TY - GEN
T1 - Model Free Adaptive Control Algorithm based on ReOSELM for Autonomous Driving Vehicles
AU - Zhang, Xiaofei
AU - Ma, Hongbin
AU - Wang, Zhichao
AU - Fan, Mingyu
AU - Zhao, Bolin
N1 - Publisher Copyright:
© 2021 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2021/7/26
Y1 - 2021/7/26
N2 - Different road conditions and dynamic environment bring significant challenges to the control system of autonomous driving vehicle (ADV). As is known, historical data collected from ADV contains valuable information about control systems, therefore, it is a promising thing to study adaptive control algorithms that have certain learning ability. In order to improve the control performance of ADV and the efficiency in data usage, in this paper, a model free adaptive control algorithm based on regularized online sequential extreme learning machine (ReOSELM) is introduced, it is difficult to analyze the algorithm based on neural network, and the system stability by improved update algorithm of ReOSELM is proved. Simulation results indicate that the proposed algorithm is effective in improving control precision when ADV is turning, and experimental results on an autonomous driving vehicle show that this algorithm is effective in real environment.
AB - Different road conditions and dynamic environment bring significant challenges to the control system of autonomous driving vehicle (ADV). As is known, historical data collected from ADV contains valuable information about control systems, therefore, it is a promising thing to study adaptive control algorithms that have certain learning ability. In order to improve the control performance of ADV and the efficiency in data usage, in this paper, a model free adaptive control algorithm based on regularized online sequential extreme learning machine (ReOSELM) is introduced, it is difficult to analyze the algorithm based on neural network, and the system stability by improved update algorithm of ReOSELM is proved. Simulation results indicate that the proposed algorithm is effective in improving control precision when ADV is turning, and experimental results on an autonomous driving vehicle show that this algorithm is effective in real environment.
KW - autonomous driving vehicle
KW - data-driven control
KW - model free adaptive control
KW - regularized online sequential extreme learning machine
UR - http://www.scopus.com/inward/record.url?scp=85117300232&partnerID=8YFLogxK
U2 - 10.23919/CCC52363.2021.9549530
DO - 10.23919/CCC52363.2021.9549530
M3 - Conference contribution
AN - SCOPUS:85117300232
T3 - Chinese Control Conference, CCC
SP - 3803
EP - 3809
BT - Proceedings of the 40th Chinese Control Conference, CCC 2021
A2 - Peng, Chen
A2 - Sun, Jian
PB - IEEE Computer Society
T2 - 40th Chinese Control Conference, CCC 2021
Y2 - 26 July 2021 through 28 July 2021
ER -