Computers & Chemical Engineering, Vol.21, No.9, 965-980, 1997
A Nonlinear Predictive Control Strategy Based on Radial Basis Function Models
A predictive control strategy for nonlinear processes based on radial basis function models is proposed. First, a radial basis function model of the process is developed using stepwise regression and least squares estimation. This model is then used to train a nonlinear predictive controller, which is also implemented as a radial basis function network. Since no optimization problems have to be solved on-line, this control strategy can be implemented easily. The proposed strategy is applied to an experimental pH neutralization process; it provides both excellent setpoint tracking and disturbance rejection when compared to conventional PI control.
Keywords:BASIS FUNCTION NETWORKS;NEURAL NETWORKS;LEARNING ALGORITHM;CHEMICAL PROCESSES;IDENTIFICATION;APPROXIMATION;SYSTEMS;DESIGN