Sensitivity-based self-learning fuzzy logic control for a servo system

Describes an experimental verification of a self-learning fuzzy logic controller (SLFLC). The SLFLC contains a learning algorithm that utilizes a second-order reference model and a sensitivity model related to the fuzzy controller parameters. The effectiveness of the proposed controller has been tested by experiment in the position control loop of a chopper fed DC servo system in the presence of a gravity-dependent shaft load and fairly high static friction. The experimental results prove that the SLFLC provides closed-loop behavior as desired and eliminates a steady-state position error.