Please use this identifier to cite or link to this item: https://doi.org/10.1109/IECON.2003.1280689
Title: PID Control Incorporating RBF-Neural Network for Servo Mechanical Systems
Authors: Lee, T.H. 
Huang, S.N. 
Tang, K.Z. 
Tan, K.K. 
Al Mamun, A. 
Issue Date: 2003
Source: Lee, T.H.,Huang, S.N.,Tang, K.Z.,Tan, K.K.,Al Mamun, A. (2003). PID Control Incorporating RBF-Neural Network for Servo Mechanical Systems. IECON Proceedings (Industrial Electronics Conference) 3 : 2789-2793. ScholarBank@NUS Repository. https://doi.org/10.1109/IECON.2003.1280689
Abstract: This paper presents a combined control scheme, comprising of the well-known PID controller augmented with a Radial Basis Function Neural Network (RBFNN) for the control of servo mechanical systems. A second-order linear dominant model is considered with an unmodeled part of dynamics that is possibly nonlinear and time-varying. The PID part of the controller is designed to stabilize the dominant model. The RBFNN is used to compensate for the deviation of the system characteristics from the dominant linear model to achieve performance enhancement. The advantage of this combined control scheme is that it can cope with strong nonlinearities in the system while still using the PID control structure which is well-known to many control engineers.
Source Title: IECON Proceedings (Industrial Electronics Conference)
URI: http://scholarbank.nus.edu.sg/handle/10635/71444
DOI: 10.1109/IECON.2003.1280689
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