Robust motion control of four-vector-thruster AUV “Aurora” under disturbances: optimized ESO-FAISMC control with thrust allocation optimization

Against challenges from internal model inaccuracies and external environmental disturbances affecting vector Autonomous Underwater Vehicles (AUVs) tracking, this paper focuses on the team-developed 4-vector AUV Aurora, establishing its kinematic/dynamic models. It proposes an optimized thrust allocation strategy and a control framework combining improved Extended State Observer (ESO) and Fuzzy Adaptive Integral Sliding Mode Control (FAISMC): the former reduces initial estimation errors for accurate disturbance estimation; the latter uses fuzzy inference to regulate gains, suppressing chattering. Lyapunov analysis verifies stability. Numerical simulations show that ESO-FAISMC reduces average errors by 79.09% vs Integral Sliding Mode Control (ISMC) and 51.36% vs Extended State Observer based Integral Sliding Mode Control (ESO-ISMC), and root mean square errors by 64.54% and 42.46% respectively. The proposed ESO-FAISMC can effectively counteract disturbances, achieving high-precision tracking, stable motion control. Experiments confirm it meets Aurora’s underwater control needs.