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Implementation of neural network based non-linear predictive control
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DOI：10.1016/S0925-2312(98)00114-3.png)
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This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system. (C) 1999 Elsevier Science B.V. All rights reserved.
Keyword:
predictive control
quasi-Newton optimization
implementation issues
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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