Neural Network Based Model Predictive Control
Neural Network Based Model Predictive Control
复制标题
基于神经网络的模型预测控制
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
M. Gerules
中科院分区：
文献类型：
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作者：
S. Piche;J. Keeler;G. Martin;G. Boe;Doug Johnson;M. Gerules
模型预测控制（MPC），一种基于过程模型使用优化器来求解未来时间范围内的最优控制移动的控制算法，在过去二十年中已经成为过程工业中的标准控制技术。在大多数工业应用中，使用使用经验数据开发的线性动态模型，即使过程本身通常是非线性的。线性模型已经被使用，因为在开发一个通用的非线性模型从经验数据和计算费用往往涉及使用非线性模型的困难。在本文中，我们提出了一个通用的神经网络为基础的技术开发非线性动态模型的经验数据，并表明这些模型可以有效地用于模型预测控制框架。这种基于非线性预测控制的方法已经成功地应用于炼油、石油化工、造纸和食品工业的许多工业应用中。控制器的非线性工业过程，聚乙烯反应器的性能。
Model Predictive Control (MPC), a control algorithm which uses an optimizer to solve for the optimal control moves over a future time horizon based upon a model of the process, has become a standard control technique in the process industries over the past two decades. In most industrial applications, a linear dynamic model developed using empirical data is used even though the process itself is often nonlinear. Linear models have been used because of the difficulty in developing a generic nonlinear model from empirical data and the computational expense often involved in using nonlinear models. In this paper, we present a generic neural network based technique for developing nonlinear dynamic models from empirical data and show that these models can be efficiently used in a model predictive control framework. This nonlinear MPC based approach has been successfully implemented in a number of industrial applications in the refining, petrochemical, paper and food industries. Performance of the controller on a nonlinear industrial process, a polyethylene reactor, is presented.