TY - GEN
T1 - Neural network based reinforcement learning control of autonomous underwater vehicles with control input saturation
AU - Cui, Rongxin
AU - Yang, Chenguang
AU - Li, Yang
AU - Sharma, Sanjay
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/1
Y1 - 2014/10/1
N2 - In this paper, the trajectory tracking control of the autonomous underwater vehicle (AUV) has been investigated in discrete time, for ease of digital computer calculation. A reinforcement learning scheme is employed using two neural networks, whereas the first one is to compensate for uncertainties for the controller, and the second one is to estimate the evaluation function, such that optimal tracking performance could be achieve for the AUV. Simulation results show that the errors convergence to a adjustable neighborhood around zero, and optimization has been achieved in the sense of reinforcement learning.
AB - In this paper, the trajectory tracking control of the autonomous underwater vehicle (AUV) has been investigated in discrete time, for ease of digital computer calculation. A reinforcement learning scheme is employed using two neural networks, whereas the first one is to compensate for uncertainties for the controller, and the second one is to estimate the evaluation function, such that optimal tracking performance could be achieve for the AUV. Simulation results show that the errors convergence to a adjustable neighborhood around zero, and optimization has been achieved in the sense of reinforcement learning.
UR - https://www.scopus.com/pages/publications/84921469968
U2 - 10.1109/CONTROL.2014.6915114
DO - 10.1109/CONTROL.2014.6915114
M3 - 会议稿件
AN - SCOPUS:84921469968
T3 - 2014 UKACC International Conference on Control, CONTROL 2014 - Proceedings
SP - 50
EP - 55
BT - 2014 UKACC International Conference on Control, CONTROL 2014 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th UKACC International Conference on Control, CONTROL 2014
Y2 - 9 July 2014 through 11 July 2014
ER -