TY - GEN
T1 - Neural network control using composite learning for USVs with output error constraints
AU - Xu, Puyong
AU - Yang, Chenguang
AU - Dai, Shi Lu
AU - Mao, Zhaoyong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - In this paper, by focusing on trajectory tracking control of unmanned surface vessel (USV), we present a control method considering uncertain dynamics and output error constrains. Firstly, by using the properties of tan-type barrier Lyapunov function (BLF), the output tracking error can be constrained. Secondly, we use radical basis function neural network (RBF NN) to approximate the uncertain dynamics. Considering that the estimated parameters convergence cannot be achieved in the absence of persistent excitation (PE) conditions, the composite learning update law of the weight matrix in the NN is adopted to guarantee the parameters convergence under interval excitation (IE) conditions which is easier to reach. In simulation studies, it is proven that the USV have good ability to follow the pre-designed trajectory with small tracking error and the parameters convergence can be ensured.
AB - In this paper, by focusing on trajectory tracking control of unmanned surface vessel (USV), we present a control method considering uncertain dynamics and output error constrains. Firstly, by using the properties of tan-type barrier Lyapunov function (BLF), the output tracking error can be constrained. Secondly, we use radical basis function neural network (RBF NN) to approximate the uncertain dynamics. Considering that the estimated parameters convergence cannot be achieved in the absence of persistent excitation (PE) conditions, the composite learning update law of the weight matrix in the NN is adopted to guarantee the parameters convergence under interval excitation (IE) conditions which is easier to reach. In simulation studies, it is proven that the USV have good ability to follow the pre-designed trajectory with small tracking error and the parameters convergence can be ensured.
KW - Barrier Lyapunov function
KW - Composite learning
KW - Neural network
KW - Unmanned surface vessel (USV)
UR - https://www.scopus.com/pages/publications/85087883252
U2 - 10.1109/ICUSAI47366.2019.9124727
DO - 10.1109/ICUSAI47366.2019.9124727
M3 - 会议稿件
AN - SCOPUS:85087883252
T3 - 2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence, ICUSAI 2019
SP - 50
EP - 55
BT - 2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence, ICUSAI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence, ICUSAI 2019
Y2 - 22 November 2019 through 24 November 2019
ER -