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Related Experiment Video

Updated: Sep 13, 2025

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Design of Adaptive LQR Control Based on Improved Grey Wolf Optimization for Prosthetic Hand.

Khaled Ahmed^1,2, Ayman A Aly^3,2, Mohamed O Elhabib^4,2

¹Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Biomimetics (Basel, Switzerland)

|July 25, 2025

Summary

This study introduces an Improved Grey Wolf Optimization (IGWO)-tuned Linear Quadratic Regulator (LQR) for advanced control of multi-fingered robotic hands (MFRHs). The IGWO-LQR significantly enhances precision and dynamic response for assistive technologies.

Keywords:

IGWO disabled people optimal control prosthetic hand

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Last Updated: Sep 13, 2025

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Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

Published on: May 20, 2020

Area of Science:

Robotics and Control Systems
Assistive Technology
Optimization Algorithms

Background:

Multi-fingered robotic hands (MFRHs) are vital assistive technologies for individuals with upper-limb disabilities.
Precise and stable control of MFRHs remains a significant engineering challenge.
Existing control methods often struggle to meet the demands for high-performance robotic hand operation.

Purpose of the Study:

To enhance the control performance of MFRHs using an optimized control strategy.
To systematically tune a Linear Quadratic Regulator (LQR) controller for improved precision and stability.
To validate the proposed controller's effectiveness against established methods under various operational conditions.

Main Methods:

Developed a dynamic model of the MFRH using Denavit-Hartenberg kinematics and Euler-Lagrange equations.
Employed an Improved Grey Wolf Optimization (IGWO) algorithm to systematically determine optimal weighting matrices for the LQR controller.
Benchmarked the performance of the IGWO-tuned LQR against Proportional-Integral-Derivative (PID) and PID with Particle Swarm Optimization (PID-PSO) controllers.

Main Results:

The IGWO-LQR controller achieved significantly faster settling times and zero overshoot for step inputs compared to PID and PID-PSO controllers.
Across all joints, the IGWO-LQR demonstrated superior performance, evidenced by a lower Integrated Absolute Error (IAE) for Joint 3 (0.001334 vs. 0.004695 for PID).
Robustness was confirmed under square-wave, sine, and sigmoid inputs, with the IGWO-LQR consistently providing minimal tracking errors and rapid stabilization.

Conclusions:

The proposed IGWO-LQR framework offers a substantial improvement in the precision and dynamic response of MFRH control systems.
This optimized control strategy holds significant promise for advancing the capabilities of assistive robotic hands.
The IGWO algorithm effectively tunes LQR controllers, outperforming traditional methods in demanding robotic applications.