A model reference & sensitivity model-based self-learning fuzzy logic controller as a solution for control of nonlinear servo systems

In this paper, design, simulation and experimental verification of a self-l earning fuzzy logic controller (SLFLC) suitable for control of nonlinear se rvo systems are described. The SLFLC contains a learning algorithm that uti lizes a second-order reference model and a sensitivity model related to the fuzzy controller parameters. The effectiveness of the proposed controller has been tested in the position control loops of two chopper-fed de servo s ystems, first by simulation in the presence of a backlash nonlinearity, the n by experiment in the presence of a gravity-dependent shaft load and fairl y high static friction. The simulation and experimental results have proved that the SLFLC provides desired closed-loop behavior and eliminates a stea dy-state position error.