Parameter Optimization Design of MPC Controller in AUV Motion Control Based on Improved Black-Winged Kite Algorithm

This study proposes an improved Black-winged Kite Algorithm (IBKA) for the parameter optimization of the Model Predictive Control (MPC) controller in Autonomous Underwater Vehicles (AUVs). To tackle the optimization challenges associated with the weight matrices and prediction horizon in the MPC controller, IBKA innovatively integrates the Lens Opposition-Based Learning (LOBL) strategy with the BKA. Specifically, after the migration behavior of BKA, the LOBL strategy is introduced to generate new individuals, and on this basis, the optimal individual is retained as the leader of the black-winged kite. In the experimental scenarios of AUV heading control and depth tracking, the optimization effect of the IBKA-MPC controller is evaluated. The results indicate that, in the heading control experiment, for the MPC controller optimized by IBKA, the Integral of Absolute Error (IAE) and Integral of Time-weighted Absolute Error (ITAE) of the heading angle decreased by a maximum of 6.29% and 18.24%, respectively, compared with the MPC controller under non-optimized parameters. In the depth tracking experiment, for the MPC controller optimized by IBKA, the IAE and ITAE of the depth decreased by 91.86% and 94.78%, respectively, compared with the MPC controller under non-optimized parameters. Meanwhile, through comparative experiments with four classical optimization algorithms, it is verified that the IBKA with the LOBL strategy introduced has a better optimization effect on the parameters of the MPC controller than classical optimization algorithms.