Fuzzy controller, designed by reinforcement learning, for vehicle traction system application

Simple item page
dc.citation.epage183
dc.citation.issue2
dc.citation.spage168
dc.contributor.affiliationНаціональний університет “Львівська політехніка”
dc.contributor.affiliationУніверситет Алабами у Бірмінгемі
dc.contributor.affiliationЦентр систем наземного транспорту армії США
dc.contributor.affiliationЦентр систем наземного транспорту, Наука і техніка Аліон
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.affiliationUniversity of Alabama at Birmingham
dc.contributor.affiliationUS Army CCDC Ground Vehicle Systems Center
dc.contributor.affiliationAlion Science and Technilogy, Ground Vehicle Systems Center
dc.contributor.authorДемків, Л. І.
dc.contributor.authorЛозинський, А. О.
dc.contributor.authorВанцевич, В. В.
dc.contributor.authorГорсіч, Д. Дж.
dc.contributor.authorЛитвин, В. В.
dc.contributor.authorКльось, С. Р.
dc.contributor.authorЛезервуд, М. Д.
dc.contributor.authorDemkiv, L. I.
dc.contributor.authorLozynskyy, A. O.
dc.contributor.authorVantsevich, V. V.
dc.contributor.authorGorsich, D. J.
dc.contributor.authorLytvyn, V. V.
dc.contributor.authorKlos, S. R.
dc.contributor.authorLetherwood, M. D.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2023-10-24T07:21:52Z
dc.date.available2023-10-24T07:21:52Z
dc.date.created2021-03-01
dc.date.issued2021-03-01
dc.description.abstractУ цій статті представлено нечіткий регулятор, що налаштовується методом навчання з підкріпленням. Розроблений алгоритм використовує теорію нечіткої логіки та методи навчання з підкріпленням для підбору параметрів функцій належності нечіткого регулятора. Крім цього, імплементовано нечіткий задавач інтенсивності вхідного сигналу (сигналу завдання) нечіткого регулятора. Нечіткий задавач інтенсивності змінює вхідний сигнал регулятора враховуючи оригінальне значення вхідного сигналу та тип зовнішніх збурень у системі. Таким чином, розроблена система керування з нечітким регулятором налаштованим за допомогою методу навчання з підкріпленням забезпечує стабільну, оптимальну та безпечну роботу системи, та враховує зовнішні збурення в системі. Для перевірки роботи запропонованого методу керування, його було синтезовано до математичної моделі колісного модуля електроавтомобіля, щоб покращити антибуксувальну систему транспортного засобу. Ефективність розробленої системи керування на базі нечіткого регулятора підтверджено результатами імітаційного моделювання.
dc.description.abstractIn this article, a fuzzy controller tuned by reinforcement learning is proposed. The developed algorithm utilizes a fuzzy logic theory and a reinforcement learning for fine-tuning parameters of the membership function for the fuzzy controller. Apart from the fuzzy controller developed, a fuzzy corrector of reference input (set-point) signal to the controller is applied. The fuzzy corrector changes the input (reference) signal of the system and takes into account an original reference input and type of external disturbances. Thus, the designed fuzzy control that is tuned by reinforcement learning is capable to ensure the stable, optimal, and safe performance of the system and takes into account external disturbances. To verify the performance of the proposed controller, the adaptive fuzzy controller tuned by reinforcement learning is applied to the mathematical model of a wheel locomotion module of an electric vehicle to advance a traction control system. Therefore, the effectiveness of the proposed adaptive fuzzy controller is proven through the simulation results.
dc.format.extent168-183
dc.format.pages16
dc.identifier.citationFuzzy controller, designed by reinforcement learning, for vehicle traction system application / L. I. Demkiv, A. O. Lozynskyy, V. V. Vantsevich, D. J. Gorsich, V. V. Lytvyn, S. R. Klos, M. D. Letherwood // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 8. — No 2. — P. 168–183.
dc.identifier.citationenFuzzy controller, designed by reinforcement learning, for vehicle traction system application / L. I. Demkiv, A. O. Lozynskyy, V. V. Vantsevich, D. J. Gorsich, V. V. Lytvyn, S. R. Klos, M. D. Letherwood // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 8. — No 2. — P. 168–183.
dc.identifier.doidoi.org/10.23939/mmc2021.02.168
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/60391
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofMathematical Modeling and Computing, 2 (8), 2021
dc.relation.references[1] Zhang Y., Li S., Liao L. Near-optimal control of nonlinear dynamical systems: A brief survey. Annual Reviews in Control. 47, 71–80 (2019).
dc.relation.references[2] Grune L., Pannek J. Nonlinear Model Predictive Control. Communications and Control Engineering. Springer, Cham (2017).
dc.relation.references[3] Bououden S., Chadli M., Karimi H. R. A Robust Predictive Control Design for Nonlinear Active Suspension Systems. Asian Journal of Control. 18, 122–132 (2016).
dc.relation.references[4] Shin Y. C., Xu C. Intelligent systems: modeling, optimization, and control. CRC press (2009).
dc.relation.references[5] Cervantes J, Yu W., Salazar S., Chairez I. Takagi–Sugeno Dynamic Neuro-Fuzzy Controller of Uncertain Nonlinear Systems. IEEE Transactions on Fuzzy Systems. 25 (6), 1601–1615 (2017).
dc.relation.references[6] Vantsevich V., Lozynskyy A., Demkiv L., Klos S. A Foundation for Real-Time Tire Mobility Estimation and Control. Proc. 19th International and 14th European-African Regional Conference of the ISTVS, Budapest, Hungary (2017).
dc.relation.references[7] Dawei M., Yu Z., Meilan Z., Risha N. Intelligent fuzzy energy management research for a uniaxial parallel hybrid electric vehicle. Computers and Electrical Engineering. 58, 447–464 (2017).
dc.relation.references[8] Arabi E., Gruenwald B. C., Yucelen T., Nguyen N. Intelligent fuzzy energy management research for a uniaxial parallel hybrid electric vehicle. International Journal of Control. 91 (5), 1195–1208 (2018).
dc.relation.references[9] Dorf R. C., Bishop R. H. Modern control systems. Pearson (2016).
dc.relation.references[10] Behrooz F., Mariun N., Marhaban M. H., Radzi M., Amran M., Ramli A. R. Review of control techniques for HVAC systems—nonlinearity approaches based on Fuzzy cognitive maps. Energies. 11 (3), 495 (2018).
dc.relation.references[11] Lozynskyy A., Demkiv L. Application of dynamic systems family for synthesis of fuzzy control with account of non-linearities. Advances in electrical and electronic engineering. 14 (5), 543–550 (2016).
dc.relation.references[12] Demkiv L. I. Research of dynamic system with unstable subsystem that has one root in the right half-plane. Mathematical modeling and computing. 1 (2), 156–162 (2014).
dc.relation.references[13] Andreev A. F., Kabanau V., Vantsevich V. Driveline systems of ground vehicles: theory and design. CRC Press (2010).
dc.relation.references[14] Lozynskyy A. O., Demkiv L. I., Vantsevich V. V., Borovets T. V., Gorsich D. J. An estimation accuracy of state observers under uncertain initial conditions. Mathematical modeling and computing. 6 (2), 320–332 (2019).
dc.relation.references[15] Savitski D., Schleinin D., Ivanov V., Augsburg K., Jimenez E., He R., Barber P. Improvement of traction performance and off-road mobility for a vehicle with four individual electric motors: driving over icy road. Journal of Terramechanics. 69, 33–43 (2017).
dc.relation.references[16] Osinenko P. V., Geissler M., Herlitzius T. A method of optimal traction control for farm tractors with feedback of drive torque. Biosystems engineering. 129, 20–33 (2015).
dc.relation.references[17] Kim J., Lee J. Traction-energy balancing adaptive control with slip optimization for wheeled robots on rough terrain. Cognitive Systems Research. 49, 142–156 (2018).
dc.relation.references[18] Addison A., Vacca A. Real-Time Parameter Setpoint Optimization for Electro-Hydraulic Traction Control Systems. Proc. 15th Scandinavian International Conference on Fluid Power, Link¨oping, Sweden. 144, 104–114 (2017).
dc.relation.references[19] Sutton R. S., Barto A. G. Reinforcement learning: An introduction. MIT press (2018).
dc.relation.references[20] Tay T. T., Mareels I., Moore J. B. High performance control. Springer Science and Business Media (2012).
dc.relation.references[21] Lozynskyy A., Demkiv L. Synthesis of multicriteria controller by means of fuzzy logic approach. Advances in Fuzzy Systems. 2014, Article ID 758207 (2014).
dc.relation.references[22] Vantsevich V. V., Lozynskyy A., Demkiv L., Holovach I. Fuzzy logic control of agile dynamics of a wheel locomotion module. Dynamics of Vehicles on Roads and Tracks 1: Proc. 25th International Symposium on Dynamics of Vehicles on Roads and Tracks, Rockhampton, Queensland, Australia. CRC Press (2018).
dc.relation.references[23] Chudakov E. A. Theory of Automobile. State Publishing House of Machine-Building Literature, Moscow, Russia (1950), (in Russian).
dc.relation.references[24] Bekker M. G. Introduction to Terrain-Vehicle Systems. Michigan University Ann Arbor (1969).
dc.relation.references[25] Kutzbach H. D., B¨urger A., Bottinger S. Rolling radii and moment arm of the wheel load for pneumatic tyres. Journal of Terramechanics. 82, 13–21 (2019).
dc.relation.references[26] Wong J. Y. Terramechanics and off-road vehicles. Elsevier (1989).
dc.relation.references[27] Gray J. P., Vantsevich V. V., Opeiko A. F., Hudas G. R. A Method for Unmanned Ground Wheeled Vehicle Mobility Estimation in Stochastic Terrain Conditions. Proc. 7th Americas Regional Conference of the ISTVS, Tampa, Florida, USA (2013).
dc.relation.references[28] Gray J. P., Vantsevich V. V., Overholt J. L. Indices and Computational Strategy for Unmanned Ground Wheeled Vehicle Mobility Estimation and Enhancement. Proceedings of the ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 6A: 37th Mechanisms and Robotics Conference. Portland, Oregon, USA. August 4–7, 2013. ASME Paper No. DETC2013-12158 (2014).
dc.relation.references[29] Vantsevich V., Gorsich D., Lozynskyy A., Demkiv L., Borovets T. State Observers for Terrain Mobility Controls: A Technical Analysis. Uhl T. (eds) Advances in Mechanism and Machine Science, IFToMM WC 2019, Mechanisms and Machine Science. 73, Springer, Cham (2019).
dc.relation.referencesen[1] Zhang Y., Li S., Liao L. Near-optimal control of nonlinear dynamical systems: A brief survey. Annual Reviews in Control. 47, 71–80 (2019).
dc.relation.referencesen[2] Grune L., Pannek J. Nonlinear Model Predictive Control. Communications and Control Engineering. Springer, Cham (2017).
dc.relation.referencesen[3] Bououden S., Chadli M., Karimi H. R. A Robust Predictive Control Design for Nonlinear Active Suspension Systems. Asian Journal of Control. 18, 122–132 (2016).
dc.relation.referencesen[4] Shin Y. C., Xu C. Intelligent systems: modeling, optimization, and control. CRC press (2009).
dc.relation.referencesen[5] Cervantes J, Yu W., Salazar S., Chairez I. Takagi–Sugeno Dynamic Neuro-Fuzzy Controller of Uncertain Nonlinear Systems. IEEE Transactions on Fuzzy Systems. 25 (6), 1601–1615 (2017).
dc.relation.referencesen[6] Vantsevich V., Lozynskyy A., Demkiv L., Klos S. A Foundation for Real-Time Tire Mobility Estimation and Control. Proc. 19th International and 14th European-African Regional Conference of the ISTVS, Budapest, Hungary (2017).
dc.relation.referencesen[7] Dawei M., Yu Z., Meilan Z., Risha N. Intelligent fuzzy energy management research for a uniaxial parallel hybrid electric vehicle. Computers and Electrical Engineering. 58, 447–464 (2017).
dc.relation.referencesen[8] Arabi E., Gruenwald B. C., Yucelen T., Nguyen N. Intelligent fuzzy energy management research for a uniaxial parallel hybrid electric vehicle. International Journal of Control. 91 (5), 1195–1208 (2018).
dc.relation.referencesen[9] Dorf R. C., Bishop R. H. Modern control systems. Pearson (2016).
dc.relation.referencesen[10] Behrooz F., Mariun N., Marhaban M. H., Radzi M., Amran M., Ramli A. R. Review of control techniques for HVAC systems-nonlinearity approaches based on Fuzzy cognitive maps. Energies. 11 (3), 495 (2018).
dc.relation.referencesen[11] Lozynskyy A., Demkiv L. Application of dynamic systems family for synthesis of fuzzy control with account of non-linearities. Advances in electrical and electronic engineering. 14 (5), 543–550 (2016).
dc.relation.referencesen[12] Demkiv L. I. Research of dynamic system with unstable subsystem that has one root in the right half-plane. Mathematical modeling and computing. 1 (2), 156–162 (2014).
dc.relation.referencesen[13] Andreev A. F., Kabanau V., Vantsevich V. Driveline systems of ground vehicles: theory and design. CRC Press (2010).
dc.relation.referencesen[14] Lozynskyy A. O., Demkiv L. I., Vantsevich V. V., Borovets T. V., Gorsich D. J. An estimation accuracy of state observers under uncertain initial conditions. Mathematical modeling and computing. 6 (2), 320–332 (2019).
dc.relation.referencesen[15] Savitski D., Schleinin D., Ivanov V., Augsburg K., Jimenez E., He R., Barber P. Improvement of traction performance and off-road mobility for a vehicle with four individual electric motors: driving over icy road. Journal of Terramechanics. 69, 33–43 (2017).
dc.relation.referencesen[16] Osinenko P. V., Geissler M., Herlitzius T. A method of optimal traction control for farm tractors with feedback of drive torque. Biosystems engineering. 129, 20–33 (2015).
dc.relation.referencesen[17] Kim J., Lee J. Traction-energy balancing adaptive control with slip optimization for wheeled robots on rough terrain. Cognitive Systems Research. 49, 142–156 (2018).
dc.relation.referencesen[18] Addison A., Vacca A. Real-Time Parameter Setpoint Optimization for Electro-Hydraulic Traction Control Systems. Proc. 15th Scandinavian International Conference on Fluid Power, Link¨oping, Sweden. 144, 104–114 (2017).
dc.relation.referencesen[19] Sutton R. S., Barto A. G. Reinforcement learning: An introduction. MIT press (2018).
dc.relation.referencesen[20] Tay T. T., Mareels I., Moore J. B. High performance control. Springer Science and Business Media (2012).
dc.relation.referencesen[21] Lozynskyy A., Demkiv L. Synthesis of multicriteria controller by means of fuzzy logic approach. Advances in Fuzzy Systems. 2014, Article ID 758207 (2014).
dc.relation.referencesen[22] Vantsevich V. V., Lozynskyy A., Demkiv L., Holovach I. Fuzzy logic control of agile dynamics of a wheel locomotion module. Dynamics of Vehicles on Roads and Tracks 1: Proc. 25th International Symposium on Dynamics of Vehicles on Roads and Tracks, Rockhampton, Queensland, Australia. CRC Press (2018).
dc.relation.referencesen[23] Chudakov E. A. Theory of Automobile. State Publishing House of Machine-Building Literature, Moscow, Russia (1950), (in Russian).
dc.relation.referencesen[24] Bekker M. G. Introduction to Terrain-Vehicle Systems. Michigan University Ann Arbor (1969).
dc.relation.referencesen[25] Kutzbach H. D., B¨urger A., Bottinger S. Rolling radii and moment arm of the wheel load for pneumatic tyres. Journal of Terramechanics. 82, 13–21 (2019).
dc.relation.referencesen[26] Wong J. Y. Terramechanics and off-road vehicles. Elsevier (1989).
dc.relation.referencesen[27] Gray J. P., Vantsevich V. V., Opeiko A. F., Hudas G. R. A Method for Unmanned Ground Wheeled Vehicle Mobility Estimation in Stochastic Terrain Conditions. Proc. 7th Americas Regional Conference of the ISTVS, Tampa, Florida, USA (2013).
dc.relation.referencesen[28] Gray J. P., Vantsevich V. V., Overholt J. L. Indices and Computational Strategy for Unmanned Ground Wheeled Vehicle Mobility Estimation and Enhancement. Proceedings of the ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 6A: 37th Mechanisms and Robotics Conference. Portland, Oregon, USA. August 4–7, 2013. ASME Paper No. DETC2013-12158 (2014).
dc.relation.referencesen[29] Vantsevich V., Gorsich D., Lozynskyy A., Demkiv L., Borovets T. State Observers for Terrain Mobility Controls: A Technical Analysis. Uhl T. (eds) Advances in Mechanism and Machine Science, IFToMM WC 2019, Mechanisms and Machine Science. 73, Springer, Cham (2019).
dc.rights.holder© Національний університет “Львівська політехніка”, 2021
dc.subjectнавчання з підкріпленням
dc.subjectнечіткий регулятор
dc.subjectантибуксувальна система
dc.subjectмобільність транспортного засобу
dc.subjectreinforcement learning
dc.subjectfuzzy logic controller
dc.subjecttraction control
dc.subjectvehicle mobility
dc.titleFuzzy controller, designed by reinforcement learning, for vehicle traction system application
dc.title.alternativeНечіткий регулятор, синтезований методом навчання з підкріпленням, для застосування у антибуксувальній системі автомобіля
dc.typeArticle

Files

Original bundle

Now showing 1 - 2 of 2
Thumbnail Image
Name:
2021v8n2_Demkiv_L_I-Fuzzy_controller_designed_168-183.pdf
Size:
1.77 MB
Format:
Adobe Portable Document Format
Download
Thumbnail Image
Name:
2021v8n2_Demkiv_L_I-Fuzzy_controller_designed_168-183__COVER.png
Size:
384.58 KB
Format:
Portable Network Graphics
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2 KB
Format:
Plain Text
Description:
Download

Collections

Mathematical Modeling And Computing. – 2021. – Vol. 8, No. 2