A neural network control system with parallel adaptive enhancements applicable to nonlinear servomechanisms

Lee, T.H.; Tan, W.K.; Ang, Marcelo H.

doi:10.1109/41.293896

Cited by 15 publications

(1 citation statement)

References 13 publications

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“…That difference may be valuable enough so ensure that the structure replacement is more better than more training or increasing units for a certain structure. In this work MLP network does better than RBFs network [6,7,9,10]. …”

Section: Discussionmentioning

confidence: 98%

Microelectronic controller design for pole balancing problem

Elnaby

Enab²,

Hemeda³

2000

ICM'99. Proceedings. Eleventh International Conference on Microelectronics (IEEE Cat. No.99EX388)

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The reason for the present upsurge of interest in intelligent control is that the present generations of control systems are incapable, to a greater or lesser extent, of dealing with problems characterized by a certain complexity. Fortunately, human operators (HO) are often experts in keeping the complex control systems on the right track. Among intelligent controller design techniques, Neurocontrol is a dynamic research field that has attached considerable attention from the scientific and control engineering community in the last several years. In this paper, the template learning Neurocontrol approach has been used for controller design where the HO, the most successful intelligent controller available until now, is the template controller. This approach has been applied to pole balancing problem. Several structures of feedforward neural networks had been used to implement the controller and their relative advantages and disadvantages were investigated.The resulting controller succeeded to balance the pole for reasonable time. The approach seemed to be useful in building complex non-linear systems controllers where no need for system models and ease of development.

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Section: Discussionmentioning

confidence: 98%