Title :
Neural Networks Based Predictive Control for TRT
Author :
Yang, Chunjie ; Wu, Ping
Author_Institution :
Ind. Inst. of Process Control, Zhejiang Univ., Hangzhou
Abstract :
This paper presents a neural network based predictive control for TRT (top gas pressure recovery turbine). TRT is a nonlinear system with time-delay. To keep the top gas pressure stable, predictive control is developed and a RBF neural network which is trained using the input-output data of the practical process as predictive model, RBFNN (radial basis function neural network) can approach any nonlinear function in theory. Simulation results show that the neural network based predictive controller can obtain satisfactory performance in top gas pressure control
Keywords :
gas turbines; neurocontrollers; nonlinear control systems; predictive control; pressure control; radial basis function networks; nonlinear system; predictive control; pressure control; radial basis function neural network; time-delay; top gas pressure recovery turbine; Blast furnaces; Electrical equipment industry; Industrial control; Neural networks; Nonlinear systems; Predictive control; Predictive models; Pressure control; Process control; Turbines;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614796