Contingent Nonlinear Model Predictive Control for Collision Imminent Steering in Uncertain Environments
| Autor: | John Wurts, Jeffrey L. Stein, Tulga Ersal, James Dallas |
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| Rok vydání: | 2020 |
| Předmět: |
Scheme (programming language)
0209 industrial biotechnology Computer science 020208 electrical & electronic engineering Context (language use) 02 engineering and technology Collision Model predictive control 020901 industrial engineering & automation Control and Systems Engineering Control theory Robustness (computer science) Nonlinear model 0202 electrical engineering electronic engineering information engineering Baseline (configuration management) computer computer.programming_language |
| Zdroj: | IFAC-PapersOnLine. 53:14330-14335 |
| ISSN: | 2405-8963 |
| Popis: | A novel uncertainty based contingent model predictive control algorithm is presented for autonomous vehicles operating in uncertain environments. Nominal model predictive control relies on a model to predict future states over a horizon and hence requires accurate models and parameterization. In application, environmental conditions and parameters may be unknown or varying, posing robustness issues for model predictive control. This work presents a new selectively robust adaptive model predictive control algorithm that is applied to collision imminent steering controllers for automotive safety. In this context, uncertainties in the road coefficient of friction are estimated using unscented Kalman filtering and the controller is updated based upon the estimated uncertainties. The utility of the uncertainty based controller is demonstrated in a collision imminent steering scenario and compared to nominal deterministic model predictive control, as well as a baseline adaptive scheme. The results suggest the uncertainty based controller can improve the robustness of model predictive control by nearly 50% for deterministic model predictive control and over 10% for the baseline adaptive scheme. |
| Databáze: | OpenAIRE |
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