A Human-in-the-loop controller using sampled-data adaptive dynamic programming achieved zero steady-state lane-keeping error and adapted to different road curvature conditions in simulations.
A novel Human-in-the-loop controller using adaptive dynamic programming successfully assists drivers in lane-keeping across varying road curvatures in simulation.
In this paper, we study the design of a lane-keeping control assistance system, by taking the interaction between the driver and the vehicle into account. A Human-in-the-loop controller design approach is proposed to assist the driver to maintain in the central position of a lane. A sampled-data adaptive dynamic programming (ADP) method is introduced to develop online adaptive optimal controllers to achieve zero steady-state lane-keeping error while the road curvature condition and the vehicle dynamics are unknown. The effectiveness of this steering control assistance system is supported by rigorous analysis and validated by simulation results. Furthermore, as opposed to previous Human-in-the-loop controllers, the proposed controller is capable of adapting to different road curvature conditions.
Huang et al. (Fri,) conducted a other in Lane-keeping control for autonomous vehicles. Human-in-the-loop controller using sampled-data adaptive dynamic programming (ADP) vs. Previous Human-in-the-loop controllers was evaluated on Zero steady-state lane-keeping error. A Human-in-the-loop controller using sampled-data adaptive dynamic programming achieved zero steady-state lane-keeping error and adapted to different road curvature conditions in simulations.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: