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A neural network model predictive controller
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DOI：10.1016/j.jprocont.2006.06.001.png)
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Abstract
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A neural network controller is applied to the optimal model predictive control of constrained nonlinear systems. The control law is represented by a neural network function approximator, which is trained to minimize a control-relevant cost function. The proposed procedure can be applied to construct controllers with arbitrary structures, such as optimal reduced-order controllers and decentralized controllers. (C) 2006 Elsevier Ltd. All rights reserved.
Keywords:
model predictive control
neural networks
nonlinear control
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