Towards Asynchronous ADMM for Distributed Model Predictive Control of Nonlinear Systems

This paper presents an asynchronous formulation of the alternating direction method of multipliers (ADMM) for solving optimal control problems that arise in distributed model predictive control (DMPC). The need for synchronization limits the applicability of DMPC to large-scale or fast nonlinear systems, since all agents have to wait for the slowest one. The main idea of the asynchronous formulation is to use data from previous iterations instead of waiting for the current data. A heuristic is proposed to adapt the maximum allowed delay depending on the residuals of the consistency constraints. The convergence behaviour is investigated in a numerical simulation and the execution time is evaluated using distributed hardware and TCP communication. The results show that the reduced execution time more than compensates for the disadvantage of slower convergence compared to the synchronous formulation.