell based systems at the IRI’s Fuel Cells Laboratory.
4. Compare the performances obtained when using the proposed control approaches with other strategies recently proposed in the literature.
5. Develop references management methodologies based on MPC for UC topologies in order to optimise efficiency and to increase the durability of the fuel cell based system while is improved its dynamic response.
F. Bianchi, C. Ocampo-Martínez, C. Kunusch and R. Sánchez-Peña. Fault-tolerant unfalsified control for PEM fuel cell systems. IEEE Transactions on Energy Conversion, 30(1): 307-315, 2015.
J.D. Rojas, C. Kunusch, C. Ocampo-Martínez and V. Puig. Control-Oriented Thermal Modeling Methodology for Water-Cooled PEM Fuel-Cell-Based Systems. IEEE Transactions on Industrial Electronics, 62(8): 5146-5154, 2015.
J.A. Luna, C. Ocampo-Martínez and M. Serra. Nonlinear predictive control for the concentrations profile regulation under unknown reaction disturbances in a fuel cell anode gas channel. Journal of Power Sources, 282: 129-139, 2015.
F. Castaños and C. Kunusch. Dither-less extremum seeking for hydrogen minimization in PEM fuel cells. IEEE Transactions on Industrial Electronics, 62(8): 5218-5226, 2015.
F. Bianchi, C. Kunusch, C. Ocampo-Martínez and R. Sánchez-Peña. A gain-scheduled LPV control for oxygen stoichiometry regulation in PEM fuel cell systems. IEEE Transactions on Control Systems Technology, 22(5): 1837-1844, 2014.
J.A. Luna, C. Ocampo-Martínez and M. Serra. Nonlinear predictive control for the concentrations proﬁle regulation in a PEM fuel cell anode gas channel, 2014 European Control Conference, 2014, Strasbourg, France, pp. 1807-1812.
J.D. Rojas, C. Ocampo-Martínez and C. Kunusch. Thermal modelling approach and model predictive control of a water-cooled PEM fuel cell system, 39th Annual Conference of the IEEE Industrial Electronics Society, 2013, Vienna, Austria, pp. 3806-3811.
F. Bianchi, C. Kunusch, C. Ocampo-Martínez and R. Sánchez Peña. On the implementation of gain-scheduled LPV control for oxygen stoichiometry regulation in PEM fuel cells, 52nd IEEE Conference on Decision and Control, 2013, Florence, pp. 990-995.
C. Kunusch, J. Moreno and M. Angulo. Identification and observation in the anode line of PEM fuel cell stacks, 52nd IEEE Conference on Decision and Control, 2013, Florence, pp. 1665-1670.
A.M. Alzate and C. Ocampo-Martínez. Nonlinear model of leachate anaerobic digestion treatment process. Technical Report IRI-TR-14-02, Institut de Robòtica i Informàtica Industrial, CSIC-UPC, 2014.
J. Sánchez and C. Ocampo-Martínez. Temperature modelling and model predictive control of a pilot-scale batch reaction system. Technical Report IRI-TR-14-03, Institut de Robòtica i Informàtica Industrial, CSIC-UPC, 2014.