Towards a Modular Framework for Distributed Model Predictive Control of Nonlinear Neighbor-Affine Systems

The development of real-time capable distributed model predictive controllers (DMPC) for nonlinear systems requires considerable effort both for the numerical solution and for the practical implementation, which is one of the reasons why DMPC so far is rarely found in practice. With the aim of simplifying the development process, this paper presents a modular framework for DMPC of nonlinear neighbor-affine systems. The numerical solution is based on the alternating direction method of multipliers (ADMM). The local optimization problems are solved using the toolbox GRAMPC which implements a gradient-based augmented Lagrangian algorithm that is particularly suited for embedded applications. The performance of the DMPC framework is evaluated for two scalable nonlinear distributed systems, whereby the scalability and the plug-and-play functionality are demonstrated.