Abstract
The control of autonomous vehicles is a challenging task that requires advanced control schemes. Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) are optimization-based control and estimation techniques that are able to deal with highly nonlinear, constrained, unstable and fast dynamic systems. In this chapter, these techniques are detailed, a descriptive nonlinear model is derived and the performance of the proposed control scheme is demonstrated in simulations of an obstacle avoidance scenario on a low-fricion icy road.
Keywords
- Model Predictive Control
- Autonomous Vehicle
- Prediction Horizon
- Dynamic Optimization Problem
- Nonlinear Model Predictive Control
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgments
This research was supported by Research Council KUL: PFV/10/002 Optimization in Engineering Center OPTEC, GOA/10/09 MaNet and GOA/10/11 Global real-time optimal control of autonomous robots and mechatronic systems. Flemish Government: IOF / KP / SCORES4CHEM, FWO: PhD/postdoc grants and projects: G.0320.08 (convex MPC), G.0377.09 (Mechatronics MPC); IWT: PhD Grants, projects: SBO LeCoPro; Belgian Federal Science Policy Office: IUAP P7 (DYSCO, Dynamical systems, control and optimization, 2012-2017); EU: FP7- EMBOCON (ICT-248940), FP7-SADCO (MC ITN-264735), ERC ST HIGHWIND (259 166), Eurostars SMART, ACCM.
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Zanon, M., Frasch, J.V., Vukov, M., Sager, S., Diehl, M. (2014). Model Predictive Control of Autonomous Vehicles. In: Waschl, H., Kolmanovsky, I., Steinbuch, M., del Re, L. (eds) Optimization and Optimal Control in Automotive Systems. Lecture Notes in Control and Information Sciences, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-319-05371-4_3
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DOI: https://doi.org/10.1007/978-3-319-05371-4_3
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