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Adaptive Nonlinear Model Predictive Cruise Controller: Trailer Tow Use Case
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Conventional cruise control systems in automotive applications are usually designed to maintain the constant speed of the vehicle based on the desired set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods namely adopting the Model Predictive Control (MPC) technology utilizing the road grade preview information and allowance of the vehicle speed variation. This paper is focused on the extension of the Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier by application to the trailer tow use-case. As the connected trailer changes the aerodynamic drag and the overall vehicle mass, it may lead to the undesired downshifts for the conventional cruise controller introducing the fuel economy losses. In this work, the ANLMPC concept is extended to avoid downshifts by translating the downshift conditions to the constraints of the underlying optimization problem to be solved. To deal with significant noise factors, e.g., overall vehicle mass, change of aerodynamic drag, actual weather conditions, fuel type, the on-line adaptation of the parameters is performed by the constrained Recursive Least Squares (RLS) method. The issue of the lack of the excitation due to the limited changes in torque and engine speed in cruise is solved by the directional forgetting and dead-zone approach together with limits on the parameters. The implementation details of ANLMPC are given describing a technique leading the significant reduction of the RAM memory. The ANLMPC has been validated in real world driving conditions running in production PCM module of a Sport Utility Vehicle (SUV) towing a trailer with 1600 kg of load, showing 6.3-8.8% fuel economy improvement compared to production cruise controller with the same time of arrival.
CitationSantin, O., Beran, J., Pekar, J., Michelini, J. et al., "Adaptive Nonlinear Model Predictive Cruise Controller: Trailer Tow Use Case," SAE Technical Paper 2017-01-0090, 2017, https://doi.org/10.4271/2017-01-0090.
Data Sets - Support Documents
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- Gilbert, E. , “Vehicle cruise: Improved fuel economy by periodic control”, Automatica, 12(1):159–166, 1976.
- Kirches, C., Bock, H. G., Schloeder, J. P. , “Mixed-integer NMPC for predictive cruise control of heavy-duty trucks”, presented at 2013 European Control Conference, Switzerland, July 17-19, 2013.
- Ivarsson, M., “Fuel Optimal Powertrain Control for Heavy Trucks Utilizing Look Ahead”, Licentiate dissertation, Electrical Engineering Department, Linköping university, Sweden, 2009.
- Santin, O., Pekar, J., Beran, J., D'Amato, A. , "Cruise Controller with Fuel Optimization Based on Adaptive Nonlinear Predictive Control," SAE Int. J. Passeng. Cars – Electron. Electr. Syst. 9(2):262–274, 2016, doi:10.4271/2016-01-0155.
- Kamal, M., Mukai M., and Murata, J. , “Model Predictive Control of vehicle on Urban Roads for Improved Fuel Economy,” IEEE Transaction on Control Systems Technology, 23(3):831-841, 2013.
- Luo, L., Liu, H., Li, P., “Model predictive control for adaptive cruise control with multi-objectives: comfort, fuel-economy, safety and car-following”, Journal of Zhejiang University-SCIENCE A, 11(3):191-201, 2010.
- Koch-Groeber, H. and Wang, J., “Criteria for Coasting on Highways for Passenger Cars," SAE Technical Paper 2014-01-1157, 2014, doi:10/4271/2014-01-1157.
- Asadi, B. and Vahidi, A., “Predictive Cruise Control: Utilizing Upcoming Traffic Signals Information for Improving Fuel Economy and Reducing Trip Time”, IEEE Trans. on Control Systems Technology , 19(3):707-714, 2011.
- Kulhavý, R., “Restricted exponential forgetting in real-time identification,” Automatica 23(5):589-600, 1987, doi:10.1016/0005-1098(87)90054-9.
- Gillespie, T., "Fundamentals of Vehicle Dynamics" (Warrendale, Society of Automotive Engineers, Inc., 1992), ISBN 978-1-56091-199-9.
- Bierman, G. J., “Factorization methods for discrete sequential estimation,” Ney York, Academic Press New York, 1977, ISBN:0-12-097350-2.
- Sripada, N. R., Fisher, D. G., “Improved Least Squares Identification for Adaptive Controllers,” presented at American Control Conference (ACC), 1987, pp. 2027–2037.
- Anderson, B. D. O., “Adaptive systems, lack of persistency of excitation and bursting phenomena,” Automatica, 21(3):247–258, 1985.
- Campbell, S.F., Nguyen, N.T., Kaneshige, J., and Krishnakumar, K., “Parameter Estimation for a Hybrid Adaptive Flight Controller”, presented at AIAA Infotech@ Aerosp.Conf. Seattle 2009, USA, 2009.
- Nocedal, J., Wright, S., “Numerical optimization, Second Edition,” (Springer, 1999), ISBN: 0-387-30303-0.
- Simon, D., “Kalman filtering with state constraints: a survey of linear and nonlinear algorithms,” IET Control Theory & Applications, 4(8):1303-1318, 2010.
- Diehl, M., Bock, H.G. and Schloeder, J.P., “A Real-Time Iteration Scheme for Nonlinear Optimization in Optimal Feedback Control,” SIAM Journal on Control and Optimization, 43(5):1714-1736, 2005.
- Houska, B., Ferreau, H.J., Diehl, M., “ACADO Toolkit -- An Open Source Framework for Automatic Control and Dynamic Optimization,” Optimal Control Applications and Methods, 32(3): 298–312, 2011.
- Cagienard, R., “Move blocking strategies in receding horizon control,” J. Process Control, 17(1):563–570, 2007.
- MISRA C:2012 Guidelines for the use of the C language in critical systems. Standard, MISRA Consortium, Nuneaton, UK, Mar. 2013.