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

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.