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 controller is implemented in a DSP-based motion board. The validity and robustness of the controller are verified through experimental results.
Highlights
The XY tables have taken an important role in manufacturing systems
This paper proposed a robust adaptive neural network control for an XY table
The proposed controller is implemented in a digital signal processor (DSP)-based motion board
Summary
The XY table is a ball-screw driven mechanism actuated by two AC servo drives Such mechanism often exist many kinds of disturbances, nonlinear friction and uncertainties that limit the tracking performance of controlled system. XY table often utilizes two AC servo motors and couples their output shafts to mechanical translators such as gears or bears to perform linear motions Such mechanical systems have been extensively used in the industrial market due to low cost, high torque density, little torque ripple and power saving. The controller is able to guarantee the convergence and stability of the servo system despite the existence of model uncertainties and external disturbances In this controller, a 2-layer neural network with radial basis function is used to approximate the nonlinear factors of the AC servo motor.
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