Motion Control of Mobile Robots Based on Improved Model Predictive Control Algorithm

The widespread application of mobile robots has driven continuous and in-depth research into their motion control to achieve higher stability and navigation accuracy. In this study, an innovative control method is proposed by integrating the Adaptive Particle Swarm Optimization, algorithm into the Model Predictive Control (MPC) framework. A comprehensive comparative analysis is conducted between this approach and the conventional Particle Swarm Optimization (PSO) method. Simulation results demonstrate that the improved PSO (IPSO)-based MPC method significantly outperforms the conventional PSO optimization in terms of tracking performance, stability, and navigation accuracy. The proposed approach exhibits greater adaptability and robustness in realtime motion control of mobile robots. Improved particle swarm optimization MPC showcases enhanced adaptability to changes and the ability to maintain a stable motion trajectory.