Abstract
Dynamic optimization control algorithm is put forward for X-Y position table. Firstly, dynamics model of X-Y position table is established, then, RBF neural network with good learning ability is used to approach non-linear system. Optimization algorithm of network weights is designed to speed up the learning speed and the adjustment velocity. The control method is more effective to improve the control precision and has a good application value.
Publisher
Trans Tech Publications, Ltd.
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