Model-Free Game-Based Dynamic Event-Driven Safety-Critical Control of Unknown Nonaffine Systems

In this paper, the model-free dynamic event-driven safe (MFDEDS) control of unknown nonaffine systems with state and input constraints is investigated via adaptive dynamic programming. To begin with, by introducing a dynamic compensator and performing system transformation, the safe control problem with state and input constraints is transformed into an optimal regulation problem of an unconstrained system. Afterwards, an integral reinforcement learning algorithm is applied to the unconstrained system to derive an optimal safe control policy independent of the original system model, which achieves model-free approximate optimal control for the original system. To conserve computing and communication resources, a novel game-based dynamic event-driven mechanism is established, which models the control policy and the event-driven error as players in a zero-sum game, with the aim of obtaining the worst event-driven error to maximize the triggering interval. Furthermore, an approximate solution to the Hamilton-Jacobi-Bellman equation is derived by constructing a single-critic learning structure, which results in an approximate optimal safe control policy. Theoretical analysis demonstrates that the proposed MFDEDS control scheme ensures the closed-loop system is asymptotically stable. Ultimately, the efficacy of the developed approach is corroborated through two simulation examples.