Please use this identifier to cite or link to this item:
|Title:||Neural network control system with parallel adaptive enhancements applicable to nonlinear servomechanisms||Authors:||Lee, T.H.
Ang Jr., M.H.
|Issue Date:||Jun-1994||Citation:||Lee, T.H., Tan, W.K., Ang Jr., M.H. (1994-06). Neural network control system with parallel adaptive enhancements applicable to nonlinear servomechanisms. IEEE Transactions on Industrial Electronics 41 (3) : 269-277. ScholarBank@NUS Repository. https://doi.org/10.1109/41.293896||Abstract:||In this paper, we present a technique for using an additional parallel neural network to provide adaptive enhancements to a basic fixed neural network-based nonlinear control system. This proposed parallel adaptive neural network control system is applicable to nonlinear dynamical systems of the type commonly encountered in many practical position control servomechanisms. Properties of the controller are discussed, and it is shown that if Gaussian radial basis function networks are used for the additional parallel neural network, uniformly stable adaptation is assured and the approximation error converges to zero asymptotically. In the paper, the effectiveness of the proposed parallel adaptive neural network control system is demonstrated in real-time implementation experiments for position control in a servomechanism with asymmetrical loading and changes in the load.||Source Title:||IEEE Transactions on Industrial Electronics||URI:||http://scholarbank.nus.edu.sg/handle/10635/80784||ISSN:||02780046||DOI:||10.1109/41.293896|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Aug 19, 2019
WEB OF SCIENCETM
checked on Jul 10, 2019
checked on Aug 17, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.