Abstract
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.
Highlights
In recent years, with the gradual scarcity of non-renewable energy resources, people have been eager to find alternatives to fossil fuel
The proposed adaptive constrained control method based on the Wiener-type neural network presented in the above sections is used to obtain safe fuel utilization and make the solid oxide fuel cell (SOFC) system satisfy the operating constraints under the condition of changing load I
The effective and safe power source will provide stable electric energy for plants; we must guarantee that output voltage Vdc is the desired constant value as much as possible, while the external current load may have a negative impact on Vdc for the SOFC for some time
Summary
With the gradual scarcity of non-renewable energy resources, people have been eager to find alternatives to fossil fuel. Neural networks are widely used in identification and control of nonlinear systems with complex dynamics due to their ability to approximate any nonlinear function. In [16], a nonlinear predictive controller based on an improved radial basis function neural network identification model was designed to guarantee the fuel utilization to operate within a safe range. The complex dynamics of SOFC can be approximated via the Wiener-type neural network, and stability analysis of the proposed control algorithm can be completed by the Lyapunov function. Simulations of the SOFC prove that our adaptive tracking constrained control strategy based on Wiener model identification is highly effective. The main innovation points in this paper are as follows: An innovative method based on the Wiener-type neural network model can be used to identify the system dynamics of an SOFC.
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