In this thesis we propose a robust PID controller to regulate the hydrogen flow of proton exchange membrane fuel cells. The controller allows the so-called hydrogen excess ratio to track a desired value rapidly in order to achieve saving hydrogen and to avoid damage of the fuel cell when the power output of the fuel cell varies from one level to another. The fuel cell system is governed by a set of complicated nonlinear dynamical equations. To ease the control design task, we model the system, at each operating point, as a feedback interconnection of a linear time-invariant nominal part with a norm-bounded perturbation. We use the technique of system identification to acquire the transfer function representation of the nominal part and the size of the perturbation. To do this, the chirp signal is adopted to excite the system and the observed response is analyzed using spectral analysis to obtain the model. Based on the model, a $H_{infty}$ PID controller is designed for the fuel cell system. The design is tested on an experimental platform. The experimental results verify that the proposed controller can regulate the hydrogen excess ratio rapidly under load variation, and effectively reject the influence of external disturbances.

This thesis proposes an adaptive control approach to regulate the hydrogen feed of a fuel cell. The goal of the controller is to maintain the so-called hydrogen excess ratio, defined as the ratio between the hydrogen fed to the cell stake and those consumed in the stake, at a desired level when the fuel cell is under load variation. Maintaining the hydrogen excess ratio at an appropriate level would avoid hydrogen starvation, which is crucial for slowing degeneration of the fuel cell membranes and prolonging the life of the cell stake. The control approach we propose is based on the receding horizon linear quadratic optimal control algorithm with an on-line turning scheme which updates the plant model according to real-time measurement. To ease the computational complexity and make real-time turning realizable, we adopt a simple autoregressive with external disturbance (ARX) model to approximate the complicate chemical/electrical process of the fuel cell. The proposed adaptive control approach is implemented on an experimental platform. The experimental results show that the proposed control works with reasonably good performance.