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Related Experiment Video

Updated: Jan 20, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive.

Chih-Hong Lin1, Kuo-Tsai Chang2

  • 1Department of Electrical Engineering, National United University, 36063 Miaoli, Taiwan. jhlin@nuu.edu.tw.

Sensors (Basel, Switzerland)
|August 23, 2019
PubMed
Summary
This summary is machine-generated.

A novel micrometer backstepping control system enhances linear motion robot precision by estimating and compensating for uncertainties using an amended neural network and optimized learning rates. This system achieves high-precision control, verified experimentally.

Keywords:
Gottlieb polynomials neural networkLyapunov functionant colony optimizationbackstepping controllinear motion single axis robot machine

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence in Engineering
  • Mechatronics

Background:

  • Linear motion single-axis robot machines are susceptible to various uncertainty disturbances affecting precision.
  • Existing control systems struggle to effectively mitigate lumped uncertainties like external forces, friction, and parameter variations.
  • High-precision control is crucial for applications demanding micrometer-level accuracy.

Purpose of the Study:

  • To develop a micrometer backstepping control system for a linear motion single-axis robot drive system.
  • To estimate and compensate for lumped uncertainties using an amended recurrent Gottlieb polynomials neural network (NN) and an altered ant colony optimization (AACO).
  • To achieve high-precision control performance with micrometer resolution.

Main Methods:

  • Implementation of a digital signal processor (DSP)-based current-regulation pulse width modulation (PWM) control scheme.
  • Development of a micrometer backstepping control system incorporating an amended recurrent Gottlieb polynomials NN with a compensated controller.
  • Utilization of a Lyapunov function-based adaptive law for the NN to estimate lumped uncertainty.
  • Proposal of a novel error-estimated law for the compensated controller to address estimation errors.
  • Application of AACO to optimize NN learning rates for faster convergence.

Main Results:

  • Successfully applied DSP-based PWM control to the robot drive system.
  • The proposed backstepping control system effectively diminished lumped uncertainty effects.
  • High-precision control performance was achieved through accurate uncertainty estimation via the NN's adaptive law.
  • The compensated controller successfully mitigated estimation errors.
  • AACO accelerated the NN's convergent speed by regulating learning rates.

Conclusions:

  • The proposed micrometer backstepping control system, integrating an amended NN and AACO, significantly enhances the precision of linear motion single-axis robot machines.
  • The adaptive NN and compensated controller effectively handle lumped uncertainties, leading to superior control performance.
  • Experimental validation confirms the effectiveness and robustness of the developed control scheme for high-precision applications.