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|Title:||Adaptive MNN control for a class of non-affine NARMAX systems with disturbances||Authors:||Ge, S.S.
|Keywords:||Implicit function theorem
Multi-layer neural networks
Non-affine nonlinear system
|Issue Date:||Sep-2004||Citation:||Ge, S.S., Zhang, J., Lee, T.H. (2004-09). Adaptive MNN control for a class of non-affine NARMAX systems with disturbances. Systems and Control Letters 53 (1) : 1-12. ScholarBank@NUS Repository. https://doi.org/10.1016/j.sysconle.2004.02.016||Abstract:||In this paper, adaptive multi-layer neural network (MNN) control is developed for a class of discrete-time non-affine nonlinear systems in nonlinear auto regressive moving average with eXogenous inputs (NARMAX) model. By using implicit function theorem, the existence of the implicit desired feedback control (IDFC) is proved. MNNs are used as the emulator of the desired feedback control. Projection algorithms are used to guarantee the boundedness of the neural network (NN) weights, which removes the need of persistent exciting (PE) condition for parameter convergence. Simulation results show the effectiveness of the proposed control scheme. © 2004 Elsevier B.V. All rights reserved.||Source Title:||Systems and Control Letters||URI:||http://scholarbank.nus.edu.sg/handle/10635/54904||ISSN:||01676911||DOI:||10.1016/j.sysconle.2004.02.016|
|Appears in Collections:||Staff Publications|
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