TY - GEN
T1 - RBF neural network identifier based constrained optimal guidance for Mars entry vehicles
AU - Qiu, Teng Hai
AU - Luo, Biao
AU - Wu, Huai Ning
AU - Guo, Lei
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/2
Y1 - 2015/10/2
N2 - In this paper, a radial basis function (RBF) neural network (NN) identifier based approximate constrained optimal guidance law is proposed for Mars entry vehicles guidance. Firstly, an RBF NN identifier is used to identify the system uncertain parameters. With the identified parameters, the optimal guidance problem of Mars entry vehicles is transformed into an optimal tracking control one, which depends on the solution of the Hamilton-Jacobi-Bellman (HJB) equation. Due to the control input constraints, a generalized non-quadratic performance function is proposed. In general, the HJB equation is a nonlinear partial differential equation that is difficult or even impossible to be solved analytically. We use an NN to solve the HJB equation approximately. Finally, the Monte-Carlo simulation results on the Mars entry vehicles demonstrate the effectiveness of the proposed method.
AB - In this paper, a radial basis function (RBF) neural network (NN) identifier based approximate constrained optimal guidance law is proposed for Mars entry vehicles guidance. Firstly, an RBF NN identifier is used to identify the system uncertain parameters. With the identified parameters, the optimal guidance problem of Mars entry vehicles is transformed into an optimal tracking control one, which depends on the solution of the Hamilton-Jacobi-Bellman (HJB) equation. Due to the control input constraints, a generalized non-quadratic performance function is proposed. In general, the HJB equation is a nonlinear partial differential equation that is difficult or even impossible to be solved analytically. We use an NN to solve the HJB equation approximately. Finally, the Monte-Carlo simulation results on the Mars entry vehicles demonstrate the effectiveness of the proposed method.
UR - https://www.scopus.com/pages/publications/84961654609
U2 - 10.1109/ICIST.2015.7289001
DO - 10.1109/ICIST.2015.7289001
M3 - 会议稿件
AN - SCOPUS:84961654609
T3 - 2015 5th International Conference on Information Science and Technology, ICIST 2015
SP - 381
EP - 386
BT - 2015 5th International Conference on Information Science and Technology, ICIST 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Information Science and Technology, ICIST 2015
Y2 - 24 April 2015 through 26 April 2015
ER -