Reducing the communication of distributed model predictive control: Autoencoders and formation control - Schiz, Torben; Ebel, Henrik (Control engineering practice, 2025)
Communication remains a key factor limiting the applicability of distributed model predictive control (DMPC) in realistic settings, despite advances in wireless communication. DMPC schemes can require an overwhelming amount of information exchange between agents as the amount of data depends on the length of the predication horizon, for which some applications require a significant length to formally guarantee nominal asymptotic stability. This work aims to provide an approach to reduce the communication effort of DMPC by reducing the size of the communicated data between agents. Using an autoencoder, the communicated data is reduced by the encoder part of the autoencoder prior to communication and reconstructed by the decoder part upon reception within the distributed optimization algorithm that constitutes the DMPC scheme. The choice of a learning-based reduction method is motivated by structure inherent to the data, which results from the data’s connection to solutions of optimal control problems. The approach is implemented and tested at the example of formation control of differential-drive robots, which is challenging for optimization-based control due to the robots’ nonholonomic constraints, and which is interesting due to the practical importance of mobile robotics. The applicability of the proposed approach is presented first in the form of a simulative analysis showing that the resulting control performance yields a satisfactory accuracy. In particular, the proposed approach outperforms the canonical naive way to reduce communication by reducing the length of the prediction horizon. Moreover, it is shown that numerical experiments conducted on embedded computation hardware, with real distributed computation and wireless communication, work well with the proposed way of reducing communication even in practical scenarios in which full communication fails, as the full-size data messages are not communicated in a timely-enough manner. This shows an objective benefit of using the proposed communication reduction in practice, especially in situations in which a lot of communication happens within a given time span, e.g., because of a large number of agents, a densely connected communication graph, and/or frequent data exchange. •Effective learning-based communication-reduction approach for nonlinear distributed iterative optimization.•Enabling functioning of distributed model predictive control in conditions where unreduced communication fails completely.•Realistic testing with lossy wireless communication between single-board computers as used in robotics.•Potential applications include robotic formation control, distributed control of smart grids, and IoT applications.