2013
DOI: 10.4236/ica.2013.43034
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Robust Adaptive Neural Network Control for XY Table

Abstract:

This paper proposed a robust adaptive neural network control for an XY table. The XY table composes of two AC servo drives controlled independently. The neural network with radial basis function is employed for velocity and position tracking control of AC servo drives to improve the system’s dynamic performance and precision. A robust adaptive term is applied to overcome the external disturbances. The stability and the convergence of the system are proved by Lyapunov theory. The proposed co… Show more

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Cited by 9 publications
(3 citation statements)
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“…This algorithm is to compensate for the disturbances. Lyapunov theory improved system stability [10]. Patrick et al presented the real-time controls of Non-Minimum Phase (NMP) XY Table system using trajectory Zero Phase Error Tracking Controller (ZPETC).…”
Section: Related Existing Workmentioning
confidence: 99%
“…This algorithm is to compensate for the disturbances. Lyapunov theory improved system stability [10]. Patrick et al presented the real-time controls of Non-Minimum Phase (NMP) XY Table system using trajectory Zero Phase Error Tracking Controller (ZPETC).…”
Section: Related Existing Workmentioning
confidence: 99%
“…While the shortcoming is that the parameters of friction are estimated offline. Moreover, RBFNN is commonly used to estimate the disturbance for its approximation ability [10,11]. In reference [11], an adaptive RBFNN was proposed to generate the feedback control parameters online, while the extended state observer is used to compensate for composite disturbances.…”
Section: Introductionmentioning
confidence: 99%
“…Thus, the active disturbance rejection control technique is widely used in ISPs to compensate for disturbance [ 15 , 16 ], which eliminates disturbance on a large scale. Due to the compensation of the disturbance, the robustness and tracking precision of ISP are enhanced.…”
Section: Introductionmentioning
confidence: 99%