A cross-coupled proportional-integral-derivative neural network (PIDNN) control is proposed in this study for the synchronous control of a dual linear motors servo system which is installed in a gantry position stage. First, the dynamics of the field-oriented control PMLSM servo drive with a lumped uncertainty, which contains parameter variations, external disturbance and friction force, is introduced. Then, to achieve accurate trajectory tracking performance with robustness, an intelligent control approach using PIDNN is proposed for the field-oriented control PMLSM servo drive system. In the proposed approach, the on-line learning algorithms of the PIDNN are derived using back-propagation (BP) method to guarantee the convergence of the network. Finally, some experimental results are illustrated to depict the validity of the proposed control approach.
Simultaneous Identification of Multiple Mechanical Parameters in a Servo Drive System Using Only One Speed
