Adaptive Event-Triggered Quantized Path Following Control for Networked Autonomous Vehicles with Backlash Hysteresis

This paper investigates the problem of path following for networked autonomous vehicles with both limited communication bandwidth and unknown backlash-like hysteresis. First, by analyzing the effects of event triggers on path following performance, an improved adaptive event-triggered (AET) mechanism with a non-monotonic threshold function is designed, which can achieve a better balance between saving limited communication resources and ensuring path following accuracy. Then, to attenuate control performance degradation caused by backlash non-linearity, the slope of hysteresis is selected as premise variables considered in Takagi-Sugeno (T-S) fuzzy modelling, and a fuzzy observer-based robust path following controller is designed with event-triggered and quantized communication. Moreover, the state constraints are formulated as matrix inequality conditions and taken into account to enhance vehicle stability. Sufficient conditions are derived to guarantee that the closed-loop system is exponentially stable and satisfies certain $\mathcal {H}_\infty$ index. Finally, the simulation results show that compared with the existing ETM-based control methods, the proposed control strategy not only reduces the communication resources up to 85.4%, but also improves the system robustness, and path following accuracy.