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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
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Related Experiment Video

Updated: Oct 12, 2025

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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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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Dual-Rate Extended Kalman Filter Based Path-Following Motion Control for an Unmanned Ground Vehicle: Realistic

Rafael Carbonell1, Ángel Cuenca1, Vicente Casanova1

  • 1Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain.

Sensors (Basel, Switzerland)
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a dual-rate Kalman filter for unmanned ground vehicle (UGV) path following. The enhanced estimation allows for faster control, significantly improving path-following accuracy compared to slower methods.

Keywords:
Kalman filterunmanned ground vehiclevehicle modeling and simulation

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Estimation Theory

Background:

  • Unmanned Ground Vehicles (UGVs) require precise path-following capabilities for autonomous operation.
  • Slower sensor rates for position and orientation limit the performance of dynamic controllers.
  • Existing methods struggle to achieve desired control requirements due to sensor data latency.

Purpose of the Study:

  • To propose a novel dual-rate extended Kalman filtering technique for UGV path-following control.
  • To enable a fast-rate dynamic controller by estimating high-frequency position and orientation data.
  • To enhance the path-following performance of a two-wheel drive UGV.

Main Methods:

  • Implementation of a dual-rate extended Kalman filter (DREKF) to estimate unavailable fast-rate measurements.
  • Design of a fast-rate dynamic controller utilizing the DREKF's estimates.
  • Development of a validated Simscape Multibody™ simulation model incorporating realistic vehicle dynamics and sensor limitations.

Main Results:

  • The dual-rate controller significantly outperformed the slow-rate controller in path-following accuracy.
  • The DREKF effectively handled non-linear vehicle dynamics and modeling uncertainties.
  • Simulation results, validated against real-world data, demonstrated the efficacy of the proposed control solution.

Conclusions:

  • Dual-rate Kalman filtering is a viable approach to overcome sensor data rate limitations in UGV control.
  • The proposed method enables high-performance path following for UGVs.
  • The validated simulation model provides a robust platform for further research in UGV control systems.