Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy
AbstractThis paper presents a novel multi-objective coordinated adaptive cruise control (ACC) algorithm based on a model predictive control (MPC) framework which can comprehensively address issues regarding longitudinal car-following performance, lateral stability, as well as vehicle safety. During the car-following, vehicle dynamics, illustrating the forces acting on the tire contact patches, are established. To simplify the tightly coupled dynamics system, a state-feedback based disturbance decoupling method is employed, by which longitudinal and lateral dynamics can be completely decoupled. Furthermore, the traditional MPC control with a constant weight matrix will probably not be able to solve time-varying multi-objective coordinated optimization issues, especially in transient scenarios. A weight coefficient self-tuning strategy is therefore suggested by which the weight coefficient for each sub-objective can be adjusted automatically with the change of traffic scenarios, accordingly improving the overall car-following performance. The simulations show that the control algorithm utilizing the suggested self-tuning strategy reaps significant benefits in terms of longitudinal car-following performance, while at the same time maintaining a small lateral stability error range. View Full-Text
Share & Cite This Article
Zhang, J.; Li, Q.; Chen, D. Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy. Appl. Sci. 2018, 8, 978.
Zhang J, Li Q, Chen D. Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy. Applied Sciences. 2018; 8(6):978.Chicago/Turabian Style
Zhang, Junhui; Li, Qing; Chen, Dapeng. 2018. "Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy." Appl. Sci. 8, no. 6: 978.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.