Type of Individual Behaviour
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
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
References: [l] E. G. Collins, Jr., Y. Zhao and R. Millett, “A Genetic Search Approach to Unfalsified PI Control Design for a Weigh Belt Feeder,”International Journal of Adaptive Control and Signal Processing, Vol. 15, pp. 519-534, 2001 [8] Z. W. Woo, H. Y . Chung and J. J. Lin, “A PID Type Fuzzy Controller with Self-tuning Scaling Factors,” Fuzzy Sets and Systems, Vol. 115, pp. 321-326, 2000. R. K. Mudi and N. K. Pal, “A Self-tuning Fuzzy PI Controller,’’ Fuzzy Sets and Systems, Vol. 115, pp. 327[3] 338, 2000. [lo] J. Fonseca, J. L. Afonso, J. S. Martins and C. Couto, “Fuzzy Logic Speed Control of an Induction Motor,” Microprocessors and Microsystems, Vol. 22, pp. 523-534, 1999. Z. Z. Zhao, M. Tomizuka and S. Isaka,“Fuzzy Gain Scheduling of PID Controller,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 5, pp. 1392[4] 1398, 1993. [ll] J. Xu, C. C. Hang and C. Liu, “Parallel Structure and Tuning of a Fuzzy PID Controller,’’ Automatica, Vol. 36, pp. 673-684, 2000. [12] R. Bandyopadhyay and D. Patranabis , “A Fuzzy Logic Based PI Controller,” ISA Transactions, Vol. 37, pp. 227-235, 1998. [13] S. Z. He, S. Tan and F. L. Xu, “Fuzzy Self-tuning of PID Controller,” Fuzzy Sets and Systems, Vol. 56, pp. 37-46, 1993.