TY - GEN
T1 - Connected cruise control for a platoon of human-operated and autonomous vehicles using adaptive dynamic programming
AU - Huang, Mengzhe
AU - Gao, Weinan
AU - Jiang, Zhong Ping
N1 - Funding Information:
This work has been supported in part by the U.S. National Science Foundation grant ECCS-1501044.
Publisher Copyright:
© 2017 Technical Committee on Control Theory, CAA.
PY - 2017/9/7
Y1 - 2017/9/7
N2 - Cooperative driving with V2V communication is under extensive investigation, because of its potential to increase the safety, reliability, connectivity, and autonomy of transportation systems. This paper studies the problem of connected cruise control (CCC) for a platoon of human-driven and autonomous vehicles. Motional data, such as distance and velocity information, are transmitted by vehicle-to-vehicle (V2V) communication between connected vehicles. Taking into account the communication latency and unpredictable behavior in the leading vehicle, we formulate the CCC problem as an adaptive optimal control problem with input delay and disturbance. A novel data-driven control solution is proposed for the vehicle platoon, which guarantees that each vehicle can achieve safe distance and desired common velocity. Incorporating adaptive dynamic programming technique with sampled-data system theory, a data-driven adaptive optimal controller is learned from sampled data, without the knowledge of the human or vehicle dynamics of the platooning vehicles. Stability and robustness analyses are provided by means of input-to-state stability theory. Numerical results confirm the efficacy of our method.
AB - Cooperative driving with V2V communication is under extensive investigation, because of its potential to increase the safety, reliability, connectivity, and autonomy of transportation systems. This paper studies the problem of connected cruise control (CCC) for a platoon of human-driven and autonomous vehicles. Motional data, such as distance and velocity information, are transmitted by vehicle-to-vehicle (V2V) communication between connected vehicles. Taking into account the communication latency and unpredictable behavior in the leading vehicle, we formulate the CCC problem as an adaptive optimal control problem with input delay and disturbance. A novel data-driven control solution is proposed for the vehicle platoon, which guarantees that each vehicle can achieve safe distance and desired common velocity. Incorporating adaptive dynamic programming technique with sampled-data system theory, a data-driven adaptive optimal controller is learned from sampled data, without the knowledge of the human or vehicle dynamics of the platooning vehicles. Stability and robustness analyses are provided by means of input-to-state stability theory. Numerical results confirm the efficacy of our method.
KW - Adaptive dynamic programming (ADP)
KW - Connected vehicles
KW - Delayed feedback
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U2 - 10.23919/ChiCC.2017.8028869
DO - 10.23919/ChiCC.2017.8028869
M3 - Conference contribution
AN - SCOPUS:85032185701
T3 - Chinese Control Conference, CCC
SP - 9478
EP - 9483
BT - Proceedings of the 36th Chinese Control Conference, CCC 2017
A2 - Liu, Tao
A2 - Zhao, Qianchuan
PB - IEEE Computer Society
T2 - 36th Chinese Control Conference, CCC 2017
Y2 - 26 July 2017 through 28 July 2017
ER -