A neuro-fuzzy networks (NFN) saturation compensation scheme for DC motor systems is presented. The scheme that leads to stability, command following, and disturbance rejection is rigorously proved. The on-line weights tuning law, the overall closed loop performance, and the boundedness of the NFN weights are derived and guaranteed based on the Lyapunov approach. The actuator saturation is assumed to be unknown and the saturation compensator is inserted into a feedforward path. The simulation and experimental results show that the proposed scheme can effectively compensate for the saturation nonlinearity in the presence of system uncertainty. © 2009 Elsevier Ltd. All rights reserved.
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