Enhancing robustness and noise rejection in flexible joint manipulators: an optimized sliding mode controller with enhanced gray wolf optimization for trajectory tracking

Muhammad I. Azeez, S. Elnaggar, A. M.M. Abdelhaleem, Kamal A.F. Moustafa, Khaled R. Atia

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The objective of this study is to enhance the performance of a nonlinear three-rigid-link manipulator (RLM) with a focus on trajectory tracking, robustness against disturbances and noises, and adaptability to joint flexibility. To achieve this, we have employed an optimized sliding mode controller with a proportional integral derivative (PID) sliding manifold. The tuning process involves selecting the critical gains of the controller that minimizes the integral time absolute error (ITAE), serving as the objective function (OBJF) to optimize the performance of the robot manipulator. To identify the optimal gains of the controller, we have utilized a new optimization algorithm known as memory enhanced linear population size reduction gray wolf optimization (MELGWO). The efficacy of this algorithm is compared to other existing optimization methods in the literature. Moreover, this research has delved into the impact of joint flexibility on the robot system’s performance. Encouragingly, the results demonstrate that the optimized SMC–PID with MELGWO adaptation can effectively address joint flexibility while maintaining acceptable performance levels.

Original languageEnglish
Article number546
JournalJournal of the Brazilian Society of Mechanical Sciences and Engineering
Volume45
Issue number10
DOIs
Publication statusPublished - Oct 2023
Externally publishedYes

Keywords

  • Enhanced gray wolf optimizer
  • Flexible joint manipulator
  • Proportional integral derivative
  • Rigid link manipulator
  • Sliding mode control
  • Trajectory tracking

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • General Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

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