Predictive functional control based onartificial neural networks and it's application of coordinated control systems of fossil power plant

Combined with decoupling control algorithm, multivariable PFC is studied. Multivariable system is decoupled by adding neural networks compensation. Based on impulse transfer function, system impulse transfer model and inverse impulse transfer model are identified. Based on this, single-variable predictive functional control is applied to every decoupled sub-system to determine every control variable. The algorithm is used in simulation research on monoblock unit coordinate control system with time-varying model, which eliminated system noises by adding inverse neural network model. Results show that this algorithm has improved tracking performance, good disturbance resistance and robustness at the same time. This algorithm is thus capable of high quality control of complex multivariable processes. It is suitable for resolving multivariable system optimization and control.