A Novel PI-PD Controller Tuning Method based on Neutrosophic Similarity Measure for Unstable and Integrating Processes with Time Delay

Integrating systems and unstable systems are two types of systems that are widely used in various industries, including control engineering and electrical engineering. However, controlling these systems can be a daunting task due to their inherent complexity. To address this challenge, the PI-PD controller structure has been widely adopted in the industry, as it has proven to be very successful in controlling integrating and unstable systems. In this paper, a new design method is proposed to determine the optimal controller parameters in the control of integrating and unstable systems with time delay. The design method utilizes a Genetic Algorithm-based optimization technique, which incorporates a new objective function that is based on the neutrosophic similarity measure. Two different types of plants have been chosen as examples in this study. The first plant is a time-delayed integrating system. The second plant is an unstable system, which means that the output signal is very sensitive to any changes or disturbances in the input signal. This study examines the parameter uncertainties of systems by comparing them with some design methods from the literature. The results are presented comparatively in figures and tables to show the superiority of the proposed method.

A Novel PI-PD Controller Tuning Method based on Neutrosophic Similarity Measure for Unstable and Integrating Processes with Time Delay

Integrating systems and unstable systems are two types of systems that are widely used in various industries, including control engineering and electrical engineering. However, controlling these systems can be a daunting task due to their inherent complexity. To address this challenge, the PI-PD controller structure has been widely adopted in the industry, as it has proven to be very successful in controlling integrating and unstable systems. In this paper, a new design method is proposed to determine the optimal controller parameters in the control of integrating and unstable systems with time delay. The design method utilizes a Genetic Algorithm-based optimization technique, which incorporates a new objective function that is based on the neutrosophic similarity measure. Two different types of plants have been chosen as examples in this study. The first plant is a time-delayed integrating system. The second plant is an unstable system, which means that the output signal is very sensitive to any changes or disturbances in the input signal. This study examines the parameter uncertainties of systems by comparing them with some design methods from the literature. The results are presented comparatively in figures and tables to show the superiority of the proposed method.

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  • [1] J. E. Normey-Rico, Control of dead-time processes. Springer Science & Business Media, 2007.
  • [2] L. M. Eriksson and M. Johansson, "PID controller tuning rules for varying time-delay systems," in 2007 American Control Conference, 2007, pp. 619-625: IEEE.
  • [3] S. E. Hamamci and M. Koksal, "Calculation of all stabilizing fractional-order PD controllers for integrating time delay systems," Computers & Mathematics with Applications, vol. 59, no. 5, pp. 1621-1629, 2010.
  • [4] G. L. Raja and A. Ali, "New PI-PD Controller Design Strategy for Industrial Unstable and Integrating Processes with Dead Time and Inverse Response," Journal of Control, Automation and Electrical Systems, vol. 32, no. 2, pp. 266-280, 2021.
  • [5] P. García and P. Albertos, "Robust tuning of a generalized predictor-based controller for integrating and unstable systems with long time-delay," Journal of Process Control, vol. 23, no. 8, pp. 1205-1216, 2013.
  • [6] N. Tan, "Computation of stabilizing PI-PD controllers," International Journal of Control, Automation and Systems, vol. 7, no. 2, pp. 175-184, 2009.
  • [7] P. Dash, L. C. Saikia, and N. Sinha, "Flower pollination algorithm optimized PI-PD cascade controller in automatic generation control of a multi-area power system," International Journal of Electrical Power & Energy Systems, vol. 82, pp. 19-28, 2016.
  • [8] E. Dincel and M. T. Söylemez, "Digital PI-PD controller design for arbitrary order systems: Dominant pole placement approach," ISA transactions, vol. 79, pp. 189-201, 2018.
  • [9] I. Kaya, "A PI-PD controller design for control of unstable and integrating processes," ISA transactions, vol. 42, no. 1, pp. 111-121, 2003.
  • [10] H. Li, "Tuning of PI–PD controller using extended non-minimal state space model predictive control for the stabilized gasoline vapor pressure in a stabilized tower," Chemometrics and Intelligent Laboratory Systems, vol. 142, pp. 1-8, 2015.
  • [11] K. Ogata and Y. Yang, Modern control engineering. Prentice hall India, 2002.
  • [12] K. J. Åström and T. Hägglund, PID controllers: theory, design, and tuning. Instrument society of America Research Triangle Park, NC, 1995.
  • [13] H. Wu, W. Su, and Z. Liu, "PID controllers: Design and tuning methods," in 2014 9th IEEE Conference on Industrial Electronics and Applications, 2014, pp. 808-813: IEEE.
  • [14] D. P. Atherton and S. Majhi, "Limitations of PID controllers," in Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251), 1999, vol. 6, pp. 3843-3847: IEEE.
  • [15] M. Zhuang and D. Atherton, "Tuning PID controllers with integral performance criteria," in International Conference on Control 1991. Control'91, 1991, pp. 481-486: IET.
  • [16] F. Padula and A. Visioli, "Tuning rules for optimal PID and fractional-order PID controllers," Journal of process control, vol. 21, no. 1, pp. 69-81, 2011.
  • [17] F. N. Deniz, A. Yüce, and N. Tan, "Tuning of PI-PD controller based on standard forms for fractional order systems," Journal of Applied Nonlinear Dynamics, vol. 8, no. 1, pp. 5-21, 2019.
  • [18] A. Ali and S. Majhi, "Integral criteria for optimal tuning of PI/PID controllers for integrating processes," Asian Journal of Control, vol. 13, no. 2, pp. 328-337, 2011.
  • [19] S. M. H. Mousakazemi, "Comparison of the error-integral performance indexes in a GA-tuned PID controlling system of a PWR-type nuclear reactor point-kinetics model," Progress in Nuclear Energy, vol. 132, p. 103604, 2021.
  • [20] M. S. Tavazoei, "Notes on integral performance indices in fractional-order control systems," Journal of Process Control, vol. 20, no. 3, pp. 285-291, 2010.
  • [21] J. Ye, "PID tuning method using single-valued neutrosophic cosine measure and genetic algorithm," Intelligent Automation and Soft Computing, vol. 25, no. 1, pp. 15-23, 2019.
  • [22] Z. Fu, C. Liu, S. Ruan, and K. Chen, "Design of Neutrosophic Self-Tuning PID Controller for AC Permanent Magnet Synchronous Motor Based on Neutrosophic Theory," Mathematical Problems in Engineering, vol. 2021, p. 5548184, 2021/05/12 2021.
  • [23] M. M. Ozyetkin, C. Onat, and N. Tan, "PI‐PD controller design for time delay systems via the weighted geometrical center method," Asian Journal of Control, vol. 22, no. 5, pp. 1811-1826, 2020.
  • [24] C. Onat, "A new design method for PI–PD control of unstable processes with dead time," ISA transactions, vol. 84, pp. 69-81, 2019.
  • [25] P. K. Padhy and S. Majhi, "Relay based PI–PD design for stable and unstable FOPDT processes," Computers & chemical engineering, vol. 30, no. 5, pp. 790-796, 2006.
  • [26] S. Chakraborty, S. Ghosh, and A. K. Naskar, "I–PD controller for integrating plus time-delay processes," IET Control Theory & Applications, vol. 11, no. 17, pp. 3137-3145, 2017.
  • [27] I. Kaya, "Optimal PI–PD Controller Design for Pure Integrating Processes with Time Delay," Journal of Control, Automation and Electrical Systems, vol. 32, no. 3, pp. 563-572, 2021.
  • [28] F. Smarandache, "Neutrosophy," arXiv preprint math/0010099, 2000.
  • [29] F. Smarandache, Neutrosophy, a new Branch of Philosophy. Infinite Study, 2002.
  • [30] F. Smarandache, "Definiton of neutrosophic logic-a generalization of the intuitionistic fuzzy logic," in EUSFLAT Conf., 2003, pp. 141-146: Citeseer.
  • [31] F. Smarandache, "Neutrosophic set-a generalization of the intuitionistic fuzzy set," International journal of pure and applied mathematics, vol. 24, no. 3, p. 287, 2005.
  • [32] H. Wang, F. Smarandache, Y. Zhang, and R. Sunderraman, Single valued neutrosophic sets. Infinite study, 2010.
  • [33] M. S. Can and O. F. Ozguven, "PID tuning with neutrosophic similarity measure," International Journal of Fuzzy Systems, vol. 19, no. 2, 2017.
  • [34] S. Broumi and F. Smarandache, Several similarity measures of neutrosophic sets. Infinite Study, 2013.
  • [35] A. Mukherjee and S. Sarkar, "Several similarity measures of neutrosophic soft sets and its application in real life problems," Annals of Pure and Applied Mathematics, vol. 7, no. 1, pp. 1-6, 2014.
  • [36] D. E. Goldberg and K. Deb, "A comparative analysis of selection schemes used in genetic algorithms," in Foundations of genetic algorithms, vol. 1: Elsevier, 1991, pp. 69-93.
  • [37] I. Kaya, "I-PD controller design for integrating time delay processes based on optimum analytical formulas," IFAC-PapersOnLine, vol. 51, no. 4, pp. 575-580, 2018.