An industrial weigh belt feeder is used to transport solid materials iuto a manufacturing process at a constant feedrate. It exhibits nonlinear behavior because of motor friction, saturation, and quantization noise in the sensors, which makes standard autotuning methods difficult to implement. This paper proposes and experimentally demonstrates two types of fuzzy logic controllers for an industrial weigh belt feeder. The first type is a Pi-like fuzzy logic controller (FLC). A gain scheduled Pi-like FLC and a self-tuning Pi-like FLC are presented. For the gain scheduled Pi-like FLC the output scaling factor of the controller is gain scheduled with the change of setpoint. For the self-tuning Pi-like FLC, the output scaling factor of the controller is modified online by an updating factor whose value is determined by a rule base with the error and change of error of the controlled variable as the inputs. A fuzzy PI controller is also presented, where the proportional and integral gains are tuned online based on fuzzy inference rules. Experimental results show the effectiveness of the proposed fuzzy logic controllers. A performance comparison of the three controllers is also given.