An experimental verification of a model reference and sensitivity model-based self-learning fuzzy logic controller applied to a nonlinear servosystem

In this paper, an experimental verification of a self-learning fuzzy logic controller (SLFLC) is described. The SLFLC contains a learning algorithm that utilizes a second-order referent model and a sensitivity model. The effectiveness of the proposed controller has been tested in the position control loop of a chopper-fed DC servo system affected by fairly high static friction and by a gravitation dependent shaft load. The experimental results have proved that the SLFLC provides desired closed-loop behavior and eliminates a steady-state position error.