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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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A comparative study on trajectory tracking control methods for automated vehicles.

Dequan Zeng1,2,3, Shicong Pan4,5, Yinquan Yu1,2

  • 1Institute of Precision Machining and Intelligent Equipment Manufacturing, East China Jiaotong University, Nanchang, 330013, China.

Scientific Reports
|May 16, 2025
PubMed
Summary
This summary is machine-generated.

This study compares advanced trajectory tracking controllers for autopilot systems. It evaluates linear quadratic regulator (LQR), model predictive control (MPC), and nonlinear integral sliding mode control (NISMC) for enhanced vehicle stability and performance.

Keywords:
Autonomous vehicleLinear quadratic regulatorModel predictive controlNonlinear integral sliding mode controlTrajectory tracking control

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Area of Science:

  • Automotive Engineering
  • Control Systems Engineering
  • Robotics

Background:

  • Trajectory tracking control is critical for high-performance autopilot systems.
  • Developing stable, reliable, accurate, fast, and robust controllers is essential.
  • Comparative analysis of different control strategies is needed to identify optimal solutions.

Purpose of the Study:

  • To propose and comparatively analyze three lateral and two longitudinal control schemes for trajectory tracking.
  • To evaluate the performance of Linear Quadratic Regulator (LQR), Model Predictive Control (MPC), and Nonlinear Integral Sliding Mode Control (NISMC) in lateral control.
  • To compare Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) controllers for longitudinal speed tracking.

Main Methods:

  • Design of an LQR-based feedforward and feedback lateral controller.
  • Implementation of a linear parametric time-varying MPC lateral controller solved with a quadratic programming (QP) solver.
  • Development of an NISMC lateral controller with integral action and saturation function for zero steady-state error.
  • Comparative analysis using step speed tracking tests, double lane change tests, and circular path tests under varying adhesion conditions.
  • Quantitative performance assessment using a 5-score chart based on multiple performance indicators.

Main Results:

  • Validation of controller effectiveness and performance metrics through step speed tracking tests.
  • Assessment of the efficacy, advantages, disadvantages, and applications of the three lateral controllers under different road conditions.
  • Direct comparison of control characteristics and comprehensive performance using a quantitative scoring system.

Conclusions:

  • The study provides a comprehensive comparison of advanced trajectory tracking control strategies.
  • The findings aid in selecting the most suitable controller based on specific performance requirements and operating conditions.
  • The research contributes to the development of more stable, reliable, and accurate autopilot systems.