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Proceedings in the American Control Conference Anchorage, AK Might 8-10, 2002

Fuzzy PI Control of a great Industrial Weigh Belt Feeder

Yanan Zhao and Emmanuel G. Collins, Jr.

Department of Mechanised Engineering, California A& M University - Florida Point out University, Tallahassee, FL 32310 [email protected] fsu. edu, [email protected] fsu. edu I


This newspaper proposes and experimentally illustrates two types of fuzzy reasoning controllers to get an commercial weigh seatbelt feeder. The first type is a PI-like fuzzy reasoning controller (FLC). A gain planned PI-like FLC and a self-tuning PI-like FLC will be presented. For the gain scheduled PIlike FLC the outcome scaling factor of the control is gain scheduled with the change of setpoint. For the self-tuning PI-like FLC, the output running factor of the controller is modified across the internet by a great updating factor whose benefit is determined by a rule-base while using error and alter of problem of the controlled variable while the advices. A unclear PI control mechanism is also provided, where the proportional and integral gains happen to be tuned on the web based on unclear inference rules. Experimental benefits show the performance of the recommended fuzzy reasoning controllers.

1 ) Introduction

A great industrial weigh belt feeder (see Number 1) was created to transport sturdy materials to a manufacturing process at a continuing feedrate, usually in kilos or pounds per second [l]#@@#@!!... ~

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Figure one particular: The Merrick Weigh Belt Feeder

The dynamics in the weigh seatbelt feeder will be dominated by the motor. To guard the engine, the control signal is fixed to lay in the span [0, 10] volts. The motor also offers significant scrubbing. In addition , the sensors display significant quantization noise. Therefore, the consider belt ‘This research was supported in part by the Countrywide Science Groundwork under Grant CMS-9802197.

feeder exhibits nonlinear behavior [l]. To design a control in the presence of scrubbing of the grow, most scrubbing compensation methods have generally involved deciding on a friction unit and then employing part of the control input to cancel the consequences of the non-linearity. This kind of model-, based compensation has limits since the features of rubbing are difficult to predict and analyze due to, their complexness and dependence on parameters that vary during the process [9]. However , fuzzy logic control has been discovered particularly useful for controller design when the flower model is usually unknown or perhaps difficult to develop. It does not will need an exact process model and has been shown to get robust regarding disturbances, significant uncertainty and variations along the way behavior [lo]. Unclear PID control has been broadly studied and various ' types of fuzzy PID (including PROFESSIONAL INDEMNITY and PD) controllers have been completely proposed. They might be classified into two major categories according to their structure [ll]. One group of " fluffy PID controllers” consists of typical fuzzy common sense controllers(FLCs) built as a pair of heuristic control rules. The control signal or the gradual change of control signal is built as being a nonlinear function of the problem, change of error and acceleration mistake, where the non-linear function includes fuzzy thinking., There are simply no explicit proportionate, integral and derivative gains; instead the control transmission is directly deduced from the knowledge base and the unclear inference. They may be referred to as fluffy PID-like remotes because all their structure can be analogous to this of the conventional PID control. Most of the analysis on fluffy logic control design is this category [3, i']. To be like nomenclature of [2], and to o distinguish from the second category of unclear PID controllers (given below), in the subsequent we will certainly call FLCs in this category PID-like (PI-like, PD-lake) FLORIDA Cs. One other category of " fuzzy PID controllers” is composed of the conventional PID control program in conjunction with some fuzzy rules (knowledge base) and a fuzzy reasoning mechanism to tune the...

References: [l] E. G. Collins, Junior., Y. Zhao and 3rd there’s r. Millett, " A Genetic Search Approach to Unfalsified PI Control Design for a Ponder Belt Feeder, ”International Log of Adaptive Control and Signal Digesting, Vol. 12-15, pp. 519-534,


[8] Z. Watts. Woo, H. Y. Chung and T. J. Lin, " A PID Type Fuzzy Control mechanism with Self-tuning Scaling Elements, ” Unclear Sets and Systems, Vol. 115, pp. 321-326, 2150.

R. E. Mudi and N. E. Pal, " A Self-tuning Fuzzy PI Controller, '' Fuzzy Models and Systems, Vol. 115, pp. 327[3] 338, 2k.

[lo] J. Fonseca, T. L. Afonso, J. H. Martins and C. Couto, " Unclear Logic Acceleration Control of a great Induction Motor, ” Microprocessors and Microsystems, Vol. twenty two, pp. 523-534,


Z .. Z. Zhao, M. Tomizuka and T. Isaka, " Fuzzy Gain Scheduling of PID Controller, ” IEEE Transactions on Systems, Guy, and Cybernetics, Vol. twenty-three, No . a few, pp. 1392[4] 1398, 1993.

[ll] L. Xu, C. C. Hang and C. Liu, " Parallel Structure and Fine tuning of a Unclear PID Controller, '' Automatica, Vol. 36, pp. 673-684, 2000.

[12] R. Bandyopadhyay and M. Patranabis, " A Fuzzy Logic Structured PI Control mechanism, ” ISA Transactions, Vol. 37, pp. 227-235, 1998. [13] S. Z. This individual, S. Suntan and Farreneheit. L. Xu, " Fuzzy Self-tuning of PID Control mechanism, ” Unclear Sets and Systems, Volume. 56, pp. 37-46, 1993.

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