Distributed Model Predictive Consensus Control of Unmanned Surface Vehicles with Post-Verification

Abstract

In this paper, the consensus control of unmanned surface vehicles (USVs) is investigated by employing a distributed model predictive control approach. A hierarchical control structure is considered during the controller design, where the upper layer determines the reference signals of USV velocities while the lower layer optimizes the control inputs of each USV. The main feature of this work is that a post-verification procedure is proposed to address the failure states caused by local errors or cyberattacks. Each USV compares the actual state and the predicted one obtained at the previous moment. This allows the estimation of local perturbations. In addition, the failure state of the USV can also be determined if a preset condition is satisfied, thus forcing a change in the communication topology and avoiding further impact. Simulations show that the proposed method is effective in USV formation control. Compared with the method without post-verification, the proposed approach is more robust when failure states occur.