Motion Planning and Tracking MPC for Multiagent Systems: A Dynamic Affine Formation Approach
提出一种在线调整仿射参数的编队运动规划算法，结合人工势场实现多障碍环境下的自重构避障，并设计分布式模型预测控制器，利用历史控制输入避免代数环，保证稳定性与可行性。
In complex and variable terrains, affine formation control, with its flexible formation adjustment ability, can achieve various formation shapes to adapt well to the environment. Notably, the existing affine formation research based on stress matrices require the variation parameters for translation, rotation, scaling, and shearing of formations to be predesigned offline. To address this, we propose a novel method for online affine parameter adjustment that enables self-reconfiguration of formations in multiobstacle environments. By adopting artificial potential field environment excitation, the proposed motion planning algorithm can dynamically adjust the affine transformation parameters online, and realize the self-reconfiguration of formation shape to avoid collision. Then, a distributed model predictive controller is proposed for multiagent systems, which actively utilizes historical control input information to flexibly adjust controller performance while avoiding algebraic loops between neighboring agent controllers. The algorithm separates stability and performance optimization within the nonlinear model predictive control framework, ensuring both the feasibility and stability of the underlying optimization. Finally, the simulation results confirm the effectiveness of the proposed controller.