Please use this identifier to cite or link to this item: https://doi.org/10.1109/41.293896
Title: Neural network control system with parallel adaptive enhancements applicable to nonlinear servomechanisms
Authors: Lee, T.H. 
Tan, W.K. 
Ang Jr., M.H. 
Issue Date: Jun-1994
Source: 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/10635/58522
ISSN: 02780046
DOI: 10.1109/41.293896
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