Adaptive Dynamic Programming with Event-Triggered Sampling Control for Path Following of Autonomous Vehicles

Abstract

Sampled-data control for dynamic programming of continuous-time system, which can facilitate to implement control actions under networked environments, is rarely considered in most existing works. In order to address this issue, an event-triggered dynamic programming sampling control (ET-DPSC) approach is investigated for networked path-following control of autonomous vehicles. The first goal is to establish the sampled-data-based event-triggered path-following control model using Hamilton–Jacobi–Bellman equation. Secondly, the asymptotic stability criterion in an input-to-state sense should be tackled by exploiting Lyapunov theory and input delay approach. As a third goal, the sampled-data controller based on dynamic programming method should be synthesized. Compared to most existing ADP-based control strategies, the proposed ET-DPSC approach not only guarantees the stability of path-following control but also provides significant benefits for control implementation under communication-constrained environments. In addition, Zeno behavior is naturally excluded by using periodic discrete-time sampling control fashion. At last, Simulink and CarSim joint simulations are conducted to show effectiveness of the proposed ET-DPSC scheme by comparing with linear quadratic regulation without considering input delay.