采用多变量RBF神经网络的非线性内部迭代预测控制

doi:10.3969/j.issn.0255-8297.2018.04.013

Abstract

Abstract: For the complexity, strong nonlinearity and multi-variability in industrial processes, a nonlinear iterative predictive control algorithm based on radial base function (RBF) neural network is proposed. This algorithm employs multi-RBF neural network to approximate the nonlinear system, which can obtain an approximate model for the predictive model. Meanwhile in order to avoid increasing some computational burden, each predictive output along the future trajectory is expanded. Therefore the problem of nonlinear optimization is transformed into that of solving the easy quadratic programming,accordingly, thus the difficulty of solving the nonlinear differential equation in real-time online can be overcome, when deriving the predictive control law. Finally, the optimized control law can be obtained until the internal conditions are satisfied through internal iteration of t times. The simulation results for a pH process show that the proposed method is effective and feasible.

Key words: radial base function (RBF) neural network, predictive control, internal iterative, multi-variable

CLC Number:

P751.1

JIANG Xue-ying, SU Cheng-li, SHI Hui-yuan, LI Ping, LIU Si-yu. Nonlinear Internal Iterative Predictive Control Using Multi-RBF Neural Network[J]. Journal of Applied Sciences, 2018, 36(4): 698-710.

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Nonlinear Internal Iterative Predictive Control Using Multi-RBF Neural Network

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