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Updated: Feb 8, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Reset Controller Design Based on Error Minimization for a Lane Change Maneuver.

Miguel Cerdeira1, Pablo Falcón2, Emma Delgado3

  • 1Department of Systems Engineering and Automation, School of Industrial Engineering, University of Vigo, 36310 Vigo, Spain. mcerdeira@uvigo.es.

Sensors (Basel, Switzerland)
|July 11, 2018
PubMed
Summary
This summary is machine-generated.

Reset control enhances intelligent vehicle performance by overcoming linear system limitations for smoother, faster lane changes. This study explores optimal reset strategies for improved ride quality and responsiveness.

Keywords:
CarSimISE minimizationlane change maneuverreset control

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Area of Science:

  • Control Systems Engineering
  • Automotive Engineering
  • Intelligent Transportation Systems

Background:

  • Intelligent vehicles require control systems capable of handling diverse driving scenarios, from smooth to aggressive maneuvers.
  • Linear control systems face fundamental limitations in achieving both smooth responses and meeting performance constraints like rise time and overshoot.
  • Intermediate scenarios demand smooth yet constrained responses, a challenge for traditional linear controllers.

Purpose of the Study:

  • To investigate the efficacy of reset control in alleviating limitations of linear controllers for intelligent vehicle maneuvers.
  • To optimize reset control strategies for lane change maneuvers, ensuring ride quality and swift response under demanding conditions.
  • To compare different reset conditions and magnitudes for enhanced controller performance.

Main Methods:

  • Exploration of reset control strategies, including zero crossing, fixed reset band, and variable reset band conditions.
  • Comparison of full-reset techniques with Lyapunov-based error minimization for determining optimal reset magnitude.
  • Utilizing genetic algorithms to design the base linear controller integrated with reset actions.
  • Validation of proposed controllers using CarSim vehicle dynamics software.

Main Results:

  • Reset control effectively mitigates linear system limitations, enabling smoother and faster lane change maneuvers.
  • Specific reset conditions and magnitudes were identified as optimal for balancing ride quality and responsiveness.
  • The Lyapunov-based error minimization method proved effective in calculating optimal reset percentages.
  • Controllers designed with genetic algorithms and validated in CarSim demonstrated superior performance.

Conclusions:

  • Reset control offers a viable solution for enhancing the performance of intelligent vehicle control systems, particularly in challenging maneuvers.
  • The study provides a framework for selecting and implementing effective reset control strategies for automotive applications.
  • Further research can explore advanced reset control techniques for even more complex driving scenarios.