Abstract
The vehicle starting-up is also an important control issue for vehicles with an AMT. The characteristics of the clutch during the start-up process are fast and complex. Moreover, the system characteristics change along with the variation of the driving conditions and long-term aging. While the performance of physical-model-based controllers relies heavily on the explicit process modeling, a data-driven predictive controller is designed in this chapter directly from input–output data and does not require an explicit model of the AMT clutch system. The predictor equation is expressed with incremental inputs and outputs in order to obtain a tracking offset-free control, and hard constraints of the input and output can be considered explicitly in the formulation of the optimization problem.
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Notes
- 1.
This chapter uses the content of [7], with permission from IEEE.
References
Chiera BA, White LB (2005) A subspace predictive controller for End-to-End TCP congestion control. In: AusCTW, pp 42–48
Favoreel W, De Moor B (1998) SPC: subspace predictive control. In: Technical report. Katholieke Universiteit, Leuven, pp 49–98
Hallouzie R, Verhaegen M (2008) Fault-tolerant subspace predictive control applied to a Boeing 747 model. J Guid Control Dyn 31(4):873–891
Kadali K, Huang B, Rossiter A (2003) A data driven subspace approach to predictive controller design. Control Eng Pract 11(3):261–278
Lee JM, Lee JH (2005) Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes. Automatica 41:1281–1288
Lespinats S, Verleysen A, Giron M, Fertil B (2007) DD-HDS: a method for visualization and exploration of high-dimensional data. IEEE Trans Neural Netw 18(5):1265–1279
Lu XH, Chen H, Wang P, Gao BZ (2011) Design of a data-driven predictive controller for start-up process of AMT vehicles. IEEE Trans Neural Netw 22(12):2201–2212
Maciejowski JM (2002) Predictive control: with constraints. Prentice Hall, New York
Morari M, Lee JH (1997) Model predictive control: past, present and future. In: Proc PSE’97-ESCAPE-7 symposium, Trondheim
Qin SJ, Lin W, Ljung L (2005) A novel subspace identification approach with enforced causal models. Automatica 41(12):2043–2053
Wang X, Huang B, Chen T (2007) Data-driven predictive control for solid oxide fuel cells. J Process Control 17(2):103–114
Xu JX, Hou ZS (2009) Notes on data-driven system approaches. Acta Autom Sin 35(6):668–675
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Chen, H., Gao, B. (2014). Data-Driven Start-Up Control of AMT Vehicle. In: Nonlinear Estimation and Control of Automotive Drivetrains. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41572-2_8
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DOI: https://doi.org/10.1007/978-3-642-41572-2_8
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