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- W4386970214 abstract "The automotive industry has developed a variety of chassis control functions to improve the comfort, safety, and handling of vehicles. Safety measures, required by legislation, are usually specifically tailored to new control laws and actuators, requiring extensive testing.In this paper, an adaptive control input reduction scheme using model predictive control is introduced as a safeguard for an untested control law. Malfunctions of the controller occurring unexpectedly in common driving situations can be hard to control for the driver and lead to severe accidents. Thus, they should be avoided by the most strict countermeasures, which can be assisted by the function presented in this paper. The goal of the optimization is to reduce the control inputs in such a way, that the deviation from the reference state is limited. This is achieved using a piece-wise defined cost function, which is almost flat in the neighborhood of the reference but steep outside. The optimization is implemented in real-time and measurements with an in-production Audi S8 (D5) are presented, showing a reduction in lane deviation of over ≈ 1.5m for some of the controller fault injections." @default.
- W4386970214 created "2023-09-23" @default.
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- W4386970214 date "2023-08-16" @default.
- W4386970214 modified "2023-10-01" @default.
- W4386970214 title "Adaptive Input Reduction for Chassis Control Functions using Model Predictive Control" @default.
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- W4386970214 doi "https://doi.org/10.1109/ccta54093.2023.10253129" @default.
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