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Evolutionary Approach for Automatic Design of PID Controllers

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Advances in Data Analysis with Computational Intelligence Methods

Part of the book series: Studies in Computational Intelligence ((SCI,volume 738))

Abstract

In this paper a new approach for automatic design of PID controllers is presented. It is based on meta-heuristic hybrid algorithm which is a combination of the genetic algorithm and the imperialist one. Main characteristic of the proposed approach is capability to design the structure and the structure parameters of a controller. It is a big advantage because it eliminates trial and error process of design the controller structure. Moreover, the proposed approach has been developed in a way that allows to obtain controllers taking different control criteria and a different control object into consideration.

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References

  1. Agharkakli, A., Sabet, G.S., Barouz, A.: Simulation and analysis of passive and active suspension system using quarter car model for different road profile. Int. J. Eng. Trends Technol. 3(5), 636–644 (2012)

    Google Scholar 

  2. Ali, S.R., Aldair, A.A., Almousawi, A.K.: Design an optimal PID controller using artificial bee colony and genetic algorithm for autonomous mobile robot. Int. J. Comput. Appl. 100(1), 6 (2014)

    Google Scholar 

  3. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Congr. Evolutionary Comput. 7(4661), 4666 (2007)

    Google Scholar 

  4. Bas, E.: The training of multiplicative neuron model based artificial neural networks with differential evolution algorithm for forecasting. J. Artif. Intell. Soft Comput. Res. 6, 5–12 (2016)

    Article  Google Scholar 

  5. Binitha, S., Sathya, S.S.: A survey of bio-inspired optimization algorithms. Int. J. Soft Comput. Eng. (IJSCE) 2(2), 137–151 (2012)

    Google Scholar 

  6. Boiko, I.: Variable-structure PID controller for level process. Control Eng. Pract. 21(5), 700–707 (2013)

    Article  Google Scholar 

  7. Cpałka, K.: A Method for Designing Flexible Neuro-fuzzy systems. Lecture Notes in Artificial Intelligence, Springer 4029, 212–219 (2006)

    Google Scholar 

  8. Cpałka, K.: Design of Interpretable Fuzzy Systems. Springer (2017)

    Google Scholar 

  9. Cpałka, K., Rebrova, O., Nowicki, R., Rutkowski, L.: On design of flexible neuro-fuzzy systems for nonlinear modelling. Int. J. Gen. Syst. 42(6), 706–720 (2013)

    Article  MATH  Google Scholar 

  10. Cpałka, K., Rutkowski, L.: Flexible Takagi-Sugeno. Fuzzy systems. In: Neural Networks, Proceedings of the 2005 IEEE International Joint Conference on IJCNN ’05, vol. 3, pp. 1764–1769 (2005)

    Google Scholar 

  11. Duda, P., Jaworski, M., Pietruczuk, L.: On pre-processing algorithms for data stream. International Conference on Artificial Intelligence and Soft Computing. Lecture Notes in Artificial Intelligence, vol. 7268, pp. 56–63. Springer (2012)

    Google Scholar 

  12. Eckenrode, R.T.: Weighting multiple criteria. Manag. Sci. 12, 19–180 (1965)

    Article  Google Scholar 

  13. Gabryel, M., Cpałka, K., Rutkowski, L.: Evolutionary strategies for learning of neuro-fuzzy systems. In: Proceedings of the I Workshop on Genetic Fuzzy Systems, Granada, pp. 119–123 (2005)

    Google Scholar 

  14. Gaweda, A.E., Scherer, R.: Fuzzy number-based hierarchical fuzzy system. ICAIS, pp. 302–307 (2004)

    Google Scholar 

  15. Ghorbani, R., Wu, Q., Wang, G.G.: Nearly optimal neural network stabilization of bipedal standing using genetic algorithm. Eng. Appl. Artif. Intell. 20, 473–480 (2007)

    Article  Google Scholar 

  16. Jaworski, M., Pietruczuk, L., Duda, P.: On resources optimization in fuzzy clustering of data streams. In: International Conference on Artificial Intelligence and Soft Computing. Lecture Notes in Artificial Intelligence, vol. 7268, pp. 92–99. Springer (2012)

    Google Scholar 

  17. Leva, A., Papadopoulos, A.V.: Tuning of event-based industrial controllers with simple stability guarantees. J. Process Control 23, 1251–1260 (2013)

    Article  Google Scholar 

  18. Lin, J., Lian, R.: Intelligent control of active suspension systems. IEEE Trans. Ind. Electron. 58(2), 618–628 (2010)

    Article  Google Scholar 

  19. Łapa, K., Cpałka, K., Wang, L.: New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability. Lect. Notes Comput. Sci. 8467, 217–232 (2014)

    Article  Google Scholar 

  20. Łapa, K., Szczypta, J., Venkatesan, R.: Aspects of structure and parameters selection of control systems using selected multi-population algorithms. Lect. Notes Comput. Sci. 9120, 247–260 (2015)

    Article  Google Scholar 

  21. Maggio, M., Bonvini, M., Leva, A.: The PID+p controller structure and its contextual autotuning. J. Process Control 22, 1237–1245 (2012)

    Article  Google Scholar 

  22. Malhotra, R., Sodh, R.: Boiler flow control using PID and fuzzy logic controller. IJCSET 1(6), 315–31 (2011)

    Google Scholar 

  23. Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Struct. Multidiscip. Optim. 26, 369–395 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  24. Marwala, T.: Control of complex systems using Bayesian networks and genetic algorithm. IJES 5, 28–37 (2004)

    Google Scholar 

  25. Perng, J.-W., Chen, G.-Y., Hsieh, S.-C.: Optimal PID controller design based on PSO-RBFNN for wind turbine systems. Energies 7, 191–209 (2014)

    Article  Google Scholar 

  26. Pietruczuk, L., Rutkowski, L., Jaworski, M., Duda, P.: How to adjust an ensemble size in stream data mining. Inf. Sci. 381, 46–54 (2017)

    Article  MathSciNet  Google Scholar 

  27. Przybył, A., Łapa, K., Szczypta, J., Wang, L.: The method of evolutionary designing the elastic controller structure. Lect. Notes Comput. Sci. 9692, 476–492 (2016)

    Article  Google Scholar 

  28. Rasoanarivo, I., Brechet, S., Battiston, A., Nahid-Mobarakeh, B.: Behavioral analysis of a boost converter with high performance source filter and a fractional-order PID controller. In: IEEE Industry Applications Society Annual Meeting (IAS), pp. 1–6 (2012)

    Google Scholar 

  29. Ribića, A.I., Mataušek, M.R.: A dead-time compensating PID controller structure and robust tuning. J. Process Control 22, 1340–1349 (2012)

    Article  Google Scholar 

  30. Rutkowski, L.: Computational Intelligence. Springer (2007)

    Google Scholar 

  31. Rutkowski, L., Cpałka, K.: A general approach to neuro-fuzzy systems. In: The 10th IEEE International Conference on Fuzzy Systems, 2001, Melbourne, pp. 1428–1431 (2001)

    Google Scholar 

  32. Rutkowski, L., Cpałka, K.: A neuro-fuzzy controller with a compromise fuzzy reasoning. Control Cybern. 31(2), 297–308 (2002)

    MATH  Google Scholar 

  33. Rutkowski, L., Cpałka, K.: Compromise approach to neuro-fuzzy systems. In: 2nd Euro-International Symposium on Computation Intelligence Location: KOSICE, SLOVAKIA Date: 16–19 Jume 2002, vol. 76, pp. 85–90 (2002)

    Google Scholar 

  34. Rutkowski, L., Cpałka, K.: Flexible weighted neuro-fuzzy systems. In: Proceedings of the 9th International Conference on Neural Information Processing (ICONIP’02), Orchid Country Club, Singapore, vol. 4, pp. 1857–1861 (2002)

    Google Scholar 

  35. Rutkowski, L., Cpałka, K.: Neuro-fuzzy systems derived from quasi-triangular norms. In: Proceedigns of the IEEE International Conference on Fuzzy Systems, Budapest, July 26–29, vol. 2, pp. 1031–1036 (2004)

    Google Scholar 

  36. Rutkowski, L., Przybył, A., Cpałka, K.: Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation. IEEE Trans. Ind. Electron. 59(2), 1238–1247 (2012)

    Article  Google Scholar 

  37. Rutkowski, L., Przybył, A., Cpałka, K., Er, M.J.: Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. Lect. Notes Artif. Intell. 114, 645–650 (2010)

    Google Scholar 

  38. Saad, M.S., Jamaluddin, H., Sodh, I.Z.M.: Implementation of PID controller tuning using differential evolution and genetic algorithms. Int. J. Innov. Comput. Inf. Control 8(11), 7761–7779 (2012)

    Google Scholar 

  39. Sande, T.P.J., Gysen, B.L.J., Besselink, I.J.M., Paulides, J.J.H., Lomonova, E.A., Nijmeijer, H.: Robust control of an electromagnetic active suspension system: simulations and measurements. Mechatronics 23, 2 (2013)

    Google Scholar 

  40. Stone, C., Chi-Wei, L.: Fuzzy PDFF-IIR controller for PMSM drive systems. Control Eng. Pract. 19, 828–835 (2011)

    Article  Google Scholar 

  41. Szczypta, J., Łapa, K., Shao, Z.: Aspects of the selection of the structure and parameters of controllers using selected population based algorithms. Lect. Notes Comput. Sci. 8467, 440–454 (2014)

    Article  Google Scholar 

  42. Teng, T.H., Tan, A.H., Żurada, J.M.: Self-Organizing neural networks integrating domain knowledge and reinforcement learning. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 889–902 (2015)

    Article  MathSciNet  Google Scholar 

  43. Van de Wal, M., Philips, P., De Jager, B.: Actuator and sensor selection for an active vehicle suspension aimed at robust performance. Int. J. Control 70(5), 703–720 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  44. Yazdani, A.M., Ahmadi, A., Buyamin, S., Rahmat, M.F., Davoudifar, F., Rahim, H.A.: Imperialist competitive algorithm-based fuzzy PID control methodology for speed tracking enhancement of stepper motor. Int. J. Smart Sens. Intell. Syst. 5, 3 (2012)

    Google Scholar 

  45. Zalasiński, M.: New algorithm for on-line signature verification using characteristic global features. Adv. Intell. Syst. Comput. 432, 137–146 (2016)

    Google Scholar 

  46. Zalasiński, M., Cpałka, K.: New algorithm for on-line signature verification using characteristic hybrid partitions. Adv. Intell. Syst. Comput. 432, 147–157 (2016)

    Google Scholar 

  47. Zalasiński, M., Cpałka, K., Hayashi, Y.: A new approach to the dynamic signature verification aimed at minimizing the number of global features. Lect. Notes Comput. Sci. 9693, 218–231 (2016)

    Article  Google Scholar 

  48. Zalasiński, M., Cpałka, K., Rakus-Andersson, E.: An Idea of the dynamic signature verification based on a hybrid approach. Lect. Notes Comput. Sci. 9693, 232–246 (2016)

    Article  Google Scholar 

  49. Żurada, J.M., Jedruch, W., Barski, M.: Neural Networks. Polish Scientific Publishers, Warsaw, Poland (1996)

    Google Scholar 

  50. Żurada, J.M.: Introduction to Artificial Neural Systems. Jaico Publishing House (2005)

    Google Scholar 

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Acknowledgements

The project was financed by the National Science Centre (Poland) on the basis of the decision number DEC-2012/05/B/ST7/02138.

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Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-200, Czestochowa, Poland

    Krystian Łapa & Krzysztof Cpałka

Authors
  1. Krystian Łapa
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  2. Krzysztof Cpałka
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Correspondence to Krzysztof Cpałka .

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Editors and Affiliations

  1. Department of Medicine, Division of Nephrology and Hypertension, University of Louisville, Louisville, Kentucky, USA

    Adam E Gawęda

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Institute of Computational Intelligence, Department of Mechanical Engineering and Computer Science, Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  4. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, Oklahoma, USA

    Gary G. Yen

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Łapa, K., Cpałka, K. (2018). Evolutionary Approach for Automatic Design of PID Controllers. In: Gawęda, A., Kacprzyk, J., Rutkowski, L., Yen, G. (eds) Advances in Data Analysis with Computational Intelligence Methods. Studies in Computational Intelligence, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-319-67946-4_16

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  • DOI: https://doi.org/10.1007/978-3-319-67946-4_16

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  • Online ISBN: 978-3-319-67946-4

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