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Related Experiment Video

Updated: Jul 20, 2025

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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Double-time-scale non-probabilistic reliability-based controller optimization for manipulator considering motion

Lei Wang1, Zheng Zhou2, Jiaxiang Liu2

  • 1National Key Laboratory of Strength and Structural Integrity, Institute of Solid Mechanics, School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China; Aircraft and Propulsion Laboratory, Ningbo Institute of Technology, Beihang University, Ningbo 315100, China.

ISA Transactions
|August 4, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel optimization method to enhance manipulator system performance by addressing motion errors and wear growth. The approach ensures reliability against both time-dependent and time-independent factors, improving control and lifespan.

Keywords:
Adaptive subinterval collocation methodDouble-time-scaleManipulatorNon-probabilistic reliabilityNon-uniform clearance

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Area of Science:

  • Robotics and Control Systems
  • Mechanical Engineering
  • Reliability Engineering

Background:

  • Manipulator system performance is critically affected by motion error and wear growth, impacting control precision and operational lifespan.
  • Existing methods often fail to comprehensively address the multi-scale temporal nature of these reliability factors.

Purpose of the Study:

  • To propose a double-time-scale non-probabilistic reliability (DTSNPR)-based optimization method for manipulator controllers.
  • To integrate time-dependent reliability (TDR) for motion error and time-independent reliability (TIR) for wear growth into a unified framework.
  • To enhance the precision and efficiency of uncertainty propagation analysis for nonlinear manipulator systems.

Main Methods:

  • Development of the DTSNPR method to simultaneously evaluate and optimize controllers considering both motion error and wear growth.
  • Adoption of the adaptive subinterval collocation method (ASICM) to manage highly nonlinear uncertainty propagation problems.
  • Application and validation of the proposed methods on three numerical manipulator systems.

Main Results:

  • The DTSNPR method successfully ensured manipulator systems operated under predefined reliability levels for both motion error and wear growth.
  • The ASICM demonstrated significant advantages in computational cost (1% of Monte Carlo) and precision (0.4% error) compared to traditional methods.
  • The integrated approach provides a robust framework for optimizing manipulator reliability across different time scales.

Conclusions:

  • The proposed DTSNPR-based optimization method offers a comprehensive solution for evaluating and enhancing manipulator system reliability.
  • The ASICM provides a computationally efficient and accurate approach for analyzing nonlinear uncertainty propagation in robotic systems.
  • This research contributes to the development of more reliable and longer-lasting manipulator systems in various industrial applications.