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Related Concept Videos

Relative Motion Analysis - Acceleration01:10

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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Sliding-mode control based on prescribed performance function and its application to a SEA-Based lower limb

Feilong Zhang1,2, Tian Wang3, Liang Zhang4

  • 1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.

Frontiers in Robotics and AI
|March 19, 2025
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Summary

This study introduces an adaptive sliding-mode control for discrete systems, improving tracking accuracy. The enhanced method adapts to system changes, maintaining performance within defined bounds for better control.

Keywords:
lower limb exoskeletonpredefined convergence zoneprescribed performancesliding-mode controltransient characteristics

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Area of Science:

  • Control Engineering
  • Robotics
  • Systems Science

Background:

  • Sliding-mode control (SMC) offers robustness but often has limitations in discrete-time systems.
  • Prescribed Performance Control (PPC) ensures tracking errors stay within defined bounds but can be rigid.
  • Existing PPC methods for discrete systems may lack adaptability to model uncertainties.

Purpose of the Study:

  • To develop a more universally applicable and adaptive sliding-mode controller for discrete-time systems.
  • To enhance the prescribed performance control method by addressing its fixed-structure limitations.
  • To improve system transient characteristics like smoothness and response speed even with model inaccuracies.

Main Methods:

  • A novel sliding-mode control strategy is proposed, separating the controller into two distinct parts.
  • The prescribed performance control (PPC) principle is analyzed and adapted for discrete-time systems.
  • Model-based control methods are integrated to replace the linear component, allowing system-specific adaptation.
  • A penalty constant is introduced to adjust transient characteristics within the prescribed performance domain.

Main Results:

  • The proposed controller maintains tracking error within a predefined convergence zone.
  • The controller demonstrates enhanced adaptability to inaccurate system models.
  • Improved system smoothness and response speed are achieved through the penalty constant.
  • Effectiveness is validated through numerical simulations and a practical lower limb exoskeleton experiment.

Conclusions:

  • The developed adaptive sliding-mode controller effectively addresses limitations of fixed-structure PPC in discrete systems.
  • The method enhances system performance by allowing adaptation to model uncertainties and tuning transient behavior.
  • The approach offers a more versatile and robust solution for control engineering applications, particularly in robotics.