A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles
This paper proposes a robust control framework for lane-keeping and obstacle avoidance of semiautonomous ground vehicles. It presents a systematic way of enforcing robustness during the MPC design stage. A robust nonlinear model predictive controller (RNMPC) is used to help the driver navigating the...
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| Published in | Vehicle system dynamics Vol. 52; no. 6; pp. 802 - 823 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Taylor & Francis
03.06.2014
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| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper proposes a robust control framework for lane-keeping and obstacle avoidance of semiautonomous ground vehicles. It presents a systematic way of enforcing robustness during the MPC design stage. A robust nonlinear model predictive controller (RNMPC) is used to help the driver navigating the vehicle in order to avoid obstacles and track the road centre line. A force-input nonlinear bicycle vehicle model is developed and used in the RNMPC control design. A robust invariant set is used in the RNMPC design to guarantee that state and input constraints are satisfied in the presence of disturbances and model error. Simulations and experiments on a vehicle show the effectiveness of the proposed framework. |
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| AbstractList | This paper proposes a robust control framework for lane-keeping and obstacle avoidance of semiautonomous ground vehicles. It presents a systematic way of enforcing robustness during the MPC design stage. A robust nonlinear model predictive controller (RNMPC) is used to help the driver navigating the vehicle in order to avoid obstacles and track the road centre line. A force-input nonlinear bicycle vehicle model is developed and used in the RNMPC control design. A robust invariant set is used in the RNMPC design to guarantee that state and input constraints are satisfied in the presence of disturbances and model error. Simulations and experiments on a vehicle show the effectiveness of the proposed framework. |
| Author | Gray, Andrew Gao, Yiqi Tseng, H. Eric Borrelli, Francesco |
| Author_xml | – sequence: 1 givenname: Yiqi surname: Gao fullname: Gao, Yiqi email: yiqigao@berkeley.edu organization: Department of Mechanical Engineering, 2169 Etcheverry Hall, UC Berkeley – sequence: 2 givenname: Andrew surname: Gray fullname: Gray, Andrew organization: Department of Mechanical Engineering, 2162 Etcheverry Hall, UC Berkeley – sequence: 3 givenname: H. Eric surname: Tseng fullname: Tseng, H. Eric organization: Ford Research & Innovation Center – sequence: 4 givenname: Francesco surname: Borrelli fullname: Borrelli, Francesco organization: Department of Mechanical Engineering, 5132 Etcheverry Hall, UC Berkeley |
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| SubjectTerms | active safety autonomous vehicles Computer simulation Design engineering Grounds Invariants Mathematical models Nonlinearity Obstacles robust control robust nonlinear MPC uncertain dynamics vehicle safety Vehicles |
| Title | A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles |
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