Decoupling Control of Fuel Cell Air Supply System Based on Data-Driven Feedforward and Adaptive Generalized Supertwisting Algorithm
Lin Chen, Jinfa Liu, Shihong Ding, Jing Zhao, Jinwu Gao, Hong Chen
Abstract
Decoupling control of the air supply system is crucial for enhancing the performance and prolonging the service life of proton exchange membrane (PEM) fuel cells. However, the strong coupling and nonlinearity inherent in the system pose significant challenges. Current decoupling techniques typically rely on model knowledge and commonly overlook the avoidance of compressor surge, which motivates our work with a twofold contribution. We first design a data-driven feedforward (DDF) and propose a feasible domain constraint (FDC) to avoid surge. Subsequently, an adaptive generalized supertwisting algorithm (AGSTA) is presented that eliminates the residual tracking errors of the DDF. Furthermore, its gradient descent principle and stability are demonstrated. The proposed method has been validated on an air supply system test bench and a hardware-in-the-loop (HiL) platform carrying a fuel cell electric vehicle (FCEV) model. The results indicate that our approach is more advantageous in terms of tracking accuracy, response speed, overshoot suppression and computational cost.