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

To enhance the tracking accuracy of AGVs, this paper proposes an online adaptive optimization strategy for MPC weight parameters based on the Improved Sparrow Search Algorithm (ISSA). Firstly, the kinematic and three-degree-of-freedom dynamic models of the AGV are established, and a trajectory tracking MPC controller based on an incremental model is designed. On this basis, the Tent chaotic mapping is introduced to initialize the population and enhance its diversity. A dynamic disturbance factor is incorporated into the position update of the discoverers, and a Cauchy mutation operator is introduced during the follower stage. These improvements effectively balance the algorithm's global exploration and local exploitation capabilities, preventing premature convergence. This method uses a composite indicator of lateral and heading tracking errors as the fitness function to periodically optimize the MPC weight parameters online, thereby adapting to the control requirements of the AGV under different working conditions. Finally, validation through co-simulation using Gazebo and Rviz under the ROS framework, along with experiments on a forklift-type AGV, shows that the proposed method offers significant advantages in improving trajectory tracking accuracy, accelerating dynamic response, and enhancing adaptability to various working conditions. It thus provides a feasible solution for high-performance trajectory tracking control of AGVs.