Abstract
The paper considers issues and algorithmic approaches related to modeling and identification of Markov Chain type models for vehicle applications. The use of Markov Chain models in these applications is stimulated by their ability to reflect aggregate vehicle operating conditions and induce “best on average” control policies based on application of stochastic dynamic programming and stochastic Model Predictive Control. A novel fuzzy encoding approach of continuous signals is proposed in which a signal value is simultaneously associated with multiple cells, and it is shown to enhance identification and prediction accuracy of Markov Chain type models. A computationally simple identification algorithm, suitable for on-board applications, is proposed to learn Markov Chain transition probabilities in real-time. Examples of real-time learning models of vehicle speed and road grade are reported to illustrate the overall identification approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fraser, A.M.: Hidden Markov Models and Dynamical Systems. SIAM, Philadelphia (2008)
Chistyakov, V.P.: A Course in Probability Theory, Nauka, Moscow (1987)
Dynkin, E.B., Yushkevich, A.A.: Markov Processes: Theorems and Problems. Plenum, New York (1969)
Filev, D., Georgieva, O.: An extended version of the Gustafson-Kessel algorithm for evolving data stream clustering. In: Angelov, P., Filev, D., Kasabov, N. (eds.) Evolving Intelligent Systems, John Wiley and Sons, New York (2009)
Filev, D., Kolmanovsky, I.: Markov chain modeling approaches for on board applications. In: Proc. of American Control Conference, Baltimore, MD (2010)
Filev, D., Kolmanovsky, I.: A generalized Markov chain modeling approach for on-board applications. In: Proc. of International Journal Conference on Neural Networks, Barcelona, Spain (2010)
Johannesson, L., Ashbogard, M., Egardt, B.: Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming. IEEE Trans. on Intelligent Transportation Systems 8(1) (2007)
Kolmanovsky, I., Filev, D.: Terrain and traffic optimized vehicle speed control. In: Proc. of 6th IFAC Symposium Advances in Automotive Control, Munich, Germany (2010)
Klir, G.: A principle of uncertainty and information invariance. Int. J. of General Systems 17(2), 249–275 (1990)
Kolmanovsky, I., Filev, D.: Stochastic optimal control of systems with soft constraints and opportunities for automotive applications. In: Proc. of IEEE Multi-conference on Systems and Control, St., Petersburg, Russia (2009)
Kolmanovsky, I., Sivergina, I., Lygoe, B.: Optimization of powertrain operation policy for feasibility assessment and calibration: Stochastic dynamic programming approach. In: Proc. of American Control Conference, Anchorage, Alaska (2002)
Kosko, B.: Fuzzy Engineering. Prentice-Hall, Upper Saddle River (1996)
O’Leary, D., Stewart, G., Vandergraft, G.: Estimating the largest eigenvalue of a positive definite matrix. Mathematics of Computation 33, 1289–1292 (1979)
Lin, C., Peng, H., Grizzle, J.: A stochastic control strategy for hybrid electric vehicles. In: Proc. of American Control Conference, Baltimore, MD, pp. 4710–4715 (2004)
Malikopoulos, A.A., Papalambros, P.Y., Assanis, D.N.: Online self-learning identification and stochastic control for autonomous internal combustion engines. ASME J. Dyn. Sys., Meas., Control 132(2) (2010)
Pedrycz, W., Skowron, A., Kreinovich, V.: Handbook of Granular Computing. John Wiley and Sons, Chichester (2008)
Ripaccioli, G., Bernardini, D., Di Cairano, S., Bemporad, A., Kolmanovsky, I.: A stochastic Model Predictive Control approach for a series hybrid electric vehicle power management. In: Proc. of American Control Conference, Baltimore, MD (2010)
Yager, R., Filev, D.: Essentials of Fuzzy Modeling and Control. John Wiley and Sons, New York (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer London
About this chapter
Cite this chapter
Filev, D.P., Kolmanovsky, I. (2012). Markov Chain Modeling and On-Board Identification for Automotive Vehicles. In: Alberer, D., Hjalmarsson, H., del Re, L. (eds) Identification for Automotive Systems. Lecture Notes in Control and Information Sciences, vol 418. Springer, London. https://doi.org/10.1007/978-1-4471-2221-0_7
Download citation
DOI: https://doi.org/10.1007/978-1-4471-2221-0_7
Publisher Name: Springer, London
Print ISBN: 978-1-4471-2220-3
Online ISBN: 978-1-4471-2221-0
eBook Packages: EngineeringEngineering (R0)