I&I Based An Adaptive MPC Approach for An Uncertain Underwater Vehicle Model

This paper considers adaptive model predictive control of an underwater vehicle with an uncertain discrete-time model that contains a singular regressor matrix complicating to estimate uncertain parameters online. As the first, immersion and invariance-based estimator is designed via a state augmentation approach to remove the singularity of the regressor matrix, and stability of the estimator is investigated. Then, using the information coming from the designed estimator, an adaptive linear model predictive control is established for reference trajectory tracking considering some hard and soft constraints. The performance of the proposed estimator and adaptive model predictive control is presented by simulation results.