Adaptive Tracking Constrained Controller Design for Solid Oxide Fuel Cells Based on a Wiener-Type Neural Network

In order to solve the control problem of the solid oxide fuel cell(SOFC), a novel adaptive tracking constrained control strategy based on a Wiener-type neural network is proposed in this paper. The working principle of SOFC is introduced, and the dynamical model of SOFC is studied. Besides, a Wiener model formulation for SOFC is proposed to approximate the nonlinear dynamics of the system, and an adaptive Wiener model identification method is utilized to identify the parameters of the model. Moreover, an adaptive exponential PID controller is designed to keep the stack output voltage stable. Meanwhile, the saturation problem is considered in the paper including input magnitude and rate constraints. Additionally, an anti-windup compensator is employed to eliminate the abominable influence of the saturation problem. Then, the stability of the control plant is analyzed and proven via the Lyapunov function. Finally, the simulation based on the MATLAB/Simulink environment is carried out, and the conventional PID controller is added and simulated as a contrast to verify the control performance of the proposed control algorithm. The results indicate that the proposed control algorithm possesses favorable control performance when dealing with nonlinear systems with complex dynamics.