设定值
控制理论(社会学)
模型预测控制
非线性系统
人工神经网络
工程类
趋同(经济学)
控制工程
计算机科学
涡轮机
控制(管理)
人工智能
物理
量子力学
机械工程
经济
经济增长
作者
Chuanliang Cheng,Chen Peng,Xiangpeng Xie,Ling Wang
标识
DOI:10.1016/j.isatra.2024.01.029
摘要
Energy efficiency optimization for the ultra supercritical (USC) boiler-turbine unit is a major concern in the field of power generation. In order to deal with the nonlinearity and slow dynamic response problems, a new nonlinear control method is proposed which integrates internal model control (IMC) and generalized predictive control (GPC) into a unified framework. Specifically, through a long short-term memory (LSTM) neural network based IMC, the system achieves rapid convergence to the vicinity of the desired setpoint, significantly enhancing the response speed. Then, by a composite weighted human learning optimization network based nonlinear generalized predictive control (CWHLO-GPC), high-accuracy tracking performance is achieved. Finally, an example on a 1000MW USC power plant demonstrates the proposed method can achieve fast and stable dynamic response under large load variation.
科研通智能强力驱动
Strongly Powered by AbleSci AI