Stochastic Model Predictive Control for Motion Control of an Underactuated Underwater Vehicle

Most dynamic system operate under uncertainties such as external disturbances, measurement noise and modeling uncertainties. Failure to account for these uncertainties in the controller design may lead to performance degradation in real-world applications. As a result, SMPC (Stochastic Model Predictive Control), which provides a probabilistic framework for the nonlinear model predictive control of systems with stochastic uncertainty, has attracted much attention. The main challenge of SMPC is the efficient propagation of probabilistic uncertainty through system dynamics. In this paper, unscented transformation was used to efficiently estimate the distribution of states under uncertainties, and it is applied to trajectory tracking and obstacle avoidance of an unmanned underwater vehicle. The performance of the proposed algorithm was demonstrated through numerical simulations.