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A nonlinear predictive control strategy based on radial basis function models
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DOI：10.1016/S0098-1354(96)00340-7.png)
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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. (C) 1997 Elsevier Science Ltd.
Keyword:
BASIS FUNCTION NETWORKS
NEURAL NETWORKS
LEARNING ALGORITHM
CHEMICAL PROCESSES
IDENTIFICATION
APPROXIMATION
SYSTEMS
DESIGN
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