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离心式压缩机防喘振控制系统具有复杂非线性、高精度控制要求等特点,传统的控制方式难以实现理想的控制效果。针对这一问题,将模糊控制系统中方便的知识抽取表达和神经网络的自适应学习与并行计算等功能有机结合,得到了性能更加完善的模糊神经网络系统。仿真分析表明,采用模糊神经网络控制可显著提升离心式压缩机防喘振控制系统的工作性能,比模糊控制具有更小的超调量和振荡。
Centrifugal compressor anti-surge control system with complex nonlinear, high-precision control requirements and other characteristics of the traditional control method is difficult to achieve the desired control effect. In response to this problem, the fuzzy neural network system with more perfect performance is obtained by combining the convenient knowledge extraction and expression in fuzzy control system with the adaptive learning and parallel computing in neural network. Simulation results show that the fuzzy neural network control can significantly improve the performance of centrifugal compressor anti-surge control system than the fuzzy control with smaller overshoot and oscillation.