Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0005-1098(01)00192-3
Title: Adaptive motion control using neural network approximations
Authors: Huang, S.N. 
Tan, K.K. 
Lee, T.H. 
Keywords: Adaptive control
Compensation
Friction
Neural networks
Uncertainty
Issue Date: Feb-2002
Citation: Huang, S.N., Tan, K.K., Lee, T.H. (2002-02). Adaptive motion control using neural network approximations. Automatica 38 (2) : 227-233. ScholarBank@NUS Repository. https://doi.org/10.1016/S0005-1098(01)00192-3
Abstract: In this paper, we present a new adaptive technique for tracking control of mechanical systems in the presence of friction and periodic disturbances. Radial Basis Functions (RBFs) are used to compensate for the effects of nonlinearly occurring parameters in the friction and periodic disturbance model. Theoretical analysis, such as stability and transient performance, is provided. Furthermore, the performance of the adaptive RBF controller and its non-adaptive counterpart are compared. © 2001 Elsevier Science Ltd. All rights reserved.
Source Title: Automatica
URI: http://scholarbank.nus.edu.sg/handle/10635/54905
ISSN: 00051098
DOI: 10.1016/S0005-1098(01)00192-3
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