Neural adaptive tracking control of a DC motor

This paper presents a neural network-based adaptive control strategy for speed or position tracking of a DC motor with unknown system nonlinearities. In the proposed scheme, we successfully integrate some existing techniques, such as the input-output linearization technique used to cancel the nonlinearities, and neural networks used to implement the linearizing control law. The network approximation errors are compensated by using the sliding mode control scheme. Moreover, the neural network parameters are updated according to the Lyapunov approach. It is shown that, through the proposed control scheme, the rotor speed or position of a DC motor can follow any arbitrarily selected trajectories under variable load torque. Numerical simulation results are provided to confirm the performance and effectiveness of the proposed control approach. (C) 1999 Published by Elsevier Science Inc. hll rights reserved.