Neural net adaptive schemes for DC motor drives

The use of neural networks in a speed control loop applied to a DC motor is described. The proposed technique makes use of the learning capability of neural networks to implement an autoadaptive control structure. This capability allows the network to learn the dynamic behavior of the SCR-driven DC motor. This identification network is then used to train another network as the process controller, so that the process output follows the reference signal. An adaptation scheme for working conditions is also presented. Its performance is verified through testing of physical parameter variations and noise presence, showing the applicability of the system. >