Model predictive tracking control based on adaptive sliding mode constraints for unmanned underwater vehicles

This study presents an improved model predictive control (MPC) approach for unmanned underwater vehicle trajectory tracking, specifically in an environment with ocean current disturbance. The proposed control strategy consists mainly of two MPC frameworks. Each MPC framework additionally attaches a nonlinear constraint used to further optimize the results. The constraint of the first part uses the Lyapunov direct method, while the constraint in the second part is based on the adaptive sliding mode controller, which has a decisive impact on the performance of the whole controller. These constraints give the system the ability to optimize the force output, increase the robustness, and reduce the tracking error. To evaluate the performance of the proposed controller, simulation experiments are conducted, comparing it with commonly used controllers. The results show the characteristics of the proposed method, including stability in the presence of undetectable disturbances and the advantage of effectively mitigating thrust saturation and oscillation caused by motion coupling.