Robust H∞ Path Tracking Control of Autonomous Ground Vehicles with Multiple Missing Measurements

This paper presents a robust path tracking of autonomous ground vehicles (AGVs) with an uncertain tire cornering stiffness and multiple missing measurements. The multiple missing measurements and deficiencies in the vehicle’s state measurements are considered by utilising independent Bernoulli random variables. The tire cornering stiffness is assumed to be varying, and uncertain and is modelled by norm-bounded uncertainties. The Lyapunov stability theory is used to design the feedback gain to ensure the asymptotic stability of the closed-loop system and meet the $H_{\infty}$ performance criterion. The desired feedback gain is obtained by solving a linear matrix inequality (LMI) problem. To demonstrate the performance and effectiveness of the proposed control design, the double-lane change manoeuvre is used for the simulation which is relatively complex and needs specific speed requirements. The simulation results show that the proposed robust $H_{\infty}$ controller can efficiently handle the uncertainties and missing measurements while performing the tracking of the desired path. The maximum lateral error of 0.02 m and maximum heading angle error of 0.05 rad are observed in the simulation results which indicate the accuracy of the tracking task.