Robust NMPC for Uncalibrated IBVS Control of AUVs

Abstract

Image-based visual servoing (IBVS) applications for autonomous underwater vehicles (AUVs) face significant challenges, including frequent recalibration and lack of constraint handling ability. This letter introduces a novel nonlinear model predictive control (NMPC) approach that integrates the Broyden method for uncalibrated IBVS and incorporates the min-max strategy to tolerate the errors in Jacobian matrix estimation. Our proposed min-max NMPC-IBVS framework estimates the Jacobian matrix online, allowing for continuous adaptation to the underwater environment without the need for prior calibration. This approach significantly enhances computational efficiency and robust control performance, enabling real-time uncalibrated applications. A rigorous proof of recursive feasibility is provided in this letter, ensuring that our NMPC-IBVS method consistently finds feasible optimal solutions that satisfy all constraints over time. Simulation results show that the proposed method is able to respect all design constraints in the AUV IBVS control and achieve robust stability with boosted computational efficiency.