A continually online-trained neural network controller for brushless DC motor drives

In this paper, a high-performance controller with simultaneous online identification and control is designed for brushless DC motor drives. The dynamics of the motor/load are modeled "online" and controlled using two different neural network based identification and control schemes, as the system is in operation. In the first scheme, an attempt is made to control the rotor angular speed, utilizing a single three-hidden-layer network. The second scheme attempts to control the stator currents, using a predetermined control law as a function of the estimated states. This scheme incorporates three multilayered feedforward neural networks that the online trained, using the Levenburg-Marquadt training algorithm. The control of the direct and quadrature components of the stator current successfully tracked a wide variety of trajectories after relatively short online training periods. The control strategy adapts to the uncertainties of the motor/load dynamics and, in addition, learns their inherent nonlinearities. Simulation results illustrated that a neurocontroller used in conjunction with adaptive control schemes can result in a flexible control device which may be utilized in a wide range of environments.