Distributed Model Predictive Control via Separable Optimization in Multiagent Networks

We present a distributed model predictive control method, which enables a group of agents to compute their control inputs locally while communicating with their neighbors over a communication network. While many distributed model predictive control methods require a central station for some coordination or computation of the optimization variables, our method does not require a central station, making our approach applicable to a variety of communication network topologies. With our method, each agent solves for its control inputs without solving for the control inputs of other agents, allowing for efficient optimization by each agent, unlike some other distributed methods. Furthermore, our method attains the linear convergence to the optimal control inputs in convex model predictive control problems, improving upon the sublinear convergence rates provided by some other distributed methods such as dual decomposition methods. Moreover, our algorithm provides a closed-loop controller for convex model predictive control problems with affine constraints. We demonstrate our method in both convex and nonconvex model predictive control problems in wireless transceiver alignment and satellite deployment, where we show the robustness of our method to time delays.