A Probabilistic Control Framework and Convergence Analysis for Multiagent Systems

This article presents an advancement of the probabilistic control framework to enable formation control in multiagent systems featuring both linear and nonlinear dynamics. The approach integrates decentralized controllers with probabilistic message passing, enabling agents to adapt their control strategies based on local information and interactions with neighboring agents. A key contribution involves transforming the nonlinear system dynamics into an affine structure, which allows computation of locally optimal control actions within Gaussian probabilistic models. This results in a generalized control approach that parallels classical methods for linear systems, while extending applicability to nonlinear settings. The proposed method’s stability and performance are validated through convergence analysis and numerical simulations, emphasizing its ability to achieve accurate formation configurations across a range of operational scenarios.