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Updated: Jan 13, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

適応型RBFニューラルネットワークによる産業用マニピュレータのローカルモデル近似に基づく追従制御

Guirong Han^1,2, Cai Huang¹, Wei Xiao¹

¹School of Mechanical & Electrical Engineering, Wuhan Institute of Technology, Wuhan, 430205, Hubei, China.

Scientific reports

|January 9, 2026

Abstract:

A Lyapunov-stability-guaranteed local model adaptive RBF neural network control algorithm is proposed for industrial robots. This algorithm eliminates the need for an exact plant mathematical model, enabling real-time learning and compensation of system nonlinearities and uncertainties. An adaptive control law adjusts neural network parameters online to achieve high-precision tracking control of robot manipulators. Particle Swarm Optimization (PSO) is employed to optimize the RBF basis width parameters. Validation was performed using the ABB IRB1600 industrial robot, modeled and simulated in MATLAB Simscape. Real-time trajectory tracking was demonstrated, with ADAMS co-simulation used for further verification. Results demonstrate the algorithm's effectiveness in reducing tracking errors and enhancing robustness and adaptability, while maintaining stability within the Lyapunov framework.

キーワード:

適応型RBFニューラルネットワーク制御産業用ロボットローカルモデル近似リアプノフ安定性 PSO最適化

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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

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

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Published on: November 6, 2015

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