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Qikun Dai

dblp:266/7252 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0002-8359-9770ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Autonomous driving · 50% Robot navigation and mapping · 50%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Autonomous driving
vehicle control
0.412020
Driver-automation shared steering control for highly automated vehicles · Sci. China Inf. Sci. 2020

Methods — techniques the papers use, named apart from their topics

shared control · 0.4automation · 0.4
YearPublicationVenuePosition
2020 Driver-automation shared steering control for highly automated vehicles
Jun Liu 0086, Hongyan Guo, Linhuan Song, Qikun Dai, Hong Chen 0003
Sci. China Inf. Sci.4
2020 A Distributed Adaptive Triple-Step Nonlinear Control for a Connected Automated Vehicle Platoon With Dynamic Uncertainty
abstract
Connected automated vehicle (CAV) platoon control is becoming increasingly prevalent because of its unique advantages in reducing fuel consumption and improving traffic efficiency. A novel control framework for CAV platoon control is designed in this article. First, a model predictive control (MPC)-based method is proposed to obtain the optimal velocity of the whole platoon, in which both reducing fuel consumption and improving transport efficiency are taken into account in the optimization process. Then, a distributed adaptive triple-step nonlinear control strategy is investigated from the perspective of multiagent system control. The adaptive performance of the control strategy can guarantee the string stability of the CAV platoon under the premise of the existence of dynamic uncertainties. Various simulation conditions with heterogeneous dynamic disturbances are designed to validate the proposed control strategy, and the results show that the proposed control strategy can be robust to dynamic disturbances while ensuring the string stability of the CAV platoon.
Hongyan Guo, Jun Liu 0086, Qikun Dai, Hong Chen 0003, Yulei Wang 0007, Wanzhong Zhao
IEEE Internet Things J.3