Dynamic-priority-based DMPC with an occupancy grid for mobile systems

Abstract

Distributed model predictive control is a wide-spread control method for systems such as mobile robots or vehicles, which operate in a shared space. Each system computes in an iterative way its control actions by locally solving an optimal control problem with regards to an objective function and subject to constraints arising from the system itself and from shared and possibly quantised information. Within state-of-the-art methods, local problems are solved sequentially and applied in a fixed order, which may not be optimal considering the overall system performance. We introduce dynamic priority rules, which are evaluated locally to establish a dynamic sequence of systems in each time instant. To avoid deadlocks possibly arising from periodic reordering, the idea is complemented by a memory rule. The impact of the proposed methods with and without quantisation is analysed via numerical simulations, revealing shorter convergence times in comparison to the state-of-the art approach.