Fuzzy-based nonlinear PID controller and its application to CSTR

Gun-Baek So; Gang-Gyoo Jin

Korean Journal of Chemical Engineering, Vol.35, No.4, 819-825, April, 2018

DOI10.1007/s11814-017-0329-1 Export Citation

Fuzzy-based nonlinear PID controller and its application to CSTR

Gun-Baek So, and Gang-Gyoo Jin^{1, †}

Department of Convergence Study on the OST, Korea Maritime and Ocean University, 727 Taejongro, Yeongdo-gu, Busan 49112, Korea
¹Division of IT, Korea Maritime and Ocean University, 727 Taejongro, Yeongdo-gu, Busan 49112, Korea

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This study presents a new design method for a nonlinear variable-gain PID controller, the gains of which are described by a set of fuzzy rules. User-defined parameters are tuned using a genetic algorithm by minimizing the integral of absolute error and the weighted control input deviation index. It was observed in the experimental results on a continuous stirred tank reactor (CSTR) that the proposed controller provided performances: overshoot Mp≤1.25%, 2% settling time ts≤1.71 s and IAE≤1.26 for set-point tracking, perturbance peak Mpeak≤0.05%, 2% recovery time trcy≤ 3.97 s and IAE≤0.10 for disturbance rejection, and Mpeak≤0.04%, trcy≤2.74 s and IAE≤0.04 for parameter changes. Comparison with those of two other methods revealed that the proposed controller not only led to less overshoot and shorter settling time for set-point tracking and less perturbance peak and shorter recovery time for disturbance rejection, but also showed less sensitivity to parameter changes.

Keywords:PID Controller;Nonlinear Gains;Tagaki-Sugeno Fuzzy Rule;Continuous Stirred Tank Reactor (CSTR)

[References]

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[1] Ray WH, Advanced Process Control, McGraw-Hill (1981).

[2] Bequette BW, Process Control: Modeling, Design and Simulation, Prentice Hall (2002).

[3] Smith CL, Practical Process Control: Tuning and Troubleshooting, Wiley (2009).

[4] Chen CT, Peng ST, Int. J. Process Control, 9, 151 (1999)

[5] Chang WD, Simulation Modelling Practice Theory, 31, 1 (2013)

[6] Dagher KA, Al-Araji AS, Al-Khwarizmi Eng. J., 9, 46 (2013)

[7] Sabir MM, Khan JA, Advances in Artificial Neural Systems, 2014 (2014).

[8] Pati AB, Chile RH, Wahane VR, Michael Faraday IET Int. Summit 2015: MFIIS-2015, 36 (2015).

[9] Zhuo Y, Zhu Q, 2015 7th Int. Conf. on Modelling, Identification and Control (2015).

[10] Rasul T, Pathak M, 1st IEEE Int. Conf. on Power Electronics, Intelligent Control and Energy Systems, 1 (2016).

[11] Saini P, Gaba SK, Rajput N, Aggarwal A, 2016 3rd Int. Conf. on Computing for Sustainable Global Development, 2778 (2016).

[12] Tamboli DA, Chile RH, 2016 Int. Conf. on Advances in Computing, Communications and Informatics, 1410 (2016).

[13] Seraji H, J. Robotics System, 15, 161 (1998)

[14] Su YX, Sun D, Duan BY, Mechatronics, 15, 1005 (2005)

[15] Li C, Wang S, Yue Z, Yu J, Wang H, Proc. of the 27th Chinese Conf. Control, 377 (2008).

[16] Zhang H, Hu B, Proc. Int. Conf. Future Electrical Power and Energy System, 202 (2012).

[17] Korkmaz M, Aydogdu O, Dogan H, Proc. Int. Symp. Innovations in Intelligent Systems and Applications, 1 (2012).

[18] Chen J, Lu B, Fan F, Zhu S, Jianxin W, Appl. Mech. Mater., 141, 157 (2012)

[19] Lucas R, Oliveira RM, Nascimento CB, Kaster MS, 2015 IEEE 24th Int. Symp. on Industrial Electronics (ISIE), 1022 (2015).

[20] Mishra P, Kumar V, Rana KPS, ISA Transactions, 58, 434 (2015)

[21] Astrom KJ, Hagglund T, PID Controllers: Theory, Design and Tuning, ISA (1995).

[22] O’Dwyer A, Handbook of PI and PID Controller Tuning Rules 3rd Ed., Imperial College Press (2009).

[23] Takagi T, Sugeno M, Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst. Man Cybern.,15, 116 (1985).

[24] Isermann R, Digital Control Systems, Springer-Verlag, N. Y. (1977).

[25] Jin GG, Genetic Algorithms and Their Application, Kyo-Woo Publishing Co. (2000).

[26] Watanabe K, Hashem MMA, Evolutionary Computations:New Algorithms and their Applications to Evolutionary Robots, Springer (2004).