摘要
针对高超声速飞行器的跟踪控制问题,提出了一种自适应最优非线性控制方法。该方法在增强学习机制的基础上,采用行为-评价体系结构(actor-critic architecture)设计自适应控制器。控制器的结构由两个相互作用的神经网络组成,一个用于生成控制协议(称为actor NN),另一个用于评估控制策略(称为critic NN)。利用离散极小原理,得到了该自适应控制器的最优条件。仿真结果验证了该设计方法在高超声速飞行器跟踪控制中的有效性。
In this paper,the adaptive optimal nonlinear control approach and algorithm are proposed for the tracking control of an air-breathing hypersonic aircraft.Based on the reinforcement learning mechanism,the adaptive control agent is constructed using actor-critic architecture,which consists of two interacting neural networks,one for tracking control protocol,known as the actor NN,and the other for policy evaluation,known as critic NN.The optimality conditions for this adaptive controller are derived by using the discrete minimum principle.Simulation results are presented to verify the effectiveness of this design method for the tracking control of the air-breathing hypersonic aircraft.
作者
李仁府
胡麟
蔡伦
Li Renfu;Hu Lin;Cai Lun(School of Aerospace Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《航空兵器》
北大核心
2018年第6期3-10,共8页
Aero Weaponry
关键词
非线性控制
神经网络
增强学习
高超声速飞行器
nonlinear control
neural networks
reinforcement learning
air-breathing hypersonic flight aircraft
