Event-triggered optimal tracking control for nonzero-sum differential game systems
SHI Yi-bo
WANG Chao-li
Abstract：Recently,for the tracking problem of nonzero-sum differential game systems with unknown dynamics,it has been discussed that these methods are time-triggered,which is not ideal in an environment with limited transmission band-width and computing resources.In this paper,an integral reinforcement learning based event-triggered adaptive dynamic programming scheme is developed for continuous-time nonlinear nonzero-sum differential game systems with unknown dynamics.The strategy is inspired by the gradient descent method and the experience replay technique and uses the histori-cal and current data to update the neural network weight.This method can improve the convergence speed of neural network weight and remove the assumption of initial admissible control often used in general literature design.In the meantime,the algorithm proposes a persistent excitation condition(commonly called PE)that is easy to check online,which avoids the traditional PE condition that is not easy to check.Based on the Lyapunov theory,the uniform ultimate boundedness(UUB)properties of the tracking error and the critic neural network estimation error have been proved.Finally,a numerical simulation example is given to verify the feasibility of the proposed method.
Keywords：nonzero-sum gamesintegral reinforcement learningoptimal tracking controlneural networkevent-triggered
Publication Date：2023-02-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 220-230 )