Fuel cells represent an area of great industrial interest due to the possibility to generate clean energy for stationary and automotive applications. It is clear that the proper performance of these devices is closely related to the kind of control that is used; therefore, a study of improved control alternatives is fully justified. The air-supply control is widely used to guarantee safety and to achieve a high performance. This paper deals with this control loop, proposing and comparing two control objectives aimed at satisfying the oxygen starvation avoidance criterion and the maximum efficiency criterion, respectively. The control architecture is based on a constrained explicit model predictive control (MPC) law suitable for real-time implementation due to its low computational demands. The proposed controller is implemented and evaluated on a 1.2-kW polymer electrolyte membrane or proton exchange membrane fuel-cell test bench, thus obtaining real data which show that the maximum efficiency criterion does not conflict with the starvation avoidance criterion and allows system performance improvements of up to 3.46%. Moreover, experimental results utilizing the explicit MPC approach also show improved transient responses compared to those of the manufacturer's control law.
This paper presents the application of a distributed model predictive controller (DMPC) to the control of an accurate model of an actual irrigation canal in Spain. The canal is modeled using the Saint-Venant equations and implemented using the well-known Simulation of Irrigation Canals (SIC) modeling software for irrigation canals. The DMPC algorithm has been implemented in Matlab and interfaced to SIC. In the distributed-control algorithms, the local controllers exchange information so that their control policies are optimal in the sense of getting the best value of a performance index. The results show that the proposed distributed-control algorithm obtains better control performance than a more-conventional decentralized control scheme without information exchange. This better performance translates directly into money and resource savings.