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Related Experiment Video

Updated: May 23, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Enhanced accuracy and adaptability: An ISSA-optimized MPC approach for AGV trajectory tracking.

Tan Zhang¹, Chengjun Ding¹, Tengfei Ma¹

¹School of Mechanical Engineering, Hebei University of Technology, Tianjin, China.

|May 21, 2026

Summary

This study introduces an adaptive strategy using the Improved Sparrow Search Algorithm (ISSA) to optimize Model Predictive Control (MPC) weights for enhanced Automated Guided Vehicle (AGV) trajectory tracking accuracy.

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Area of Science:

Robotics
Control Systems Engineering
Artificial Intelligence

Background:

Automated Guided Vehicles (AGVs) require precise trajectory tracking for efficient operation.
Existing Model Predictive Control (MPC) methods can struggle with adaptability to varying conditions.
Online optimization of MPC parameters is crucial for robust AGV control.

Purpose of the Study:

To develop an online adaptive optimization strategy for MPC weight parameters in AGVs.
To improve the trajectory tracking accuracy and dynamic response of AGVs.
To enhance the adaptability of AGV control systems to diverse working environments.

Main Methods:

Established kinematic and dynamic models for a three-degree-of-freedom AGV.
Designed a trajectory tracking MPC controller utilizing an incremental model.
Implemented an Improved Sparrow Search Algorithm (ISSA) with Tent chaotic mapping, dynamic disturbance factors, and Cauchy mutation for population initialization and optimization.
Utilized a composite indicator of lateral and heading tracking errors as the fitness function for online MPC weight optimization.

Main Results:

The ISSA effectively balanced global exploration and local exploitation, preventing premature convergence.
Online optimization of MPC weights adapted the control strategy to different AGV working conditions.
Co-simulation (Gazebo/Rviz/ROS) and experimental validation demonstrated significant improvements in trajectory tracking accuracy.
The proposed method accelerated dynamic response and enhanced adaptability compared to conventional approaches.

Conclusions:

The proposed online adaptive optimization strategy using ISSA provides a feasible solution for high-performance AGV trajectory tracking.
This method significantly enhances AGV control performance, particularly in terms of accuracy and adaptability.
The approach offers a practical way to improve the reliability and efficiency of AGVs in complex environments.