A novel adaptive neural network constrained control for solid oxide fuel cells via dynamic anti-windup
A novel adaptive neural network constrained control for solid oxide fuel cells via dynamic anti-windup
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DOI：
10.1016/j.neucom.2016.05.076
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发表时间：
2016-11
期刊：
影响因子：
6
通讯作者：
Nan Ji;Dezhi Xu;Fei Liu
中科院分区：
文献类型：
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作者：
Nan Ji;Dezhi Xu;Fei Liu
提出了一种基于神经网络的固体氧化物燃料电池（SOFC）自适应约束控制方案。首先，设计了一种用于SOFC动态模型辨识的径向基函数（RBF）神经网络。通过辨识出的RBF模型可以得到雅可比信息。然后，利用BP神经网络具有较强的自学习和自适应能力，设计了一种基于BP神经网络的PID控制器。同时，为了解决SOFC的控制输入饱和和燃料利用率问题，提出了一种动态抗饱和补偿器来适应参考。基于李雅普诺夫稳定性分析，从理论上证明了该方法的稳定性。最后，以固体氧化物燃料电池为例进行了仿真，验证了所提出的约束控制方法的有效性。
This paper proposes a neural network based adaptive constrained control scheme for a solid oxide fuel cell (SOFC). First, a radial basis function (RBF) neural network is designed for the identification of SOFC dynamic model. The Jacobian information can be obtained through the identified RBF model. Then, a back propagation (BP) neural network based PID controller is designed to tune the parameters that BP neural network has strong self-learning and adaptive capabilities. At same time, in order to solve the control input saturation and fuel utilization problems of SOFC, a dynamic anti-windup compensator is proposed for accommodating the reference. Moreover this paper theoretically proves the stability of the proposed method based on Lyapunov stability analysis. Finally, the simulation results for SOFC are provided to demonstrate the effectiveness of the proposed constrained control approach.