Neural network approach eliminates tracking errors in position control systems, suggesting a stable implementation.
In this article, a neural-network based repetitive control system that eliminates tracking errors due to periodic reference command and disturbance in a position control system is presented. A functional-link neural network is used to carry out the repetitive control law. The proposed repetitive control system is designed to be easily implemented onto any system without destabilizing the existing stable servo system. The performance of the proposed scheme is evaluated by applying it to a simulated disk drive head-positioning servo systems.
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Wang et al. (1995) studied this question.
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