Fuzzy and Taguchi based Fuzzy Optimization of Performance Criteria of the Process Control Systems

Fatih Kara, Arda Kucuk, Baris Simsek
  • Fatih Kara
    Karatekin University,
  • Baris Simsek
    Karatekin University,


This paper proposes a Taguchi based Fuzzy and Fuzzy PID application using MATLAB® version 2015a to assess and optimize of process control performance criteria of liquid level and flow rate control system. When the main effect graphs for the liquid level and flow rate control system are evaluated, it was seen that the change in the membership function is the most effective factor on the process control performance. It can be said that the Gaussian membership function provides the lowest mean and standard deviation in the offset value. Improvement rates for “overshoot”, “rise time”, “first peak time”, “%95 setting time, “%99 setting time”, “mean” and “the standard deviation of the offset values” are %50, %50, %55, %77, %64, %5, %63 for flow rate control system; %50, %49, %55, %43, %48, %4, %63 for liquid level control system in order. In comparison with the classical PID method, in the Fuzzy PID method, the improvement is calculated as 54% in the average of the offset value and 99% in the standard deviation.


Fuzzy PID, Fuzzy Logic, Taguchi Optimization, Process control, Design of Experiments, DoE

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Submitted: 2018-01-31 16:00:12
Published: 2018-06-29 14:38:55
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Zhang, R., J. Tao, and F. Gao, A New Approach of Takagi–Sugeno Fuzzy Modeling Using an Improved Genetic Algorithm Optimization for Oxygen Content in a Coke Furnace. Industrial & Engineering Chemistry Research, 2016. 55(22): p. 6465-6474.

Padula, F., et al., Optimized PID control of depth of hypnosis in anesthesia. Computer Methods and Programs in Biomedicine, 2017. 144(Supplement C): p. 21-35.

Taysom, B.S., C.D. Sorensen, and J.D. Hedengren, A comparison of model predictive control and PID temperature control in friction stir welding. Journal of Manufacturing Processes, 2017. 29(Supplement C): p. 232-241.

Trafczynski, M., et al., The influence of fouling on the dynamic behavior of PID-controlled heat exchangers. Applied Thermal Engineering, 2016. 109(Part A): p. 727-738.

Beschi, M., F. Padula, and A. Visioli, Fractional robust PID control of a solar furnace. Control Engineering Practice, 2016. 56(Supplement C): p. 190-199.

Thenozhi, S. and W. Yu, Stability analysis of active vibration control of building structures using PD/PID control. Engineering Structures, 2014. 81(Supplement C): p. 208-218.

Zhang, J., Design of a new PID controller using predictive functional control optimization for chamber pressure in a coke furnace. ISA Transactions, 2017. 67(Supplement C): p. 208-214.

Zhang, R., et al., Predictive control optimization based PID control for temperature in an industrial surfactant reactor. Chemometrics and Intelligent Laboratory Systems, 2014. 135(Supplement C): p. 48-62.

Zarei, M., et al., Robust PID control of power in lead cooled fast reactors: A direct synthesis framework. Annals of Nuclear Energy, 2017. 102(Supplement C): p. 200-209.

Lamba, R., S.K. Singla, and S. Sondhi, Fractional order PID controller for power control in perturbed pressurized heavy water reactor. Nuclear Engineering and Design, 2017. 323(Supplement C): p. 84-94.

Wang, Y., Q. Jin, and R. Zhang, Improved fuzzy PID controller design using predictive functional control structure. ISA Transactions, 2017. 71(Part 2): p. 354-363.

Rakhtala, S.M. and E. Shafiee Roudbari, Fuzzy PID control of a stand-alone system based on PEM fuel cell. International Journal of Electrical Power & Energy Systems, 2016. 78(Supplement C): p. 576-590.

Liu, F. and H. Wang, Fuzzy PID controller for optoelectronic stabilization platform with two-axis and two-frame. Optik - International Journal for Light and Electron Optics, 2017. 140(Supplement C): p. 158-164.

Kumar, A. and V. Kumar, Evolving an interval type-2 fuzzy PID controller for the redundant robotic manipulator. Expert Systems with Applications, 2017. 73(Supplement C): p. 161-177.

Moradi, H., H. Setayesh, and A. Alasty, PID-Fuzzy control of air handling units in the presence of uncertainty. International Journal of Thermal Sciences, 2016. 109(Supplement C): p. 123-135.

Kosari, A., H. Jahanshahi, and S.A. Razavi, An optimal fuzzy PID control approach for docking maneuver of two spacecraft: Orientational motion. Engineering Science and Technology, an International Journal, 2017. 20(1): p. 293-309.

Dettori, S., et al., A Fuzzy Logic-based Tuning Approach of PID Control for Steam Turbines for Solar Applications. Energy Procedia, 2017. 105(Supplement C): p. 480-485.

Mahmoodabadi, M.J. and H. Jahanshahi, Multi-objective optimized fuzzy-PID controllers for fourth order nonlinear systems. Engineering Science and Technology, an International Journal, 2016. 19(2): p. 1084-1098.

Dequan, S., et al., Application of Expert Fuzzy PID Method for Temperature Control of Heating Furnace. Procedia Engineering, 2012. 29(Supplement C): p. 257-261.

J.H. Al Gizi, A., et al., Integrated PLC-fuzzy PID Simulink implemented AVR system. International Journal of Electrical Power & Energy Systems, 2015. 69(Supplement C): p. 313-326.

Kudinov, Y.I., et al., Optimization of Fuzzy PID Controller's Parameters. Procedia Computer Science, 2017. 103(Supplement C): p. 618-622.

Şimşek, B., et al., PID Control Performance Improvement for a Liquid Level System using Parameter Design. 2016.

Ahmad, A., et al., Liquid level control by using fuzzy logic controller. 2012.

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