Work

Self‐Triggered Robust Safety‐Critical Control of Multiplayer Nonlinear Systems via Adaptive Dynamic Programming

Year: 2026

Type: article

Abstract: ABSTRACT This paper addresses the robust safety‐critical control problem for uncertain multiplayer nonlinear systems with state and input constraints by proposing a robust self‐triggered control (RSTC) scheme based on adaptive dynamic programming. Through the integration of a dynamic compensator and a modified performance index function, the constrained robust safety‐critical control problem for uncertain multiplayer nonlinear systems is converted into an optimal control problem for affine nonlinear systems with state and control input constraints. Subsequently, the constrained system is converted into an unconstrained one by employing the system transformation approach, which helps to derive the optimal control strategy for each player. To conserve computational and communication resources, a self‐triggered control mechanism is developed. This mechanism updates the control strategy when the preset triggering condition is satisfied. The closed‐loop system is guaranteed to be uniformly ultimately bounded via Lyapunov stability theory. Numerical simulations validate the feasibility and effectiveness of the proposed RSTC method.

Source: International Journal of Robust and Nonlinear Control

DOI: 10.1002/rnc.70410

Authors: Shukun Liu, Yongwei Zhang, Yonghua Wang

Institutions: Guangdong University of Technology, South China Agricultural University

Language: English

Topic: Adaptive Dynamic Programming Control

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: National Natural Science Foundation of China

Award: G8251953571