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.