Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 68, Issue 9, Pages 8698-8707Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2020.3009585
Keywords
Mathematical model; Tires; Uncertainty; Autonomous vehicles; Vehicle dynamics; Robustness; Axles; Automated steering system; linear matrix inequalities (LMI); parametric uncertainties; robust model predictive control (MPC)
Categories
Funding
- Key Scientific and Technological Research Project in Anhui Province [JAC2019022505]
- National Science Fund of the People's Republic of China [51675293]
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The article proposes a vehicle automated steering controller based on model predictive control (MPC) approach, which improves control performance, ensures control accuracy, and strong robustness.
The automated steering control technology is crucial for an autonomous vehicle, but due to parametric uncertainties and time varying, the performance of automated steering control can be degraded. Therefore, a vehicle automated steering controller based on a model predictive control (MPC) approach is proposed in this article. First, considering tire nonlinear characteristics, the state and control matrices are modified, then the time-varying vehicle speed is considered and a linear parameter varying lateral model is established through utilizing a polytope with finite vertices to describe vehicle longitudinal velocity. Then, the MPC-based vehicle path tracking controller, which is robust against parameter uncertainties, is designed; the proposed controller can be solved via a set of linear matrix inequalities (LMI), which are derived from Lyapunov asymptotic stability and the minimization of the worst case infinite horizon quadratic objective function. The proposed control system is evaluated by both cosimulations of MATLAB/Simulink & CarSim and real-bus tests. Results show the effectiveness of the proposed controller, and it can ensure the control accuracy and strong robustness.
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