지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Vol.41 No.3
- 발행연도
- 2024.3
- 수록면
- 223 - 230 (8page)
- DOI
- 10.7736/JKSPE.023.141
이용수
초록· 키워드
오류제보하기
In recent years, research on machine learning techniques that can be integrated with existing suspension control algorithms for enhanced control effects has advanced considerably. Machine learning, especially involving neural networks, often requires many samples, which makes maintaining robust performance in diverse, changing environments challenging. The present study applied reinforcement learning, which can generalize complex situations not previously encountered, to overcome this obstacle and is crucial for suspension control under varying road conditions. The effectiveness of the proposed control method was evaluated on different road conditions using the quarter-vehicle model. The impact of training data was assessed by comparing models trained under two distinct road conditions. In addition, a validation exercise on the performance of the control method that utilizes reinforcement learning demonstrated its potential for enhancing the adaptability and efficiency of suspension systems under various road conditions.
목차
1. 서론
2. 실험 구성
3. 실험 결과 및 분석
4. 결론
REFERENCES
참고문헌 (12)
참고문헌 신청
Ahmed, A. E.-N. S., Ali, A. S., Ghazaly, N. M., Abd el-Jaber, G., (2015), PID controller of active suspension system for a quarter car model, International Journal of Advances in Engineering & Technology, 8(6), 899-909.
Kumar, M. S., Vijayarangan, S., (2006), Design of LQR controller for active suspension system, International Journal of Engineering & Materials Sciences, 13(3), 173-179.
Ghoniem, M., Awad, T., Mokhiamar, O., (2020), Control of a new low-cost semi-active vehicle suspension system using artificial neural networks, Alexandria Engineering Journal, 59(5), 4013-4025.
Vidya, V., Dharmana, M. M., (2017), Model reference based intelligent control of an active suspension system for vehicles, Proceedings of the 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), 1-5.
Lee, D., Jin, S., Lee, C., (2022), Deep reinforcement learning of semi-active suspension controller for vehicle ride comfort, IEEE Transactions on Vehicular Technology, 72(1), 327-339.
Kumar, M. S., Vijayarangan, S., (2006), Design of LQR controller for active suspension system, International Journal of Engineering & Materials Sciences, 13(3), 173-179.
Ghoniem, M., Awad, T., Mokhiamar, O., (2020), Control of a new low-cost semi-active vehicle suspension system using artificial neural networks, Alexandria Engineering Journal, 59(5), 4013-4025.
Vidya, V., Dharmana, M. M., (2017), Model reference based intelligent control of an active suspension system for vehicles, Proceedings of the 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), 1-5.
Lee, D., Jin, S., Lee, C., (2022), Deep reinforcement learning of semi-active suspension controller for vehicle ride comfort, IEEE Transactions on Vehicular Technology, 72(1), 327-339.
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