Intelligent control of braking process
Само за регистроване кориснике
2012
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Intelligent modeling, prediction and control of the braking process are not an easy task if using classical modeling techniques, regarding its complexity. In this paper, the new approach has been proposed for easy and effective monitoring, modeling, prediction, and control of the braking process i.e. the brake performance during a braking cycle. The context based control of the disc brake actuation pressure was used for improving the dynamic control of braking process versus influence of the previous and current values of the disc brake actuation pressure, the vehicle speed, and the brake interface temperature. For these purposes, two different dynamic neural models have been developed and integrated into the microcontroller. Microcontrollers are resource intensive and cost effective platforms that offer possibilities to associate with commonly used artificial intelligence techniques. The neural models, based on recurrent dynamic neural networks, are implemented in 8-bit CMOS microcont...roller for control of the disc brake actuation pressure during a braking cycle. The first neural model was used for modeling and prediction of the braking process output (braking torque). Based on such acquired knowledge about the real brake operation, the inverse neural model has been developed which was able to predict the brake actuation pressure needed for achieving previously selected (desired) braking torque value in accordance with the previous and current influence of the pressure, speed, and the brake interface temperature. Both neural models have had inherent abilities for on-line learning and prediction during each braking cycle and an intelligent adaptation to the change of influences of pressure, speed, and temperature on the braking process.
Кључне речи:
Microcontroller / Intelligent control / Braking processИзвор:
Expert Systems With Applications, 2012, 39, 14, 11758-11765Издавач:
- Pergamon-Elsevier Science Ltd, Oxford
Финансирање / пројекти:
- Научно-технолошка подршка унапређењу безбедности специјалних друмских и шинских возила (RS-MESTD-Technological Development (TD or TR)-35045)
- Развој, пројектовање и имплементација савремених стратегија интегрисаног управљања оперативним радом и одржавањем возила и механизације у системима аутотранспорта, рударства и енергетике (RS-MESTD-Technological Development (TD or TR)-35030)
- Интелигентни роботски системи за екстремно диверзификовану производњу (RS-MESTD-Technological Development (TD or TR)-35007)
DOI: 10.1016/j.eswa.2012.04.076
ISSN: 0957-4174
WoS: 000305597700026
Scopus: 2-s2.0-84861822455
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Aleksendrić, Dragan AU - Jakovljević, Živana AU - Ćirović, Velimir PY - 2012 UR - https://machinery.mas.bg.ac.rs/handle/123456789/1495 AB - Intelligent modeling, prediction and control of the braking process are not an easy task if using classical modeling techniques, regarding its complexity. In this paper, the new approach has been proposed for easy and effective monitoring, modeling, prediction, and control of the braking process i.e. the brake performance during a braking cycle. The context based control of the disc brake actuation pressure was used for improving the dynamic control of braking process versus influence of the previous and current values of the disc brake actuation pressure, the vehicle speed, and the brake interface temperature. For these purposes, two different dynamic neural models have been developed and integrated into the microcontroller. Microcontrollers are resource intensive and cost effective platforms that offer possibilities to associate with commonly used artificial intelligence techniques. The neural models, based on recurrent dynamic neural networks, are implemented in 8-bit CMOS microcontroller for control of the disc brake actuation pressure during a braking cycle. The first neural model was used for modeling and prediction of the braking process output (braking torque). Based on such acquired knowledge about the real brake operation, the inverse neural model has been developed which was able to predict the brake actuation pressure needed for achieving previously selected (desired) braking torque value in accordance with the previous and current influence of the pressure, speed, and the brake interface temperature. Both neural models have had inherent abilities for on-line learning and prediction during each braking cycle and an intelligent adaptation to the change of influences of pressure, speed, and temperature on the braking process. PB - Pergamon-Elsevier Science Ltd, Oxford T2 - Expert Systems With Applications T1 - Intelligent control of braking process EP - 11765 IS - 14 SP - 11758 VL - 39 DO - 10.1016/j.eswa.2012.04.076 ER -
@article{ author = "Aleksendrić, Dragan and Jakovljević, Živana and Ćirović, Velimir", year = "2012", abstract = "Intelligent modeling, prediction and control of the braking process are not an easy task if using classical modeling techniques, regarding its complexity. In this paper, the new approach has been proposed for easy and effective monitoring, modeling, prediction, and control of the braking process i.e. the brake performance during a braking cycle. The context based control of the disc brake actuation pressure was used for improving the dynamic control of braking process versus influence of the previous and current values of the disc brake actuation pressure, the vehicle speed, and the brake interface temperature. For these purposes, two different dynamic neural models have been developed and integrated into the microcontroller. Microcontrollers are resource intensive and cost effective platforms that offer possibilities to associate with commonly used artificial intelligence techniques. The neural models, based on recurrent dynamic neural networks, are implemented in 8-bit CMOS microcontroller for control of the disc brake actuation pressure during a braking cycle. The first neural model was used for modeling and prediction of the braking process output (braking torque). Based on such acquired knowledge about the real brake operation, the inverse neural model has been developed which was able to predict the brake actuation pressure needed for achieving previously selected (desired) braking torque value in accordance with the previous and current influence of the pressure, speed, and the brake interface temperature. Both neural models have had inherent abilities for on-line learning and prediction during each braking cycle and an intelligent adaptation to the change of influences of pressure, speed, and temperature on the braking process.", publisher = "Pergamon-Elsevier Science Ltd, Oxford", journal = "Expert Systems With Applications", title = "Intelligent control of braking process", pages = "11765-11758", number = "14", volume = "39", doi = "10.1016/j.eswa.2012.04.076" }
Aleksendrić, D., Jakovljević, Ž.,& Ćirović, V.. (2012). Intelligent control of braking process. in Expert Systems With Applications Pergamon-Elsevier Science Ltd, Oxford., 39(14), 11758-11765. https://doi.org/10.1016/j.eswa.2012.04.076
Aleksendrić D, Jakovljević Ž, Ćirović V. Intelligent control of braking process. in Expert Systems With Applications. 2012;39(14):11758-11765. doi:10.1016/j.eswa.2012.04.076 .
Aleksendrić, Dragan, Jakovljević, Živana, Ćirović, Velimir, "Intelligent control of braking process" in Expert Systems With Applications, 39, no. 14 (2012):11758-11765, https://doi.org/10.1016/j.eswa.2012.04.076 . .