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Implementation of fuzzy PID controller in cascade with anti-windup to real-scale test equipment for pavements

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the industry and academia, large-scale equipment has been developed, which requires control systems that provide safety and efficiency with the lowest possible energy consumption. In the industrial cascade control system, nested controllers have been a versatile tool for the control of large-scale equipment. Research shows that these types of controllers improve their performance with the integration of artificial intelligence algorithms and prevention methods against controller saturation. For this reason, this paper presents the development of a fuzzy proportional-integral-derivative (PID) controller in cascade with anti-windup (AW) for full-scale test equipment for pavements. In this study, the mathematical expressions for the equipment, the design of the controller and additional systems for comparison, simulation and analysis are developed. The main objective is to test the functionality of this type of nested controllers for these systems.
Słowa kluczowe
Rocznik
Strony
3--19
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr.
Twórcy
  • Faculty of Engineering Universidad Militar Nueva Granada Bogota, Colombia
  • Faculty of Engineering Universidad Militar Nueva Granada Bogota, Colombia
  • Faculty of Engineering Universidad Militar Nueva Granada Bogota, Colombia
Bibliografia
  • 1. Yang W., Li L., Efficiency evaluation of industrial waste gas control in China: A study based on data envelopment analysis (DEA) model, Journal of Cleaner Production, 179: 1–11, 2018.
  • 2. du Plessis L., Ulloa-Calderon A., Harvey J.T., Coetzee N.F., Accelerated pavement testing efforts using the Heavy Vehicle Simulator, International Journal of Pavement Research and Technology, 11(4): 327–338, 2018.
  • 3. Wersäll C., Nordfelt I., Larsson S., Resonant roller compaction of gravel in fullscale tests, Transportation Geotechnics, 14: 93–97, 2018.
  • 4. Illić B., Miloš M., Isaković J., Cascade nonlinear feedforward-feedback control of stagnation pressure in a supersonic blowdown wind tunnel, Measurement, 95: 424–438, 2017.
  • 5. Jia B., Mikalsen R., Smallbone A., Zuo Z., Feng H., Roskilly A.P., Piston motion control of a free-piston engine generator: A new approach using cascade control, Applied Energy, 179: 1166–1175, 2016.
  • 6. Chang W.-J., Qiao H.-Y., Ku C.-C., Sliding mode fuzzy control for nonlinear stochastic systems subject to pole assignment and variance constraint, Information Science, 432: 133–145, 2018.
  • 7. Naghibi S.R., Pirmohamadi A.A., Moosavian S.A.A., Fuzzy MTEJ controller with integrator for control of underactuated manipulators, Robotics and Computer-Integrated Manufacturing, 48: 93–101, 2017.
  • 8. Priyanka E.B., Maheswari C., Thangavel S., Online monitoring and control of flow rate in oil pipelines transportation system by using PLC based Fuzzy-PID controller, Flow Measurement and Instrumentation, 62: 144–151, 2018.
  • 9. Verma O.P., Manik G., Jain V.K., Simulation and control of a complex nonlinear dynamic behavior of multi-stage evaporator using PID and Fuzzy-PID controllers, Journal of Computational Science, 25: 238–251, 2018.
  • 10. Padhy S., Panda S., Mahapatra S., A modified GWO technique based cascade PI-PD controller for AGC of power systems in presence of plug in electric vehicles, International Journal of Engineering, Science and Technology, 20(2): 427–442, 2017.
  • 11. Camacho-Tauta J., Reyes-Ortiz O., da Fonseca A.V., Rios S., Cruz N., Rodrigues C., Full-scale evaluation in a fatigue track of a base course treated with geopolymers, Procedia Engineering, 143: 18–25, 2016.
  • 12. Acuńa-Bravo W., Canuto E., Agostani M., Bonadei M., Proportional electrohydraulic valves: An Embedded Model Control solution, Control Engineering Practice, 62: 22–35, 2017.
  • 13. Chen J., Zhu H., Zhang L., Sun Y., Research on fuzzy control of path tracking for underwater vehicle based on genetic algorithm optimization, Ocean Engineering, 156: 217–223, 2018.
  • 14. Wang Y., Jin Q., Zhang R., Improved fuzzy PID controller design using predictive functional control structure, ISA Transactions, 71: 354–363, 2017.
  • 15. Karakose M., Yetis H., Akin E., Sine-square embedded fuzzy sets versus type-2 fuzzy sets, Advanced Engineering Informatics, 36: 43–54, 2018.
  • 16. Asadi M., Optimized Mamdani fuzzy models for predicting the strength of intact rocks and anisotropic rock masses, Journal of Rock Mechanics and Geotechnical Engineering, 8(2): 218–224, 2016.
  • 17. Castillo O., Amador-Angulo L., Castro J.R., Garcia-Valdez M., A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems, Information Science, 354: 257–274, 2016.
  • 18. Adegbebe A. A., Heath W.P., A framework for multivariable algebraic loops in linear anti-windup implementations, Automatica, 83: 81–90, 2017.
  • 19. Turner M.C., Kerr M., A nonlinear modification for improving dynamic anti-windup compensation, European Journal of Control, 41: 44–52, 2018.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6bd2c555-321f-42d9-a380-268d17542919
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