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A Fuzzy Knowledge Based Controller To Tune Pid Parameters

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1999 Annual Conference


Charlotte, North Carolina

Publication Date

June 20, 1999

Start Date

June 20, 1999

End Date

June 23, 1999



Page Count


Page Numbers

4.15.1 - 4.15.12

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Paper Authors

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Kenny Fotouhi

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Ali Eydgahi

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NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Session 2520

A Fuzzy Knowledge-Based Controller to Tune PID Parameters Ali Eydgahi, Mohammad Fotouhi

Engineering and Aviation Sciences Department / Technology Department University of Maryland Eastern Shore Princess Anne, MD 21853


In this paper integration of fuzzy knowledge-based control with the hard control technique is purposed. The fuzzy knowledge-based is implemented as a set of fuzzy rules with an inference mechanism to tune the PID controller in the system. A software is developed in which users can define the rule base. The program generates the fuzzy decision table based on all inputted information and in a descriptive fashion. Then, the decision table is used to modify the parameters required by the fuzzy tuner in on-line operations. A simulation environment based on MATLAB® and SIMULINK® is used for demonstration purpose. Two control systems with the same structures are constructed. One system is using a fuzzy knowledge-based tuner and the other one is using a conventional PID control loop. Simulation results show improvement on system response of fuzzy knowledge-based control structure.

I. Introduction

There are many reasons for the practical deficiencies of control systems. Unconsidered conditions or any changes in environment may result in undesired outputs in a control system. For example, if the model of the process is inaccurate, model-based control can provide unsatisfactory results. Even with an accurate model, approximations are applied if parameter values are partially known or vague. Algorithmic control based on such incomplete information will not usually give satisfactory results. Often, the environment with which the process interacts may not be completely predictable and it is normally not possible for a hard control to respond accurately to a condition that it did not anticipate 1.

However, use of intelligent control may improve the performance and efficiency of such systems 1-5. Intelligence can be embedded into a controller in the form of a knowledge base typically expressed as a set of rules and an associated inference mechanism.

Human knowledge and experience are gained not through on-line generation of control signals manually, but through performing parameter adjustments and tuning operations. A knowledge-based controller may be more effective in a monitoring and tuning capacity than in direct generating control signal capacity since human experts are quite effective in tuning operations.

Fotouhi, K., & Eydgahi, A. (1999, June), A Fuzzy Knowledge Based Controller To Tune Pid Parameters Paper presented at 1999 Annual Conference, Charlotte, North Carolina.

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