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Proceedings of the American Control Conference Anchorage, AK May 8-10,2002

Fuzzy PI Control of an Industrial Weigh Belt Feeder
Yanan Zhao and Emmanuel G. Collins, Jr.
Department of Mechanical Engineering, Florida A&M University - Florida State University, Tallahassee, FL 32310 yzhao@eng.fsu.edu,ecollins@eng.fsu.edu I

Abstract
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 PIlike 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 on-line 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 on-line based on fuzzy inference rules. Experimental results show the effectiveness of the proposed fuzzy logic controllers.

1. Introduction
An industrial weigh belt feeder (see Figure 1) is designed to transport solid materials into a manufacturing process at a constant feedrate, usually in kilograms or pounds per second [l] . . .~

~ . . _ . ...

Figure 1: The Merrick Weigh Belt Feeder
The dynamics of the weigh belt feeder are dominated by the motor. To protect the motor, the control signal is restricted to lie in the interval [0,10] volts. The motor also has significant friction. In addition, the sensors exhibit significant quantization noise. Hence, the weigh belt ‘This research was supported in part by the National Science Foundation under Grant CMS-9802197.

feeder exhibits nonlinear behavior [l]. To design a controller in the presence of friction of the plant, most friction compensation methods have generally involved selecting a friction model and then using part of the control input to cancel the effects of the nonlinearity. This kind of model- , based compensation has limitations since the characteristics of friction are difficult to predict and analyze due to , their complexity and dependence on parameters that vary during the process [9]. However, fuzzy logic control has been found particularly useful for controller design when the plant model is unknown or difficult to develop. It does not need an exact process model and has been shown to be robust with respect to disturbances, large uncertainty and variations in the process behavior [lo]. Fuzzy PID control has been widely studied and various ’ types of fuzzy PID (including PI and PD) controllers have been proposed. They can be classified into two major categories according to their construction [ll]. One category of “fuzzy PID controllers” consists of typical fuzzy logic controllers(FLCs) constructed as a set of heuristic control rules. The control signal or the incremental change of control signal is built as a nonlinear function of the error, change of error and acceleration error, where the nonlinear function includes fuzzy reasoning. , There are no explicit proportional, integral and derivative gains; instead the control signal is directly deduced from the knowledge base and the fuzzy inference. They are referred to as fuzzy PID-like controllers because their structure is analogous to that of the conventional PID controller. Most of the research on fuzzy logic control design belongs to this category [3, i’]. To be consistent with the nomenclature of [2], and t o distinguish from the 2nd category of fuzzy PID controllers (given below), in the following we will call FLCs in this category PID-like (PI-like, PD-lake) FL Cs. Another category of “fuzzy PID controllers” is composed of the conventional PID control system in conjunction with a set of fuzzy rules (knowledge base) and a fuzzy reasoning mechanism to tune the...
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