Neural network enhancement of closed-loop controllers for nonlinear systems

The actual trend is to combine traditional control methods with neural networks in parallel. This paper places the neural network inside the closed loop, in series with the existing controller. With the neural network inside the closed-loop, randomly initialized weights, unknown performance levels,...

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Bibliographic Details
Published in7th International Workshop on Advanced Motion Control. Proceedings (Cat. No.02TH8623) pp. 291 - 295
Main Authors Trusca, M., Lazea, G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Automatic control
Automation
Control systems
Convergence
Neural networks
Neurofeedback
Nonlinear control systems
Nonlinear systems
Testing
White noise
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Summary:The actual trend is to combine traditional control methods with neural networks in parallel. This paper places the neural network inside the closed loop, in series with the existing controller. With the neural network inside the closed-loop, randomly initialized weights, unknown performance levels, and multiple reinitializations are more difficult. A problem not so readily seen is that the weights update rules for neural networks that were not designed to work in a feedback setting but in a feed-forward setting. The derivation of update rules, particularly for back propagation, were based on the independence of the weights and the input to the neural network. For a neural network in the closed-loop, the assumption is no more valid; therefore, a new update rule had to be derived.
ISBN:0780374797
9780780374799
DOI:10.1109/AMC.2002.1026933