自适应评判神经网络在微分对策中的应用

Design of Differential Game Controllers Using Adaptive Critic Neural Networks

采用由 3个神经网络组成的自适应评判神经网络结构求解微分对策的 2点边值问题 ,其中 2个为控制神经网络 ,分别实现对微分对策系统中双边控制器的优化 ,一个为协态神经网络 ,用于对 2点边值问题中的协态变量进行求解 ,协态网络的输出对控制网络进行校正 ,训练以后的 2个控制网络作为双边的反馈控制器在线应用 .并将神经网络结果与采用Chebyshev技术的微分对策数字解进行了对比 .追逃微分对策仿真结果表明了该方法的有效性 ,并且对初始条件和测量噪声具有较强的鲁棒性 .

An adaptive critic structure including three neural networks was developed to solve the two point boundary value problem of differential games. Two control neural networks were used to optimize the controllers on two sides of the differential games, and a co-state neural network was used to approximate the co-state variables in Hamiltonian function. The output of co-state network was used to correct the output of the control networks, and the two convergent control networks can be used as feedback controllers on two sides of the differential games system respectively. The solution of differential games based on neural networks was compared with the one based on Chebyshev technique. The simulation results of the pursing-escaping differential games show that the neural network controllers are consistent with the optimal solution and present good robustness with respect to the initial conditions and measuring noises.