基于TSA的非线性神经网络预测控制

doi:10.3969/j.issn.0255-8297.2018.05.014

Abstract

Abstract: Because present nonlinear predictive control methods are difficult to solve the nonlinear equation online, a nonlinear neural network predictive control scheme based on tree and seed algorithm(TSA)is proposed in this paper. In this scheme, a process model of the nonlinear system is firstly built up based on radical basis function(RBF)neural networks, and regarded as a predictive model to approximate the process performance of system. Then the predictive output is derived by this model and the quadratic performance index under constrains is designed. And the optimal control law of the nonlinear predictive control system can be online searched with TSA under the performance index. Thus, the proposed scheme can avoid the direct derivation of the control law in complex nonlinear optimization problems and reduce the computational burden. Simulation results of biochemical fermentation process show that the proposed control scheme performs excellent tracking and anti-disturbance abilities.

Key words: predictive control, biochemical fermentation, nonlinear optimization, radical basis function (RBF), tree and seed algorithm (TSA)

CLC Number:

P751.1

JIANG Xue-ying, SHI Hui-yuan, SU Cheng-li, LI Ping. Nonlinear Neural Network Predictive Control Based on Tree and Seed Algorithm[J]. Journal of Applied Sciences, 2018, 36(5): 870-878.

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Nonlinear Neural Network Predictive Control Based on Tree and Seed Algorithm

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