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

In this paper, the design, simulation and experimental verification of a self-learning fuzzy logic controller (SLFLC) suitable for the control of nonlinear servo systems are described. 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 in the position control loops of two chopper-fed DC servo systems, first by simulation in the presence of a backlash nonlinearity, then by experiment in the presence of a gravity-dependent shaft load and fairly high static friction. The simulation and experimental results have proved that the SLFLC provides desired closed loop behavior and eliminates a steady-state position error