Application of a neural network predictive control based on GGAP-RBF for the supercritical main steam

The Supercritical Main Steam has a large inertia, delay and nonlinear and dynamic characteristics change with the operating conditions, it is difficult to establish the precise mathematical model, this algorithm based on RBF neural network GGAP posed a direct neural network predictive controller, the combination of online learning and control to a supercritical power plant main stream temperature as the research object, MATLAB simulation results show that the superheated steam temperature system can achieve effective control, performance than the conventional PID control has greatly improved.