Adaptation learning control scheme for a high performance permanent magnet stepper motor using online random training of neural networks

This paper addresses the problem of controlling the speed of a permanent magnet stepper motor assumed to operate in a high-performance drives environment. An artificial neural network control scheme which uses continual on-line random training (with no off-line training) to simultaneously identify and adaptively control the speed of the stepper motor is proposed. The control scheme utilizes two three-layer feed-forward artificial neural networks: (1) a tracker identification neural network which captures the nonlinear dynamics of the motor over any arbitrary time interval in its range of operation and (2) a controller neural network to provide the necessary control actions to achieve trajectory tracking of the motor speed. The inputs to the controller neural network are not constructed from the actual motor/load dynamics, but as a feedback signal, from the estimated state variables of the motor supplied by the neural identifier and the reference trajectory to be tracked by the actual speed. This paper also makes use of a very realistic and practical scheme to estimate and adaptively learn the noise content in the speed-load torque characteristic of the motor. Simulations reveal that the neuro-controller adapts and generalizes its learning rate to a wide variety of loads, in addition to providing the necessary abstraction when measurements are contaminated with noise.