NEURAL-NETWORK-BASED NEAR-TIME-OPTIMAL POSITION CONTROL METHOD FOR DCMOTOR SERVOSYSTEMS

The paper considers the development and implementation of a near-time- optimal neural-network-based position control method for DC motor serv osystems. To bypass the difficulties caused by system constraints and modelling uncertainties, the paper uses classification neural networks to learn the time-optimal control law from experimentally generated n ear-time-optimal trajectories. In addition, by using regression neural networks to learn the relationship between control object displacemen t and the armature voltage pulse-width, a variable-pulsewidth control strategy is developed to achieve accurate positioning. Experimental re sults are given to demonstrate the effectiveness of the proposed appro ach.