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Models for Optimal Online Tuning Based on Computational Intelligence of PID Controllers Applied to Operational Processes of Bulk Reclaimers

  • José Pinheiro de MouraEmail author
  • João Viana da Fonseca Neto
  • Patrícia Helena M. Rêgo
Article
  • 31 Downloads

Abstract

This paper considers the development of models for the optimal online tuning of PID controllers based on computational intelligence approaches that are applied to the operational processes of bulk reclaimers. In the first instance, the optimal gains of a PID controller are determined using a structured artificial neural network (ANN), and in the second instance, a fuzzy system is used to carry out online adjustment through a real-time gain scheduling scheme. The bulk resumption process consists of picking up the stored material in stacks and transporting it using conveyor belts for shipment. For the control system, a model based on data pertaining to the electric current in a bucket wheel motor (a device that picks up material) is estimated and compared with the load measured using a scale. The difference between the load estimated by the model and that measured by the scale is the error, and the proposed control system is designed to minimize it. The results of simulations show that the controller models performed better using structured ANNs and fuzzy logic than PID controllers tuned by the second Ziegler–Nichols method, and the PID–fuzzy controller proposed by Zhao and Tomizuka.

Keywords

Computational intelligence PID controllers Bulk reclaimers Artificial neural network Fuzzy system 

Notes

Acknowledgements

We thank PPGEE of the Federal University of Maranhão for the technical/scientific and practical lessons. We are especially grateful to FAPEMA for encouraging high-level research in the State of Maranhão. We also thank the Department of Physics of the State University of Maranhão for making this research feasible. We thank CAPES for promoting and supporting advanced research that contributed to this work. Finally, the Vale S.A. Company for providing its specialists for practical guidance for the execution of the experiments.

References

  1. Alcántara, S., Vilanova, R., & Pedret, C. (2013). PID control in terms of robustness/performance and servo/regulator trade-offs: A unifying approach to balanced autotuning. Journal of Process Control, 23(4), 527–542.CrossRefGoogle Scholar
  2. Almutairi, N. B., & Zribi, M. (2009). Sliding mode control of a three-dimensional overhead crane. Journal of Vibration and Control, 15(11), 1679–1730.MathSciNetCrossRefzbMATHGoogle Scholar
  3. Aström, K. J., & Hägglund, T. (1995). PID controllers: Theory, design, and tuning (Vol. 2). Research Triangle Park, NC: Instrument Society of America.Google Scholar
  4. Aström, K. J., & Hägglund, T. (2001). The future of PID control. Control Engineering Practice, 9(11), 1163–1175.CrossRefGoogle Scholar
  5. Brisilla, R. M., & Sankaranarayanan, V. (2017). Extended state observer-based sliding mode control for multi-input multi-output system with multiple disturbances. Journal of Control, Automation and Electrical Systems, 28(1), 11–25.CrossRefGoogle Scholar
  6. Castillo-Garcia, F. J., Feliu-Batlle, V., & Rivas-Perez, R. (2013). Frequency specifications regions of fractional-order PI controllers for first order plus time delay processes. Journal of Process Control, 23(4), 598–612.CrossRefGoogle Scholar
  7. Chang, C.-Y. (2007). Adaptive fuzzy controller of the overhead cranes with nonlinear disturbance. IEEE Transactions on Industrial Informatics, 3(2), 164–172.CrossRefGoogle Scholar
  8. Chu, Q., Liu, T., & Wang, Y. (2013). Research on excitation controller based on the adaptive fuzzy PID technique. In 5th international conference on intelligent human-machine systems and cybernetics IHMSC, IEEE (Vol. 1, pp. 297–300).Google Scholar
  9. Cohen, G. H. (1953). Theoretical consideration of retarded control. Transactions of ASME, 75, 827–834.Google Scholar
  10. de Moura, J. P., & da Fonseca Neto, J. V. N. (2003). Sistema Especialista para Identificação de Falhas e Tomada de Decisão (Expert System for Fault Identification and Decision Making), Master thesis, UFMA.Google Scholar
  11. de Moura, J. P., & da Fonseca Neto, J. V. (2016a). Modelo de um Controle de Nível de Sólidos em Silos Usando Sistemas Fuzzy Aplicados Em PLC Industrial (Model of a Level Control of Solids in Silos Using Fuzzy Systems Applied in Industrial PLC). CBA (pp. 51–57).Google Scholar
  12. de Moura, J. P., da Fonseca Neto, J. V. (2016b). Fuzzy controller in the cargo control wagons dump. In IEEE conference on evolving and adaptive intelligent systems (EAIS) (pp. 10–16).Google Scholar
  13. de Moura, J. P., de Oliveira Serra, L. G., & da Fonseca, J. V. (2013). Logica Fuzzy no Controle de Embarque de Navios no Terminal Marítimo de Ponta da Madeira (Fuzzy Logic in the Control of Shipping of Ships in the Maritime Terminal of Ponta da Madeira). SBAI—Artigo (Article) 4836.Google Scholar
  14. Desborough, L., & Miller, R. (2002). Increasing customer value of industrial control performance monitoring-Honeywell’s experience. In AIChE symposium series. New York: American Institute of Chemical Engineers (Vol. 326, pp. 169–189).Google Scholar
  15. Dias, C. G., & de Sousa, C. M. (2018). A neuro-fuzzy approach for locating broken rotor bars in induction motors at very low slip. Journal of Control, Automation and Electrical Systems, 29(4), 1–11.CrossRefGoogle Scholar
  16. Eris, O., & Kurtulan, S. (2011). A new PI tuning rule for first order plus dead-time systems (pp. 1–4). IEEE AFRICON.Google Scholar
  17. Farias, O. S., Santos, J. H. M., & Neto, J. V. F., Labidi, S., Drummond, T., de Moura, J. P. & Neves, S. C. F. (2008). A real time expert system for faults identification in rotary railcar dumpers. In ICINCO-ICSO (pp. 347–350).Google Scholar
  18. Garpinger, O. (2015). Analysis and design of software-based optimal PID controllers. Department of Automatic Control, Lund Institute of Technology, Lund University.Google Scholar
  19. Golub, G. H., & Van Loan, C. F. (2012). Matrix computations (Vol. 3). Baltimore: JHU Press.zbMATHGoogle Scholar
  20. Gopi Krishna Rao, P. V., Subramanyam, M. V., & Satyaprasad, K., (2013). Model based tuning of PID controller. In JoCI (Vol. 16).Google Scholar
  21. Gopi Krishna Rao, P. V., Venkata, S. M., & Kodati, S. (2015). Robust design of PID controller using IMC technique for integrating process based on maximum sensitivity. Journal of Control, Automation and Electrical Systems, 26, 466–475.CrossRefGoogle Scholar
  22. Ham, F. M., & Kostanic, I. (2000). Principles of neurocomputing for science and engineering. New York: McGraw-Hill Higher Education.Google Scholar
  23. Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (2011). Robust statistics: The approach based on influence functions (Vol. 114). New York: Wiley.zbMATHGoogle Scholar
  24. Haykin, S. (1994). Neural networks: A comprehensive foundation. Upper Saddle River: Prentice Hall PTR.zbMATHGoogle Scholar
  25. Huber, P. J. (1996). Robust statistical procedures. Philadelphia: SIAM.CrossRefzbMATHGoogle Scholar
  26. Jeng, J.-C. (2015). A model-free direct synthesis method for PI/PID controller design based on disturbance rejection. Chemometrics and Intelligent Laboratory Systems, 147, 14–29.CrossRefGoogle Scholar
  27. Kar, B., & Roy, P. (2018). A comparative study between cascaded FOPI–FOPD and IOPI–IOPD controllers applied to a level control problem in a coupled tank system. Journal of Control, Automation and Electrical Systems, 29(3), 340–349.CrossRefGoogle Scholar
  28. Katsuhiko, O. (2009). Modern control engineering (5th ed.). Prentice-Hall Electrical Engineering Series. Instrumentation and Controls Series.Google Scholar
  29. Khayamy, M., Chaoui, H., Oukaour, A., & Gualous, H. (2016). Adaptive fuzzy logic control mixing strategy of DC/DC converters in both discontinuous and continuous conduction modes. Journal of Control, Automation and Electrical Systems, 27(3), 274–288.CrossRefGoogle Scholar
  30. Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242.CrossRefGoogle Scholar
  31. Lee, H.-H., Liang, Y., & Segura, D. (2006). A sliding-mode antiswing trajectory control for overhead cranes with high-speed load hoisting. Journal of Dynamic Systems, Measurement, and Control, 128(4), 842–845.CrossRefGoogle Scholar
  32. Lee, M., Shamsuzzoha, M., & Vu, T. N. L. (2008). IMC–PID approach: An effective way to get an analytical design of robust PID controller. In ICCAS international conference on control, automation and systems (pp. 2861–2866).Google Scholar
  33. Le, T. A., Kim, G.-H., Kim, M. Y., & Lee, S.-G. (2012). Partial feedback linearization control of overhead cranes with varying cable lengths. International Journal of Precision Engineering and Manufacturing, 13(4), 501–507.CrossRefGoogle Scholar
  34. Lopes, B. E., de Moura, J. P., Ribeiro, D. A., e Borges, F. H. C., & de Souza, M. A. (2012). Flow optimization for iron ore reclaiming process. In ICINCO (pp. 425–432).Google Scholar
  35. Mizuyama, D., da Silva, C. E., Goedtel, A., Graciola, C. L., & Palácios, R. H. C. (2018). Neural predictor for surface roughness of turned parts. Journal of Control, Automation and Electrical Systems, 29(3), 360–370.CrossRefGoogle Scholar
  36. Murrill, P. W. (1967). Automatic control of processes. Scranton: International Textbook Company.Google Scholar
  37. Ngo, Q. H., & Hong, K.-S. (2012). Sliding-mode antisway control of an offshore container crane. IEEE/ASME Transactions on Mechatronics, 17(2), 201–209.CrossRefGoogle Scholar
  38. O’Dwyer, A. (2009). Handbook of PI and PID controller tuning rules. Singapore: World Scientific.CrossRefzbMATHGoogle Scholar
  39. Pai, N.-S., Chang, S.-C., & Huang, C.-T. (2010). Tuning PI/PID controllers for integrating processes with deadtime and inverse response by simple calculations. Journal of Process Control, 20(6), 726–733.CrossRefGoogle Scholar
  40. Park, H., Chwa, D., & Hong, K. (2007). A feedback linearization control of container cranes: Varying rope length. International Journal of Control Automation and Systems, 5(4), 379–387.Google Scholar
  41. Parr, E. A. (1998). Industrial control handbook. New York City: Industrial Press Inc.Google Scholar
  42. Pratap, B., & Purwar, S. (2014). Real-time implementation of neuro adaptive observer-based robust backstepping controller for twin rotor control system. Journal of Control, Automation and Electrical Systems, 25(2), 137–150.CrossRefGoogle Scholar
  43. Precup, R.-E., & Hellendoorn, H. (2011). A survey on industrial applications of fuzzy control. Computers in Industry, 62(3), 213–226.CrossRefGoogle Scholar
  44. Rossomando, F. G., & Soria, C. M. (2015). Design and implementation of adaptive neural-PID for non linear dynamics in mobile robots. In Institute of electrical and electronics engineers (pp. 913–918).Google Scholar
  45. Sanchez, E. N., Becerra, H. M., & Velez, C. M. (2007). Combining fuzzy, PID and regulation control for an autonomous mini-helicopter information sciences. Information Sciences, 177(10), 1999–2022.CrossRefGoogle Scholar
  46. Shamsuzzoha, M. (2014). A unified approach of PID controller tuning for time delay processes. In American Control Conference—ACC. IEEE (pp. 4865–4870).Google Scholar
  47. Sun, N., & Fang, Y. (2012). New energy analytical results for the regulation of underactuated overhead cranes: An end-effector motion-based approach. IEEE Transactions on Industrial Electronics, 59(12), 4723–4734.CrossRefGoogle Scholar
  48. Verma, S. K., Yadav, S., & Nagar, S. K. (2017). Optimization of fractional order PID controller using grey wolf optimizer. Journal of Control, Automation and Electrical Systems, 28(3), 314–322.CrossRefGoogle Scholar
  49. Wang, L.-X. (1993). Stable adaptive fuzzy control of nonlinear systems. IEEE Transactions on Fuzzy Systems, 1(2), 146–165.CrossRefGoogle Scholar
  50. Yang, J. H., & Yang, K. S. (2007). Adaptive coupling control for overhead crane systems. Mechatronics, 17(2), 143–152.CrossRefGoogle Scholar
  51. Zhao, Z.-Y., Tomizuka, M., & Isaka, S. (1993). Fuzzy gain scheduling of PID controllers. IEEE Transactions on Systems, Man, and Cybernetics, 23(5), 1392–1398.CrossRefGoogle Scholar
  52. Ziegler, J. G., & Nichols, N. B. (1942). Optimum settings for automatic controllers. Transactions of the ASME, 64(11), 759–768.Google Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2019

Authors and Affiliations

  • José Pinheiro de Moura
    • 1
    • 2
    Email author
  • João Viana da Fonseca Neto
    • 1
    • 2
  • Patrícia Helena M. Rêgo
    • 1
  1. 1.UEMA - Cidade Universitária Paulo VISão LuísBrazil
  2. 2.UFMA - Cidade Universitátia Dom DelgadoSão LuísBrazil

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