Event-Triggered Safe Control for the Zero-Sum Game of Nonlinear Safety-Critical Systems With Input Saturation

In this paper, a novel adaptive dynamic programming (ADP)-based event-triggered safe control method is proposed to solve the zero-sum game problem of nonlinear safety-critical systems with safety constraints and input saturation. First, barrier function-based system transformation, saturation transformed into an equivalent problem, so as guarantee that does not violate constraints. Furthermore, non-quadratic utility function introduced performance Then, critic neural network (NN) constructed approximate optimal value function. Subsequently, scheme developed determine update instant law disturbance law. Therefore, ADP-based can ensure states satisfy constraints, while greatly reducing amount calculation saving communication resources. addition, during learning process, concurrent used relax persistence excitation (PE) condition. According Lyapuov theory, it proved weight estimation error are uniformly ultimately bounded (UUB), Zeno behavior excluded. Finally, simulation example verifies effectiveness method.