Abstract
A nonlinear predictive control algorithm based on fuzzy model is presented for a family of complex system with severe nonlinearity such as Proton exchange membrane fuel cell (PEMFC). In order to implement nonlinear predictive control of the plant, the fuzzy model is identified by learning offline and rectified online. The model parameters are initialized by fuzzy clustering, and learned using back-propagation algorithm offline. If necessary, it can be rectified online to improve the predictive precision in the process of real-time control. Based on the obtained model, discrete optimization of the control action is carried out according to the principle of Branch and Bound (B&B) method. The test results demonstrate the effectiveness and advantage of this approach
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© 2004 Springer-Verlag Berlin Heidelberg
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Li, X., Fu, Xw., Cao, Gy., Zhu, Xj. (2004). Fuzzy Predictive Control Based on PEMFC Stack. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_23
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DOI: https://doi.org/10.1007/978-3-540-28648-6_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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