Offset-Free Model Predictive Control of Fuel Cell DC-DC Boost Converter with Low-Complexity and High-Robustness

This article proposes an easily implementable and computationally effective model predictive control (MPC) scheme for fuel cell dc-dc boost converter. The proposed control scheme contains an explicit MPC method just with a single-step predictive horizon. Thus, the computational burden can be greatly reduced. To further enhance its antidisturbance ability, the observer technique is adopted to, respectively, estimate the load impedance and input voltage for the predictive models. Since the estimated variables can be directly sent to the cost function, the satisfactory disturbance rejection performance can be guaranteed with optimal control. Besides, an additional reference current generation module based on the flatness theory is built to deal with the inevitable parameter uncertainties and the estimation errors. Thus, the nominal system model can be directly adopted to further reduce the design complexity. Some simulation and experimental tests based on a multiphase interleaved fuel cell dc-dc boost converter are conducted to verify the superiority of the proposed control scheme. Results show that, with the proposed control scheme, both the dynamic performance and robustness of the closed-loop system are satisfactory, which can help to contribute to the fuel cell applications in transportation electrification.