High performance drive of DC brushless motors using neural network

In this paper, a multi-layer neural network (NN) architecture is proposed for the identification and control of DC brushless motors operating in a high performance drives environment. The NN in the proposed structure performs two functions. The first is to identify the nonlinear system dynamics at all times. Hence, detailed and elaborate models for the DC brushless machines are not needed. Furthermore, unknown nonlinear dynamics that are difficult to model such as load disturbances, system noise and parameter variations can be recognized by the trained neural network. The second function of the NN is to control the motor voltage so that the speed and position are made to follow pre-selected tracks (trajectories) at all times. The control action emulated by the NN is based on the indirect model reference adaptive control. A hardware laboratory setup is utilized to test and evaluate the proposed neural network structure. The paper shows, based on the laboratory test results, that the proposed neural network structure performance was good: the tracking accuracy was very high and the system robustness was quite evident even in the presence of random and severe disturbances