Control de aprendizaje seguro activado por eventos mediante optimización de inteligencia de enjambre

This article develops an event-triggered safe critic learning control (ESCLC) algorithm for nonlinear systems subject to asymmetric state constraints by integrating a safe critic learning control (SCLC) framework with an event-triggering mechanism. The SCLC algorithm innovatively incorporates control barrier functions into the safe value function design, addressing the challenge of deriving optimal control policies that guarantee system safety. Convergence of the SCLC algorithm is rigorously established within the value iteration framework, along with a criterion for assessing the admissibility of control policies. To enhance the application value of the algorithm in resource-constrained scenarios, an event-triggering mechanism is incorporated into the SCLC framework, yielding the ESCLC algorithm. The resulting closed-loop system under the ESCLC algorithm is proved to be asymptotically stable, and an upper bound on the actual value function is derived to ensure bounded performance degradation. In addition, a policy improvement method based on particle swarm optimization is designed that eliminates dependence on the system control matrix. Finally, the effectiveness of the ESCLC algorithm is verified through simulation experiments on a torsion pendulum system and a ball-and-beam system.