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Generalized Predictive Control for Industrial Processes Based on Neuron Adaptive Splitting and Merging RBF Neural Network
IEEE Transactions on Industrial Electronics ( IF 7.5 ) Pub Date : 5-10-2018 , DOI: 10.1109/tie.2018.2835402 Shiwen Xie , Yongfang Xie , Tingwen Huang , Weihua Gui , Chunhua Yang
IEEE Transactions on Industrial Electronics ( IF 7.5 ) Pub Date : 5-10-2018 , DOI: 10.1109/tie.2018.2835402 Shiwen Xie , Yongfang Xie , Tingwen Huang , Weihua Gui , Chunhua Yang
An adaptive generalized predictive control (GPC) scheme for an industrial process is designed based on a neuron adaptive splitting and merging radial basis function neural network (NASM-RBFNN) in this paper. The NASM-RBFNN is developed to identify the dynamic behaviors of the industrial process. In order to provide an accurate prediction model for the GPC, a neuron adaptive splitting and merging strategy and a weighted parameters adaptive correction approach are proposed. The neuron adaptive splitting and merging strategy can automatically add or delete the hidden neurons on-line, and the weighted parameters adaptive correction approach can update the weights based on the error of the neural network. The proposed approaches can enable the NASM-RBFNN to be adapted to the time-varying production condition and stochastic disturbance. The stability analysis and convergence of neuron adaptive splitting and merging strategy and weighted parameters adaptive correction approach are given. Finally, the NASM-RBFNN-based GPC (NASM-RBFNN-GPC) is applied to control the iron removal process in the largest zinc hydrometallurgy plant in China. The industrial experiments demonstrate that the NASM-RBFNN-GPC has a satisfactory tracking and control performance.
更新日期：2024-08-22
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