What question did this study set out to answer?

The research aims to enhance the performance of collaborative robots using advanced control methodologies.

February 2, 2026

Design and Analysis of an Intelligent Control System for Collaborative Robots Using Model Predictive Control (MPC) and Particle Swarm Optimization (PSO)"

Key Points

The research aims to enhance the performance of collaborative robots using advanced control methodologies.
Proposed integration of Model Predictive Control (MPC) with Particle Swarm Optimization (PSO)
Real-time optimization of MPC parameters using PSO
Simulation tests to compare hybrid MPC+PSO with traditional MPC
Hybrid MPC+PSO system reduced RMSE by 57.8%
Energy consumption decreased by 22%
Enhanced accuracy and robustness of robotic control

Abstract

Background: Collaborative robots require accurate and adaptive control strategies to operate effectively in dynamic environments. Goal: This work aims to improve trajectory tracking accuracy and system efficiency using a hybrid control strategy. Method: We propose a novel integration of Model Predictive Control (MPC) with Particle Swarm Optimization (PSO), where PSO is used to optimize MPC parameters in real-time. Results: Simulations show that the hybrid MPC+PSO system outperforms traditional MPC, reducing RMSE by 57.8% and energy consumption by 22%. Conclusion: The proposed method enhances the accuracy and robustness of collaborative robotic control and is suitable for real-world deployment.

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