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Control Algorithms for Plants Operating Under Variable Conditions, Applications

  • Stefan PreitlEmail author
  • Radu-Emil Precup
  • Zsuzsa Preitl
  • Alexandra-Iulia Stînean
  • Mircea-Bogdan Rădac
  • Claudia-Adina Dragoş
Conference paper
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 8)

Abstract

The chapter deals with development, analysis and applicability of several easy applicable control algorithms, dedicated to plants working under continuously variable conditions: variable plant parameters or (the worst case) variable structure, variable reference and variable load (disturbance). Two speed control applications are selected and treated from the wide range of such applications: a case specific to the metallurgical industry, and the speed control of an electric (hybrid) vehicle model. The efficiency of the algorithms is tested and illustrated on different plant models and also on laboratory equipment with variable moment of inertia. The presented algorithms are easily adaptable to similar applications.

Keywords

Control algorithms electrical driving systems variable conditions mathematical models switching logic 

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References

  1. 1.
    Christian, J.A., Turbe, M.A., Kabo, E.M., Manno, L.C., Johnson, E.N.: Development of a variable inertia reaction wheel system for spacecraft attitude control. In: Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit, Providence, RI, USA, 13 p. (2004)Google Scholar
  2. 2.
    Akpolat, Z.H., Asher, G.M., Clare, J.C.: A practical approach to the design of robust speed controllers for machine drives. IEEE Trans. Ind. Electron. 47, 315–324 (2000)CrossRefGoogle Scholar
  3. 3.
    Mink, F., Bahr, A.: Adaptive speed control for drives with variable moments of inertia and natural drequencies. LTi DRIVES GmbH Entwicklung Software, Lahnau, Germany (2011)Google Scholar
  4. 4.
    Lamar, K.: Digital control of permanent magnet synchronous motors. In: Proc. Budapest-Tech Jubilee Conference, Budapest, Hungary, pp. 213–228 (2004)Google Scholar
  5. 5.
    Modi, V.J., Karray, F., Mah, H.A.: Composite Control Scheme for Joint Tracking and Active Vibration Suppression of Mobile Flexible Manipulator Systems. Acta Astronautica 36, 261–275 (1995)CrossRefGoogle Scholar
  6. 6.
    Crowder, R.M.: Electric Drives and their Controls. Oxford University Press Inc., New York (1998)Google Scholar
  7. 7.
    Rizzoni, G.: Priciples and Applications of Electrical Engineering. McGraw-Hill (2000)Google Scholar
  8. 8.
    Preitl, Z., Bauer, P., Bokor, J.: Fuel Consumption Optimization for Hybrid Solar Vehicle. In: Proc. Workshop on Hybrid Solar Vehicles, Salerno, Italy (2006)Google Scholar
  9. 9.
    Preitl, Z.: Control design methods for optimal energy consumption systems, PhD Thesis, Supervisors: Prof. Dr. József Bokor, member of The Hungarian Academy of Science, Budapest University of Technology and Economics (2009)Google Scholar
  10. 10.
    Ťapák, P., Huba, M., Žáková: Constrained Control for Systems with Relative Degree One. In: Proc. 17th IFAC World Congress, Seoul, South Korea, vol. 17, pp. 5814–5819 (2008)Google Scholar
  11. 11.
    Åström, K.J., Hägglund, T.: PID controller theory: Design and tuning. Instrument Society of America, Research Triangle Park, NC (1995)Google Scholar
  12. 12.
    Ozawa, S., Furuya, H.: Feedback Liniearization Technique in Variable Inertia Systems. Trans. Japan Soc. Aero Space Sci. 45(147), 1–9 (2002)CrossRefGoogle Scholar
  13. 13.
    Möhler, R.R.: Applications to Bilinear Control. Prentice Hall, Englewood Cliffs (1991)Google Scholar
  14. 14.
    Sherer, C.: Mixed H 2/H  ∞  Control. In: Trends in Control: An European Perspective, Volume of the Special Contributions to the ECC, pp. 173–216 (1995)Google Scholar
  15. 15.
    Chiali, M., Gahinet, P.: H  ∞  Design with Pole Placement Constraints: an LMI Approach. IEEE Trans. Automat. Control 41, 358–367 (1996)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Åström, K.J., Panagopoulos, H., Hägglund, T.: Design of PI controllers based non-convex optimization. Automatica 34, 585–601 (1998)CrossRefzbMATHGoogle Scholar
  17. 17.
    Preitl, S., Precup, R.-E.: Extension of tuning relations after symmetrical optimum method for PI and PID controllers. Automatica 35(10), 1731–1736 (1999)CrossRefzbMATHGoogle Scholar
  18. 18.
    Preitl, S., Precup, R.-E.: Linear and Fuzzy Control Extensions of the Symmetrical Optimum Method. In: Proc. International Conference on Complex Systems: Synergy of Control, Computing & Communications (COSY 2011), Ohrid, Republic of Machedonia, pp. 59–68 (2011)Google Scholar
  19. 19.
    Vrancic, D., Peng, Y., Strmcnik, S.: A new PID controller tuning method based on multiple integrations. Control Engineering Practice 7(5), 623–633 (1999)CrossRefGoogle Scholar
  20. 20.
    Vrancic, D., Strmcnik, S., Juricic, D.: A magnitude optimum multiple integration tuning method for filtered PID controller. Automatica 37(9), 1473–1479 (2001)CrossRefzbMATHGoogle Scholar
  21. 21.
    Papadopoulos, K.G., Mermikli, K., Margaris, N.I.: Optimal tuning of PID controllers for integrating processes via the symmetrical optimum criterion. In: Proc. 19th Mediterranean Conference on Control and Automation (MED 2012), Corfu, Greece, pp. 1289–1294 (2011)Google Scholar
  22. 22.
    Papadopoulos, K.G., Mermikli, K., Margaris, N.I.: On the automatic tuning of PID type controllers via the magnitude optimum criterion. In: Proc. 2012 IEEE International Conference on Industrial Technology (ICIT 2012), Athens, Greece, pp. 869–874 (2012)Google Scholar
  23. 23.
    Papadopoulos, K.G., Margaris, N.I.: Extending the symmetrical optimum criterion to the design of PID type-p control loops. Journal of Process Control 22(1), 11–25 (2012)CrossRefGoogle Scholar
  24. 24.
    Loron, L.: Tuning of PID controllers by the non-symmetrical optimum method. Automatica 33(1), 103–107 (1997)CrossRefzbMATHGoogle Scholar
  25. 25.
    Precup, R.-E., Preitl, S.: Development of some fuzzy controllers with non-homogenous dynamics with respect to the input channels meant for a class of systems. In: Proc. European Control Conference (ECC 1999), Karlsruhe, Germany, paper index F56, 6 p. (1999)Google Scholar
  26. 26.
    Araki, M., Taguchi, H.: Two-degree-of-freedom PID controllers. Int. J. Control Automat. Syst. 1, 401–411 (2003)Google Scholar
  27. 27.
    Preitl, Z.: Model Based Design Methods for Speed Control, Applications. PhD Thesis, “Politehnica” University of Timisoara, Editura Politehnica, Timisoara, Romania (2008)Google Scholar
  28. 28.
    Preitl, S., Precup, R.-E., Preitl, Z.: Aspects concerning the tuning of 2-DOF fuzzy controllers. In: Proc. Xth Triennial International SAUM Conference on Systems, Automatic Control and Measurements (SAUM 2010), Nis, Serbia, pp. 210–219 (2010)Google Scholar
  29. 29.
    Miklosovic, R., Gao, Z.: A robust two-Degree of Freedom control design Technique and its practical application. In: Proc. 39th IAS Annual Meeting Conference Record of the 2004 IEEE Industry Application Conference, vol. 3, pp. 1495–1502 (2004)Google Scholar
  30. 30.
    Preitl, S., Precup, R.-E., Dragos, C.-A., Radac, M.-B.: Tuning of 2-DOF fuzzy PI(D) controllers, laboratory applications. In: Proc. 11th International Conference on Computational Intelligence and Informatics (CINTI 2010), Budapest, Hungary, pp. 237–242 (2010)Google Scholar
  31. 31.
    Bagheri, P., Nemati, H.: Novel tuning strategy for two-degree-of-freedom PI controllers. In: Proc. 18th IFAC World Congress, Milano, Italy, pp. 6757–6762 (2011)Google Scholar
  32. 32.
    Preitl, Z., Levendovszky, T.: Computer Aided Design of Two-Degree-of-Freedom (2DOF) Controllers. Buletinul Stiintific al Universitatii “Politehnica” din Timisoara, Romania, Seria Automatica si Calculatoare 48(62), 70–75 (2003)Google Scholar
  33. 33.
    Stinean, A.-I.: Contribuţii la Dezvoltarea unor Soluţii de Reglare Dedicate Sistemelor de Actionare Electrică cu Parametri Variabili si cu Intrări Variabile in Timp. Ph.D. thesis, Politehnica University of Timisoara, Timisoara, Romania (2014) (in Romanian)Google Scholar
  34. 34.
    Precup, R.-E., Preitl, S.: Fuzzy Controllers. Editura Orizonturi Universitare, Timisoara (1999)Google Scholar
  35. 35.
    Stinean, A.-I., Preitl, S., Precup, R.-E., Dragos, C.-A., Radac, M.-B.: Classical and Fuzzy Approaches to 2-DOF Control Solutions for BLDC-m Drives. In: Pap, E. (ed.) Intelligent Systems: Models and Applications. TIEI, vol. 3, pp. 175–193. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  36. 36.
    Proceedings of 18th IFAC World Congress, Milan, Italy (2011)Google Scholar
  37. 37.
    Bay, O.F., Bal, G., Demirbas, S.: Fuzzy Logic Based Control of a Brushless DC Servo Motor Drive. In: Proc. 7th International Power Electronics & Motion Control Conference Exhibition (1996), Budapest, Hungary, vol. 3, pp. 448–452 (1996)Google Scholar
  38. 38.
    Lomonova, E.A., Miziurin, S.R., Klaassens, J.B.: Brushless Machines as Electrome-chanical Actuators for Flight Control Systems. In: Proc. 7th International Power Electronics & Motion Control Conference Exhibition, Budapest, Hungary, vol. 2, pp. 627–631 (1996)Google Scholar
  39. 39.
    Vaščák, J., Madarász, L.: Adaptation of fuzzy cognitive maps - a comparison study. Acta Polytechnica Hungarica 7, 109–122 (2010)Google Scholar
  40. 40.
    Preitl, S., Precup, R.-E., Preitl, Z.: Control structures and algorithms, vol. 1, 2. Editura Orizonturi Universitare, Timisoara (2009) (in Romanian)Google Scholar
  41. 41.
    Cao, W., Yeng, C.S., Chakravarthy, V.K.: An integrated nonlinear observer with sliding mode estimation for a class of nonlinear uncertain systems. In: Proc. 42nd IEEE Conference on Decision and Control, pp. 5741–5746 (2003)Google Scholar
  42. 42.
    Zhang, J., Shi, P., Xia, Y.: Robust adaptive sliding-mode control for fuzzy systems with mismatched uncertainties. IEEE Trans. Fuzzy Syst. 18, 700–711 (2010)CrossRefGoogle Scholar
  43. 43.
    Utkin, V., Guldner, J., Shi, J.: Sliding Mode Control in Electro-mechanical Systems. CRC Press, Boca Raton (2009)CrossRefGoogle Scholar
  44. 44.
    Baldursson, S.: BLDC motor modelling and control - A Matlab/Simulink implementation. M.Sc. Thesis, Institutionen för Energi och Miljö, Göteborg, Sweden (2005)Google Scholar
  45. 45.
    Nasar, S.A., Boldea, I.: Electric Drives, 2nd edn. CRC Press (2005)Google Scholar
  46. 46.
    Dixon, J.W., Real, I.: Current control strategy for brushless dc motors based on a common DC signal. IEEE Trans. Power Electron. 17, 232–240 (2002)CrossRefGoogle Scholar
  47. 47.
    Yasuhiko, D.: Servo Motor and Motion control Using Digital Signal Processors, Texas Instruments. Prentice Hall, Englewood Cliffs (1990)Google Scholar
  48. 48.
    Bauer, P., Preitl, Z., Gaspar, P., Szabo, Z., Bokor, J.: Modelling of a Series Hybrid Electric Vehicle. In: Workshop on Hybrid Electric Vehicle Modelling and Control, Istanbul, Turkey (2007)Google Scholar
  49. 49.
    Preitl, Z., Kulcsár, B., Bokor, J.: Mathematical Models of a Hybrid Electric Vehicles. In: Proc. 8th International Conference on Technical Informatics (CONTI 2008), Timisoara, Romania, 6 p. (2008)Google Scholar
  50. 50.
    Tsai, T.-C., Tsai, M.-C.: Power Control of a Brushless Permanent Magnet Electric Machine for Exercise Bikes. In: Proc. 15th IFAC Triennial World Congress, Barcelona, Spain, 6 p. (2002)Google Scholar
  51. 51.
    Workshop Papers, Workshop on Hybrid Solar Vehicles, Salerno, Italy (2006)Google Scholar
  52. 52.
    Preitl, Z., Bauer, P., Bokor, J.: Cascade control solution for traction motor for hybrid electric vehicles. Acta Polytechnica Hungarica 4(3), 75–88 (2007)Google Scholar
  53. 53.
    ECP M 220, Industrial emulator/servo trainer model 220 system, testbed for practical control training, Bell Canyon, CA, USA. Educational Control Products (2010)Google Scholar
  54. 54.
    Musardo, C., Rizzoni, C., Guezennec, Y., Staccia, B.: A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management. European Journal of Control 11, 509–524 (2005)CrossRefMathSciNetGoogle Scholar
  55. 55.
    Tsai, T.-C., Tsai, M.-C.: Power Control of a Brushless Permanent Magnet Electric Machine for Exercise Bikes. In: Proc. 15th IFACT Triennial World Congress, Barcelona, Spain (2002)Google Scholar
  56. 56.
    Precup, R.-E., Preitl, S.: On a hybrid PI-neuro-fuzzy controller meant for a class of non-minimum phase systems. In: Proc. 7th European Congress on Intelligent Technologies and Soft Computing (EUFIT 1999), Aachen, Germany, 6 p. (1999)Google Scholar
  57. 57.
    Precup, R.-E., David, R.-C., Petriu, E.-M., Rădac, M.-B., Preitl, S., Fodor, J.: Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems. Knowledge-Based Systems 38, 74–84 (2013)CrossRefGoogle Scholar
  58. 58.
    Stînean, A.-I., Preitl, S., Precup, R.-E., Dragoş, C.-A., Rădac, M.-B., Crainic, M.: Adaptable fuzzy control solutions for driving systems working under continuously variable conditions. In: Proc. 14th IEEE International Symposium on Computational Intelligence and Informatics (CINTI 2013), Budapest, Hungary, pp. 231–237 (2013)Google Scholar
  59. 59.
    Preitl, S., Stinean, A.-I., Precup, R.-E., Preitl, Z., Petriu, E.-M., Drago, C.-A., Rãdac, M.-B.: Controller design methods for driving systems based on extensions of symmetrical optimum method with DC and BLDC Motor Applications. In: Proc. IFAC Conference on Advances in PID Control (PID 2012), Brescia, Italy, pp. 264–269 (2012)Google Scholar
  60. 60.
    Stinean, A.-I., Preitl, S., Precup, R.-E., Dragos, C.-A., Petriu, E.M., Radac, M.-B.: Choosing a Proper Control structure for a mechatronic system with variable parameters. In: Proc. 2nd IFAC Workshop on Convergence of Information Technologies and Control Methods with Power Systems (ICPS 2013), Cluj-Napoca, Romania, pp. 29–34 (2013)Google Scholar
  61. 61.
    Precup, R.-E., Preitl, S.: On some low cost hybrid PI-neuro-fuzzy controllers for the second-order “right half plane zero” system. In: Proc. 13th CSCS Conference, pp. 170–175. Editura Politehnica Press, Bucharest (2001)Google Scholar
  62. 62.
    Stînean, A.-I., Preitl, S., Precup, R.-E., Petriu, E.-M., Dragoş, A.-C., Rădac, B.-M.: Solutions for avoiding the worst case scenario in driving system working under continuously variable conditions. In: Proc. IEEE 9th International Conference on Computational Cybernetics (ICCC 2013), Tihany, Hungary, pp. 339–344 (2013)Google Scholar
  63. 63.
    Stînean, A.-I., Preitl, S., Precup, R.-E., Dragoş, A.-C., Petriu, E.-M., Rădac, B.-M.: Low-Cost Neuro-Fuzzy Control Solution for Servo Systems with Variable Parameters. In: Proc. 2013 IEEE International Conference on Computational Intelligence and Virtual Environements for Measurement Systems and Applications (CIVEMSA 2013), Milano, Italy, pp. 156–161 (2013)Google Scholar
  64. 64.
    Precup, R.-E., Preitl, S.: Popov-type stability analysis method for fuzzy control systems. In: Proc. Fifth EUFIT 1997 European Congress, Aachen, Germany, pp. 1306–1310 (1997)Google Scholar
  65. 65.
    Carlsson, C., Fullér, R.: Optimization under fuzzy if-then rules. Fuzzy Sets and Systems 119(1), 111–120 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  66. 66.
    Baranyi, P., Tikk, D., Yam, Y., Patton, R.J.: From differential equations to PDC controller design via numerical transformation. Computers in Industry 51(3), 281–297 (2003)CrossRefGoogle Scholar
  67. 67.
    Tar, J.K., Rudas, I.J., Bitó, J.F., Horváth, L., Kozlowski, K.: Analysis of the effect of backlash and joint acceleration measurement noise in the adaptive control of electro-mechanical systems. In: Proc. 2003 IEEE International Symposium on Industrial Electronics (ISIE 2003), Rio de Janeiro, Brazil, vol. 1, pp. 286–291 (2003)Google Scholar
  68. 68.
    Tanelli, M., Sartori, R., Savaresi, S.M.: Combining slip and deceleration control for brake-by-wire control systems: A sliding-mode approach. Eur. J. Control 13(6), 593–611 (2007)CrossRefMathSciNetGoogle Scholar
  69. 69.
    Rudas, I.J., Fodor, J.: Intelligent systems. International Journal of Computers, Communications & Control 3, 132–138 (2008)Google Scholar
  70. 70.
    Blažič, S.: A novel trajectory-tracking control law for wheeled mobile robots. Robotics and Autonomous Systems 59(11), 1001–1007 (2011)CrossRefGoogle Scholar
  71. 71.
    Tikk, D., Johanyák, Z.C., Kovács, S., Wong, K.W.: Fuzzy rule interpolation and extrapolation techniques: criteria and evaluation guidelines. Journal of Advanced Computational Intelligence and Intelligent Informatics 15(3), 254–263 (2011)Google Scholar
  72. 72.
    Angelov, P., Yager, R.: A new type of simplified fuzzy rule-based systems. International Journal of General Systems 41(2), 163–185 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  73. 73.
    Melin, P., Castillo, O.: A review on the applications of type-2 fuzzy logic in classification and pattern recognition. Expert Syst. Appl. 40(13), 5413–5423 (2013)CrossRefGoogle Scholar
  74. 74.
    Petra, M.I., De Silva, L.C.: Implementation of folding architecture neural networks into an FPGA for an optimized inverse kinematics solution of a six-legged robot. International Journal of Artificial Intelligence 10(S13), 123–138 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Stefan Preitl
    • 1
    Email author
  • Radu-Emil Precup
    • 1
  • Zsuzsa Preitl
    • 2
  • Alexandra-Iulia Stînean
    • 1
  • Mircea-Bogdan Rădac
    • 1
  • Claudia-Adina Dragoş
    • 1
  1. 1.Department of Automation and Applied InformaticsPolitehnica University TimisoaraTimisoaraRomania
  2. 2.Siemens AGErlangenGermany

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