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

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.