Skip to Main Content
This paper presented a recurrent radial basis function network-based (RBFN-based) fuzzy neural network (FNN) to control the position of x-y table mover to track periodic reference trajectories. The two-axis motion control system was composed of two permanent-magnet linear synchronous motors (PMLSM). The proposed recurrent RBFN-based FNN combined the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN) and RBFN. The structure-learning and parameter-learning phases were performed concurrently. The structure learning was based on the partition of input space, and the parameter learning was based on the supervised gradient descent method using a delta adaptation law. The simulation results show that the designed control system of XY table has strong robustness and high contour accuracy.
Control Conference (CCC), 2010 29th Chinese
Date of Conference: 29-31 July 2010