Path-tracking control occupies a critical role within autonomous driving systems,
directly reflecting vehicle motion and impacting both safety and user
experience. However, the ever-changing vehicle states, road conditions, and
delay characteristics of control systems present new challenges to the path
tracking of autonomous vehicles, thereby limiting further enhancements in
performance. This article introduces a path-tracking controller, time-varying
gain-scheduled path-tracking controller with delay compensation (TGDC), which
utilizes a linear parameter-varying system and optimal control theory to account
for time-varying vehicle states, road conditions, and steering control system
delays. Subsequently, a polytopic-based path-tracking model is applied to design
the control law, reducing the computational complexity of TGDC. To evaluate the
effectiveness and real-time capability of TGDC, it was tested under a series of
complex conditions using a hardware-in-the-loop platform. The results
demonstrate that through the polytopic-based path-tracking model and delay
compensation strategy in TGDC, it can effectively enhance path-tracking
performance with minimal computational load, even under conditions of parameter
variability and control delays.
