Distributed implementation of nonlinear model predictive control for AUV trajectory tracking

This paper studies the trajectory tracking control of an autonomous underwater vehicle (AUV). We investigate the nonlinear model predictive control (NMPC) method looking for possible approaches to alleviate the heavy computational burden. Novel distributed NMPC algorithms are developed exploiting the dynamic properties of the AUV motion. By appropriately decomposing the original optimization problems into smaller size subproblems and then solving them in a distributed manner, the expected floating point operations (flops) can be reduced dramatically. We show that the convergence of the AUV trajectory can be guaranteed by the proposed contraction constraints in the decomposed subproblems. Recursive feasibility and closed-loop stability are proved. Taking advantage of the guaranteed stability, a real-time distributed implementation algorithm is further developed to automatically trade off between control performance and computational complexity. Extensive simulation studies on the Falcon AUV model demonstrate the effectiveness and robustness of the proposed approach. (C) 2020 Elsevier Ltd. All rights reserved.