Robust nonlinear predictive controller design using Particle Swarm Optimisation (PSO): a case study in speed control of an experimental hot-rolling mill

A new design methodology for an efficient implementation of Adaptive Fuzzy Predictive Control (AFPC) using a Radial Basis Function (RBF) based neural-fuzzy model and Particle Swarm Optimisation (PSO) for an experimental hot-rolling mill is proposed in this paper. A predictive model of the process based on RBF is studied. On-line adaptive neural network identification was used to adapt the model parameters. A modified PSO is used at the optimisation process in Nonlinear Model Predictive Control (NMPC) to calculate a sequence of future control actions. The performance of AFPC along with PSO was evaluated via a computing simulation platform under different operating conditions. The simulation results demonstrate the effectiveness of the proposed control scheme

Robust nonlinear predictive controller design using Partial Swarm Optimisation (PSO): a case study in speed control of an experimental hot-rolling mill (PDF Download Available). Available from: http://www.researchgate.net/publication/263844370_Robust_nonlinear_predictive_controller_design_using_Partial_Swarm_Optimisation_(PSO)_a_case_study_in_speed_control_of_an_experimental_hot-rolling_mill [accessed Sep 22, 2015].