An Overview on the Adaptive Dynamic Programming Based Missile Guidance Law

被引:0
|
作者
Sun J.-L. [1 ]
Liu C.-S. [1 ]
机构
[1] College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing
来源
Liu, Chun-Sheng (liuchsh@nuaa.edu.cn) | 1600年 / Science Press卷 / 43期
基金
中国国家自然科学基金;
关键词
Adaptive dynamic programming (ADP); Guidance law; Missile; Optimal control;
D O I
10.16383/j.aas.2017.c160735
中图分类号
学科分类号
摘要
Adaptive dynamic programming (ADP) is a powerful tool for optimal control of complicated nonlinear system, which is a novel approximate optimal control method. Recently, it has become a hot topic in the field of control theory and computational intelligence. This paper focuses on giving a review of ADP on the development of ADP algorithms and its aerospace applications. The design methods of classic missile guidance law are introduced, as well as the present and potential applications of ADP in the guidance law design of missiles. Copyright © 2017 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码:1101 / 1113
页数:12
相关论文
共 151 条
  • [1] Zhang H.G., Zhang X., Luo Y.H., Yang J., An overview of research on adaptive dynamic programming, Acta Automatica Sinica, 39, 4, pp. 303-311, (2013)
  • [2] Liu D.R., Li H.L., Wang D., Data-based self-learning optimal control: research progress and prospects, Acta Automatica Sinica, 39, 11, pp. 1858-1870, (2013)
  • [3] Werbos P., Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, (1974)
  • [4] Prokhorov D.V., Wunsch D.C., Adaptive critic designs, IEEE Transactions on Neural Networks, 8, 5, pp. 997-1007, (1997)
  • [5] Padhi R., Unnikrishnan N., Wang X.H., Balakrishnan S.N., A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems, Neural Networks, 19, 10, pp. 1648-1660, (2006)
  • [6] Wang Y., O'Donoghue B., Boyd S., Approximate dynamic programming via iterated Bellman inequalities, International Journal of Robust and Nonlinear Control, 25, 10, pp. 1472-1496, (2015)
  • [7] Bertsekas D.P., Tsitsiklis J.N., Neuro-dynamic programming: an overview, Proceedings of the 34th IEEE Conference on Decision and Control, pp. 560-564, (1995)
  • [8] Zhu L.M., Modares H., Peen G.O., Lewis F.L., Yue B.Z., Adaptive suboptimal output-feedback control for linear systems using integral reinforcement learning, IEEE Transactions on Control Systems Technology, 23, 1, pp. 264-273, (2015)
  • [9] Bhasin S., Reinforcement Learning and Optimal Control Methods for Uncertain Nonlinear Systems, (2011)
  • [10] Vrabie D., Vamvoudakis K.G., Lewis F.L., Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles, (2012)