Quantized Event-Based Sampled-Data Nonlinear Control for PEM Fuel Cell Air Supply

The problem of oxygen starvation avoidance in air-feed proton exchange membrane fuel cells is widely known and studied in the literature because it crucially affects the performances of the overall system. To address this problem, many nonlinear control techniques have been provided which, however, do not take into account the effects on the performances induced by the digital hardware commonly used for the practical implementation of control strategies. In this paper, a methodology for the design of quantized sampled-data event-based stabilizers is proposed for a class of time-varying non-linear systems and applied to the problem of oxygen starvation and maximum net power achievement in Proton Exchange Membrane Fuel Cells (PEMFCs). In particular, by exploiting a Lyapunov-based approach and the stabilization in the sample-and-hold sense theory, it is shown that there exist a suitably fast sampling and an accurate quantization of the input/output channels such that the digital event-triggered implementation of a proposed continuous-time stabilizer ensures the semi-global practical stability property of the related closed-loop system. In the theory developed here, time-varying sampling periods and non-uniform quantization of the input/output channels are allowed. Simulations confirm the effectiveness of the theoretical results.