SELF-ADAPTIVE RBF NEURAL NETWORK PID CONTROL IN EXHAUST TEMPERATURE OF MICRO GAS TURBINE

摘要：

Mathematical model of exhaust temperature control in micro gas turbine is introduced. To obtain better performance, a self-adaptive PID control is applied to the exhaust temperature control. The parameters of PID control are tuned by radial basis function (RBF) neural network. In this paper, the RBF neural network is given which has been used extensively in the areas of pattern recognition, systems modeling and identification. The effectiveness and efficiency of the proposed control strategy is demonstrated by applying it to the exhaust temperature control. The simulations show that the dynamic responses of the exhaust control system can be effectively improved and the anti-disturbance of the proposed controller is better than that of the PID controller. However, the learning rate of RBK neural network and PID parameters is not too large due to the great gain of micro gas turbine. Otherwise the output will surge acutely.

关键词： Radial basis function(RBF) Neural network PID control Self-adaptive Ezhaust temperature control Micro gas turbine

作者: JIANG-JIANG WANG CHUN-FA ZHANG YOU-YIN JING

作者单位: School of Energy and Power Engineering, North China Electric Power University, Baoding 071003, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 2131-2136

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）