Title :
Nonlinear Neural Network Predictive Control for Power Unit using Particle Swarm Optimization
Author :
Xiao, Jian-mei ; Wang, Xi-huai
Author_Institution :
Dept. of Electr. Eng. & Autom., Shanghai Maritime Univ.
Abstract :
A novel approach of nonlinear model predictive control (NMPC) is proposed using radial basis function neural network (RBFNN) and particle swarm optimization (PSO). A multi-step predictive model of the controlled process based on RBFNN is studied. The fuzzy c-mean (FCM) clustering algorithm was used to determine the position of centers of the hidden layer of RBFNN. A modified PSO with simulated annealing is used at the optimization process in NMPC. The unit control for a fossil fuel power unit (FFPU) load system is studied. The simulation results demonstrate the effectiveness of the proposed algorithm
Keywords :
fossil fuels; fuzzy set theory; neurocontrollers; nonlinear control systems; particle swarm optimisation; pattern clustering; power generation control; power system simulation; predictive control; radial basis function networks; simulated annealing; thermal power stations; fossil fuel power unit load system; fuzzy c-mean clustering; nonlinear model predictive control; particle swarm optimization; radial basis function neural network; simulated annealing; Clustering algorithms; Control systems; Fossil fuels; Neural networks; Particle swarm optimization; Predictive control; Predictive models; Process control; Radial basis function networks; Simulated annealing; Fossil fuel power unit; fuzzy c-mean clustering; nonlinear model predictive control; particle swarm optimization; radial basis function neural network;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.259068
