Event-triggered adaptive dynamic programming algorithm for the nonlinear zero-sum differential games
CUI Li-li
ZHANG Yong
ZHANG Xin
Abstract：In this paper, an event-triggered adaptive dynamic programming algorithm (ET-ADP) is proposed to solve the saddle point of a class of nonlinear zero-sum differential games. Firstly, a new adaptive event-triggered condition is proposed. Then, a neural network (critic network) with the sampled state as its input is utilized to approximate the optimal value function. The new neural network weights updating law is designed to enable the value function, the control strategy and the disturbance strategy to be updated synchronously only at the event-triggered time. Further, the Lyapunov stability theory is used to prove that the proposed algorithm can obtain the saddle point of nonlinear zero-sum differential games online and avoid the occurrence of Zeno behavior. In the proposed ET-ADP algorithm, the value function, the control strategy and the disturbance strategy are updated only when the event-triggered condition is satisfied, as a result of which the computational burden is reduced and the network burden is eased effectively. Finally, two simulation examples validate the effectiveness of the proposed ET-ADP algorithm.
Keywords：adaptive dynamic programmingnonlinear zero-sum differential gamesevent-triggeredoptimal control
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 610-618 )