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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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Summary

This study introduces a novel control framework for connected autonomous vehicles (CAVs) to improve path-tracking accuracy despite communication and computation delays. The proposed method ensures robust trajectory following in complex driving scenarios.

Keywords:
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Area of Science:

  • Autonomous Systems
  • Control Theory
  • Robotics

Background:

  • Connected autonomous vehicles (CAVs) require precise path-tracking for safety and efficiency.
  • Communication and computation delays degrade conventional controller performance.
  • Existing disturbance observers (DOB) struggle with unknown time delays.

Purpose of the Study:

  • To develop a delay-tolerant control framework for CAV path-tracking.
  • To address the performance degradation caused by time delays in connected systems.
  • To enhance trajectory tracking accuracy and robustness under uncertain delay conditions.

Main Methods:

  • Proposed a novel delay-tolerant communication disturbance observer (CDOB) framework.
  • Implemented CDOB for low-level path-tracking control in autonomous vehicles.
  • Conducted simulation studies with various driving scenarios and time delays.

Main Results:

  • The CDOB framework effectively compensates for adverse effects of time delays.
  • Accurate trajectory tracking was maintained even with uncertain and varying delay conditions.
  • The proposed method demonstrated superior performance compared to conventional approaches in simulations.

Conclusions:

  • The CDOB framework offers a robust solution for path-tracking control in delayed autonomous systems.
  • This approach significantly improves tracking accuracy and delay robustness for CAVs.
  • The method is well-suited for real-world connected autonomous driving applications.