On the use of parameterized NMPC in real-time automotive control

被引:0
作者
Alamir M. [1 ]
Murilo A. [1 ]
Amari R. [2 ]
Tona P. [2 ]
Fürhapter R. [3 ]
Ortner P. [3 ]
机构
[1] Gipsa-lab, CNRS-University of Grenoble. Domaine Universitaire, Saint-Martin d'Hères 38400
[2] IFP Powertrain Engineering, Vernaison 69390
[3] Johannes Kepler University, Linz
来源
Lecture Notes in Control and Information Sciences | 2010年 / 402卷
关键词
Economic and social effects - Control nonlinearities - Model predictive control - Computation theory - Predictive control systems - Computational efficiency - Diesel engines;
D O I
10.1007/978-1-84996-071-7_9
中图分类号
学科分类号
摘要
Automotive control applications are very challenging due to the presence of constraints, nonlinearities and the restricted amount of computation time and embedded facilities. Nevertheless, the need for optimal trade-off and efficient coupling between the available constrained actuators makes Nonlinear Model Predictive Control (NMPC) conceptually appealing. From a practical point of view however, this control strategy, at least in its basic form, involves heavy computations that are often incompatible with fast and embedded applications. Addressing this issue is becoming an active research topics in the worldwide NMPC community. The recent years witnessed an increasing amount of dedicated theories, implementation hints and software. The Control Parametrization Approach (CPA) is one option to address the problem. The present chapter positions this approach in the layout of existing alternatives, underlines its advantages and weaknesses. Moreover, its efficiency is shown through two real-world examples from the automotive industry, namely: the control of a diesel engine air path; and the Automated Manual Transmission (AMT)-control problem. In the first example, the CPA is applied to the BMW M47TUE Diesel engine available at Johannes Kepler University, Linz while in the second, a real world Smart hybrid demo car available at IFP is used. It is shown that for both examples, a suitably designed CPA can be used to solve the corresponding constrained problem while requiring few milliseconds of computation time per sampling period. © 2010 Springer-Verlag London.
引用
收藏
页码:139 / 149
页数:10
相关论文
共 20 条
[1]  
Alamir M., Nonlinear receding horizon sub-optimal guidance law for minimum interception time problem, Control Engineering Practice, 9, 1, pp. 107-116, (2001)
[2]  
Alamir M., Stabilization of nonlinear system using receding-horizon control schemes: A parametrized approach for Fast Systems, LNCIS, (2006)
[3]  
Alamir M., A framework for monitoring control updating period in real-time NMPC, Assessement and Future Direction in NMPC, (2008)
[4]  
Alamir M., Marchand N., Constrained minimum-time-oriented feedback control for the stabilization of nonholonomic systems in chained form, Journal Optimization Theory with Applications, 118, 2, pp. 229-244, (2003)
[5]  
Amari R., Alamir M., Tona P., Unified mpc strategy for idle-speed control, vehicle start-up and gearing applied to an automated manual transmission, Proceedings of the 17th IFAC World Congress, (2008)
[6]  
Amari R., Tona P., Alamir M., Experimental evaluation of a hybrid mpc strategy for vehicle start-up with an automated manual transmission, Proceedings of the European Control Conference (ECC 2009), (2009)
[7]  
Bemporad A., Borrelli F., Glielmo L., Vasca F., Hybrid control of dry clutch engagement, Proceedings of the European Control Conference, (2001)
[8]  
Diehl M., Bock H.G., Schlooder J.P., A real-time iteration scheme for nonlinear optimization in optimal feedback control, SIAM Journal on Control and Optimization, 43, pp. 1714-1736, (2005)
[9]  
Dolcini P.J., Canudas De Wit C., Bechart H., Observer-based optimal control of dry clutch engagement, Oil & Gas Science Technology, 62, 4, pp. 615-621, (2007)
[10]  
Falcone P., Borrelli F., Tseng H.E., Asgari J., Hrovat D., Linear time varying model predictive control and its application to active steering systems: Stability analysis and experimental validation, International Journal of Robust and Nonlinear Control, 18, 8, pp. 862-975, (2008)