基于统计线性化的随机非线性微分对策逼近最优策略

张平; 方洋旺; 惠晓滨; 刘新爱; 李亮

doi:10.3724/SP.J.1004.2013.00390

基于统计线性化的随机非线性微分对策逼近最优策略

doi: 10.3724/SP.J.1004.2013.00390 cstr: 32138.14.SP.J.1004.2013.00390

1.
中国人民解放军第95889部队酒泉 735018;
2.
空军工程大学航空航天工程学院西安 710038

详细信息

通讯作者:
张平

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出版历程
- 收稿日期: 2012-03-07
- 修回日期: 2012-11-06
- 刊出日期: 2013-04-20

Near Optimal Strategy for Nonlinear Stochastic Differential Games Based on the Technique of Statistical Linearization

1.
Unit 95889, PLA, Jiuquan 735018;
2.
College of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an 710038

摘要

摘要: 针对二人零和随机非线性微分对策问题, 利用统计线性化技术并提出一种新的逼近策略控制方法. 通过求解具有统计线性化参数的Riccati微分方程得到逼近控制策略, 该Riccati微分方程与一般线性系统的Riccati微分方程具有明显的区别; 同时对控制量受约束情形的控制策略也进行了求解; 最后通过仿真验证了所得结论的正确性.
- 微分对策 /
- 统计线性化 /
- 随机非线性系统 /
- 逼近最优策略
Abstract: A novel solution for a class of nonlinear zero-sum stochastic differential games is given based on the technique of statistical linearization. The near optimal feedback strategies are derived by solving the statistical state dependent Riccati equation, which is significantly different from the Riccati equation of linear systems. The case of strategy with bound limitation is also investigated. An example is given to illustrate the application of the theory.
- Differential games /
- statistical linearization /
- nonlinear stochastic system /
- near optimal strategy

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参考文献(1)

[1]

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