Discrete-Time Distributed Optimal Formation Algorithms for Multiagent Systems With Nonlinear Inequality Constraints

Yi Huang, Jiacheng Kuai, Ziyang Meng, Jian Sun

Abstract: This article studies the distributed optimal formation problems for multiagent systems subject to nonlinear inequality constraints. This problem can be formulated into a nonlinear mixed-integer programming (NMIP) problem. We first decompose the NMIP problem into a formation optimal matching problem, which is actually an integer linear programming (ILP) problem, and an optimal formation reference center problem described as a constrained quadratic optimization problem. Subsequently, we develop a discrete-time perturbation-based distributed dual consensus ADMM (PDC-ADMM) algorithm, which achieves an optimal integer solution to the ILP problem and eliminates the unmatched phenomenon. In addition, we propose a distributed optimistic gradient descent ascent (D-OGDA) algorithm with a constant step size, which guarantees exact convergence to the optimal formation reference center. Finally, three simulation examples are carried out to demonstrate the effectiveness of the developed algorithms.

External IDs:dblp:journals/tsmc/HuangKMS26