Control of Semi-active Suspension System Using Kalman Observer
Main Article Content
Abstract
Optimal control methods are increasingly used in automatic control systems, especially in automotive suspension system. However, the optimal control algorithm only achieves the highest efficiency in suspension control system when the required number of sensors is sufficient, corresponding to the number of states in the system. The arrangement of sufficient number of sensors depends on the capacity, economic conditions and responsiveness of the sensor. The Kalman observer is designed to reliably estimate the required parameters in the control where the number of sensors is limited. The article focuses on analyzing the theory of building a quarter-car model, developing and determining the optimal control matrix, the Kalman observer design method. The findings of the article reveal the effectiveness of automotive body vibration suppression and the required force for control corresponding to different control algorithms, under the influence of square pulse road surface, when using the different sensor types, thereby providing a choice of sensor type and the location on the semi-active ¼ suspension.
Keywords
LQR, LQG, observer, Kalman, a quarter-car model
Article Details
References
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[2] Pawar, Shital M., A. A. Panchwadkar, Estimation of state variables of active suspension system using Kalman filter, International Journal of Current Engineering and Technology EISSN 2017: 2277-4106.
[3] Lindgärde, Olof. Kalman filtering in semi-active suspension control, IFAC Proceedings, Vol. 35.1 2002, pp. 439-444.
[4] Zuohai Yan Shuqi Zhao, Road condition predicting with kalman filter for magneto-rheological damper in suspension system, Blekinge Institute of Technology, 2012
[5] Jonathan Daniel Ziegenmeyer, Estimation of disturbance inputs to a tire coupled quarter-car suspension test rig, Master Thesis, Virginia Polytechnic Institute and State University, 2007.
[6] Rahman M, Rideout G, Using the lead vehicle as preview sensor in convoy vehicle active suspension control, Vehicle System Dynamics, Vol. 50, Issue 12, 2012, p. 1923-1948.
[7] Ying Fan, Hongbin Ren, Sizhong Chen, Yuzhuang Zhao, Observer design based on nonlinear suspension model with unscented Kalman filter, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China, 2015
https://doi.org/10.1080/00423114.2012.707801
[2] Pawar, Shital M., A. A. Panchwadkar, Estimation of state variables of active suspension system using Kalman filter, International Journal of Current Engineering and Technology EISSN 2017: 2277-4106.
[3] Lindgärde, Olof. Kalman filtering in semi-active suspension control, IFAC Proceedings, Vol. 35.1 2002, pp. 439-444.
[4] Zuohai Yan Shuqi Zhao, Road condition predicting with kalman filter for magneto-rheological damper in suspension system, Blekinge Institute of Technology, 2012
[5] Jonathan Daniel Ziegenmeyer, Estimation of disturbance inputs to a tire coupled quarter-car suspension test rig, Master Thesis, Virginia Polytechnic Institute and State University, 2007.
[6] Rahman M, Rideout G, Using the lead vehicle as preview sensor in convoy vehicle active suspension control, Vehicle System Dynamics, Vol. 50, Issue 12, 2012, p. 1923-1948.
[7] Ying Fan, Hongbin Ren, Sizhong Chen, Yuzhuang Zhao, Observer design based on nonlinear suspension model with unscented Kalman filter, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China, 2015
https://doi.org/10.1080/00423114.2012.707801