Comparison Performance of Robustness Test using Intelligent Fuzzy based Controller for Simulation Study


  • Zakiah Mohd Yusoff
  • Nur Dalila Khirul Ashar
  • Zuraida Muhammad
  • Noor Fadzilah Razali
  • Shakira Azeehan Azli
  • Amar Faiz Zainal Abidin
  • Khairul Kamaruddin Hasan





robustness test, fuzzy based controller, intelligent controller, extraction process, essential oil.


This paper presents a comparison simulation performance of robustness test using intelligent fuzzy based controller in extraction process of essential oil. In this study, the control variable is the steam temperature since it gives large effect to quality of essential oil.  Ideally, the aims of control system applications and design the controllers is to ensure the close loop system satisfies performance criteria such as the system must be stable, minimize the effects of disturbances, good set-point tracking which is rapid and smooth response to set point changes.  Thus, the robustness test is applied in this study to provide the controller that can produce a smooth control response and also robust to any changes of the operation conditions during running process. The standard performance criteria used to represent dynamic performance are percentage overshoot, rise time, settling time, root mean square error (RMSE) and time on recovering load disturbance. The STFPID controller that was used in controlling steam temperature for extraction process shows the excellent performances based on the result. However, both controllers pass the robustness test with small %OS, RMSE, settling time and rise time.




[1] Y. Kansha, L. Jia, and M.-S. Chiu, "Self-tuning PID controllers based on the Lyapunov approach," Chemical Engineering Science, vol. 63, pp. 2732 – 2740, 2008.

[2] C.-F. Hsu and B.-K. Lee, "FPGA-based adaptive PID control of a DC motor driver via sliding-mode approach," Expert Systems with Applications, vol. 38, pp. 11866-11872, 2011.

[3] J. Flores-Cerrillo and J. F. MacGregor, "Latent variable MPC for trajectory tracking in batch processes," Jounal of Process Control, vol. 15, pp. 651-663, 2005.

[4] M. Tajjudin, M. H. F. Rahiman, N. Ishak, H. Ismail, N. M. Arshad, and R. Adnan, " Adaptive Steam Temperature Regulation for Essential Oil Extraction Process " International Journal of Control Science and Engineering vol. 2, pp. 111-119, 2012.

[5] X. Wu, et al., "Fuzzy modeling and predictive control of superheater steam temperature for power plant," ISA Transactions, vol. 56, pp. 241-251, 2015/05/01/ 2015.

[6] H. Hu, et al., "Feedforward DMC-PID cascade strategy for main steam temperature control system in fossil-fired power plant," in 2017 29th Chinese Control And Decision Conference (CCDC), 2017, pp. 3087-3091.

[7] Z. M. Yusoff, et al., "Steam temperature control of hydro-diffusion essential oil extraction system using hybrid-fuzzy plus PID controller," in 2014 IEEE Conference on Systems, Process and Control (ICSPC 2014), 2014, pp. 105-110.

[8] Monje CA, Chen YQ, Vinagre BM, Xue D, Feliu V. Fractional-order systems and controls: fundamentals and applications. London: Springer; 2010.

[9] Caponetto R, Dongola G, Fortuna L, Petras I. Fractional order systems: modeling and control applications, Vol. 72. Singapore: World Scientific Publishing; 2010.

[10] Das S, Pan I, Das S. Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time. ISA Trans 2013;52(4):550–66.

[11] Das S, Pan I, Das S, Gupta A. Improved model reduction and tuning of fractional-order PIλDm controllers for analytical rule extraction with genetic programming. ISA Trans 2012;51(2):237–61

[12] Das S, Pan I, Das S. Fractional order fuzzy control of nuclear reactor power with thermal-hydraulic effects in the presence of random network induced delay and sensor noise having long range dependence. Energy Convers Manag 2013;68:200–18

[13] Das S, Pan I, Das S, Gupta A. Master-slave chaos synchronization via optimal fractional order PIλDm controller with bacterial foraging algorithm. Nonlinear Dyn 2012;69(4):2193–206.

[14] Efe MO. Fractional fuzzy adaptive sliding-mode control of a 2-DOF direct-drive robot arm. Syst Man Cybern Part B: Cybern IEEE Trans 2008;38(6):1561–70.

[15] Delavari H, Ghaderi R, Ranjbar A, Momani S. Fuzzy fractional order sliding mode controller for nonlinear systems. Commun Nonlinear Sci Numer Simul 2010;15(4):963–78

[16] Das S, Pan I. On the mixed H2/H1 loop shaping trade-offs in fractional order control of the AVR system. Ind Inf IEEE Trans 2014;10(4):1982–91.

[17] I. Pan, et al., "Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay," ISA Transactions, vol. 50, pp. 28-36, 2011.

[18] "Fuzzy Logic Toolbox," R. 2007a, Ed.: MathWorks Inc., 2007.

[19] J. H.Lilly, Fuzzy control and identification: John Wiley & Sons, Inc., Hoboken, New Jersey, 2010.

[20] C. Cheng and M.-S. Chiu, "Robust PID controller design for nonlinear processes using JITL technique," Chemical Engineering Science, vol. 63, pp. 5141 -- 5148, 2008

[21] Z. Yusuf, "Fuzzy logic control for glycerin bleaching temperature control," Msc of Electrical Engineering, Universiti Teknologi Mara, Shah Alam, 2012.

[22] A. Ohata, K. Furuta, and H. Nita, "Identification of nonlinear ARX model with input and output dependent coefficients," in Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, 2006 IEEE, 2006, pp. 2577-2582.

[23] L. Reznik, Fuzzy controllers, 1 ed. Oxford: Newnes, 1997

[24] Z. M. Yusoff, et al., "Hybrid fuzzy plus PID controller of hydro-diffusion steam distillation essential oil extraction system: Design and performance evaluation," AIP Conference Proceedings, vol. 1774, 2016.

[25] Z. M. Yusoff, et al., "Self- tuning fuzzy PID controller using online method in essential oil extraction process," in 6th International Conference on Computing and Informatics, ICOCI, 2017, pp. 208-214.

View Full Article: