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Neural networks for modelling and control of a non-linear dynamic system

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3 Author(s)
Murray-Smith, R. ; Daimler-Benz Res. Group., Berlin, Germany ; Neumerkel, D. ; Sbarbaro-Hofer, D.

The authors describe the use of neural nets to model and control a nonlinear second-order electromechanical model of a drive system with varying time constants and saturation effects. A model predictive control structure is used. This is compared with a proportional-integral (PI) controller with regard to performance and robustness against disturbances. Two feedforward network types, the multilayer perceptron and radial-basis-function nets, are used to model the system. The problems involved in the transfer of connectionist theory to practice are discussed

Published in:

Intelligent Control, 1992., Proceedings of the 1992 IEEE International Symposium on

Date of Conference:

11-13 Aug 1992