Adaptive Pseudo Linear RBF Model for Process Control
A pseudo-linear radial basis function (PLRBF) network is developed in this paper. This network is used to model a real process and its weights are on-line updated using a recursive orthogonal least squares (ROLS) algorithm. The developed adaptive model is then used in model predictive control strategy, which is applied to a pilot multivariable chemical reactor. The first stage of the project, simulation study, has been investigated and is presented. The effectiveness of the adaptive control in improving the closed-loop performance has been demonstrated for process time-varying dynamics and model-process mismatch.
KeywordsModel Predictive Control Radial Basis Function Neural Network Adaptive Model Internal Model Control Hide Layer Node
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