抄録
In thermal power plants, it is an important theme to improve the control accuracy of main steam pressure and temperature etc. during load up/down. This paper focused on temperature control that is most difficult due to the nonlinearity and long dead time of power plants. MRAC is applicable for the feedforward control of power plants, but there are some problems. The most serious problem is that PE (presistently exciting) condition is not satisfied, and so it is difficult to estimate plant parameters using the well-known recursive least square method. We proposed a combined method of neural networks and MRAC (Model Reference Adaptive Control) in which plant parameters are decided by Jacobian calculation of neural networks. This method was evaluated by a detailed simulator that represents accurately the dynamics of power plants.
