Neural network optimal control for nonlinear system based on zero-sum differential game

Title:

Neural network optimal control for nonlinear system based on zero-sum differential game

Creator:

Xingjian, Fu and Zizheng, Li

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:c9961ffa-2919-45a7-8df7-a1f24fda42da
uuid:c9961ffa-2919-45a7-8df7-a1f24fda42da
doi:10.14736/kyb-2021-3-0546

Subject:

neural network, nonlinear system, approximate dynamic programming, and zero-sum game

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper, for a class of the complex nonlinear system control problems, based on the two-person zero-sum game theory, combined with the idea of approximate dynamic programming(ADP), the constrained optimization control problem is solved for the nonlinear systems with unknown system functions and unknown time-varying disturbances. In order to obtain the approximate optimal solution of the zero-sum game, the multilayer neural network is used to fit the evaluation network, the execution network and the disturbance network of ADP respectively. The Lyapunov stability theory is used to prove the uniform convergence, and the system control output converges to the neighborhood of the target reference value. Finally, the simulation example verifies the effectiveness of the algorithm.

Language:

English

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

546-566

Source:

Kybernetika | 2021 Volume:57 | Number:3

Harvested from:

CDK

Metadata only:

false