Vehicle predictive control based on the recursive subspace identification method. (July 2015)
- Record Type:
- Journal Article
- Title:
- Vehicle predictive control based on the recursive subspace identification method. (July 2015)
- Main Title:
- Vehicle predictive control based on the recursive subspace identification method
- Authors:
- Ba, Tengyue
Guan, Xiqiang
Zhang, Jianwu - Abstract:
- Vehicle active control is an important technology for vehicle safety and performance. Most vehicle control systems are based on models, and the effects of model-based controllers are more dependent on the model parameters. In order to improve the adaptability and the veracity of a control system, a new vehicle predictive control method is proposed. Based on the recursive optimized version of the predictor-based subspace identification method, the vehicle model can be identified from input–output data. According to the predicted outputs and the optimal criterion, two predictive controllers are derived. One is called the recursive subspace predictive controller, and the other is called the recursive subspace predictive controller with integrator, which is an improved form to handle integrated white noise. Compared with the outputs from traditional predictive control, the predicted outputs in this paper are obtained directly from the subspace identification process at each step, which avoids the difficult process of solving Diophantine equations. In this paper, a simulation vehicle model, which is made up of a seven-degree-of-freedom vehicle model and a magic formula tyre model, is used to verify the effectiveness of the controllers. By numerical examples, it can be shown that the proposed methods not only are effective for vehicle control in the linear domain but also can track the desired values well and can improve the effect of vehicle control even when the lateral tyreVehicle active control is an important technology for vehicle safety and performance. Most vehicle control systems are based on models, and the effects of model-based controllers are more dependent on the model parameters. In order to improve the adaptability and the veracity of a control system, a new vehicle predictive control method is proposed. Based on the recursive optimized version of the predictor-based subspace identification method, the vehicle model can be identified from input–output data. According to the predicted outputs and the optimal criterion, two predictive controllers are derived. One is called the recursive subspace predictive controller, and the other is called the recursive subspace predictive controller with integrator, which is an improved form to handle integrated white noise. Compared with the outputs from traditional predictive control, the predicted outputs in this paper are obtained directly from the subspace identification process at each step, which avoids the difficult process of solving Diophantine equations. In this paper, a simulation vehicle model, which is made up of a seven-degree-of-freedom vehicle model and a magic formula tyre model, is used to verify the effectiveness of the controllers. By numerical examples, it can be shown that the proposed methods not only are effective for vehicle control in the linear domain but also can track the desired values well and can improve the effect of vehicle control even when the lateral tyre forces reach saturation. … (more)
- Is Part Of:
- Proceedings of the Institution of Mechanical Engineers. Volume 229:Number 8(2015:Aug.)
- Journal:
- Proceedings of the Institution of Mechanical Engineers
- Issue:
- Volume 229:Number 8(2015:Aug.)
- Issue Display:
- Volume 229, Issue 8 (2015)
- Year:
- 2015
- Volume:
- 229
- Issue:
- 8
- Issue Sort Value:
- 2015-0229-0008-0000
- Page Start:
- 1094
- Page End:
- 1109
- Publication Date:
- 2015-07
- Subjects:
- Vehicle active control -- subspace predictive control -- recursive subspace identification method -- vehicle model simulation
Mechanical engineering -- Congresses
Transportation engineering -- Congresses
629.2 - Journal URLs:
- http://pid.sagepub.com/ ↗
http://www.uk.sagepub.com/home.nav ↗
http://journals.pepublishing.com/content/119783 ↗ - DOI:
- 10.1177/0954407014555878 ↗
- Languages:
- English
- ISSNs:
- 0954-4070
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 6463.xml