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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Highly Robust Adaptive Sliding Mode Trajectory Tracking Control of Autonomous Vehicles.

Fengxi Xie1, Guozhen Liang1, Ying-Ren Chien2

  • 1Department of Electrical Engineering and Computer Science, Technische Universität Berlin, 10623 Berlin, Germany.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive sliding mode controller (SMC) for autonomous vehicles, optimized using enhanced particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms. The new controller significantly improves trajectory tracking accuracy and robustness in challenging driving conditions.

Keywords:
autonomous vehicleshigh robustnessimproved grey wolf optimizationimproved particle swarm optimizationsliding mode controltrajectory trackingvector field guidance law

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Autonomous driving requires high-accuracy trajectory tracking, especially in complex scenarios.
  • Existing control methods face challenges in achieving desired performance and robustness.
  • Optimization algorithms are crucial for enhancing controller performance.

Purpose of the Study:

  • To develop an advanced control scheme for autonomous vehicles.
  • To improve trajectory tracking accuracy and robustness in challenging driving conditions.
  • To optimize the controller using enhanced metaheuristic algorithms.

Main Methods:

  • Proposed an adaptive sliding mode controller (SMC).
  • Developed an improved particle swarm optimization (PSO) algorithm with modified inertial weight and learning rates.
  • Created an enhanced grey wolf optimization (GWO) algorithm incorporating speed and memory mechanisms, inspired by PSO.
  • Utilized an expanded vector field guidance law for error calculation.
  • Validated controller stability using Lyapunov's approach.

Main Results:

  • The enhanced PSO and GWO algorithms successfully optimized the adaptive SMC.
  • The proposed control scheme demonstrated superior performance compared to existing controllers.
  • Achieved more precise trajectory tracking, faster convergence, and enhanced robustness.
  • Lyapunov's approach confirmed the stability of the developed control strategies.

Conclusions:

  • The proposed adaptive SMC, optimized by improved PSO and GWO, offers a robust solution for autonomous driving trajectory tracking.
  • The enhanced optimization algorithms effectively address limitations of standard PSO and GWO.
  • This approach significantly advances the potential for widespread adoption of autonomous driving technology.