Power Plant Coordinated Predictive Control using Neurofuzzy Model

In unit steam-boiler generation, a coordinated control strategy is required to ensure a higher rate of load change without violating thermal constraints. The process is characterized by nonlinearity and uncertainty. While neural networks can model highly complex nonlinear dynamical systems, they produce black box models. This has led to significant interest in neuro-fuzzy networks (NFNs) to represent a nonlinear dynamical process. Two alternative methods of exploiting the NFNs within a generalised predictive control (GPC) framework are described. Coordinated control of steam-boiler generation using the two nonlinear GPC methods show excellent tracking and disturbance rejection results and improved performance compared with conventional linear GPC