摘要
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 by a set of locally valid and simpler submodels. Two alternative methods of exploiting the NFNs within a generalised predictive control (GPC) framework for nonlinear model predictive control 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.
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 by a set of locally valid and simpler submod- els. Two alternative methods of exploiting the NFNs within a generalised predictive control （GPC） framework for nonlinear model predictive control 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.
出处
《自动化学报》
EI
CSCD
北大核心
2006年第5期785-790,共6页
Acta Automatica Sinica
基金
Supported by National Natural Science Foundation of P. R. China (69804003, 50576022)the Natural Science Foundation of Beijing (4062030)
