This paper addresses the challenges of distributed formation control in
multiple mobile robots, introducing a novel approach that enhances real-world
practicability. We first introduce a distributed estimator using a variable
structure and cascaded design technique, eliminating the need for derivative
information to improve the real time performance. Then, a kinematic tracking
control method is developed utilizing a bioinspired neural dynamic-based
approach aimed at providing smooth control inputs and effectively resolving the
speed jump issue. Furthermore, to address the challenges for robots operating
with completely unknown dynamics and disturbances, a learning-based robust
dynamic controller is developed. This controller provides real time parameter
estimates while maintaining its robustness against disturbances. The overall
stability of the proposed method is proved with rigorous mathematical analysis.
At last, multiple comprehensive simulation studies have shown the advantages
and effectiveness of the proposed method.

本论文针对多移动机器人的分布式编队控制提出了一种提高现实可行性的新方法，首先采用变结构和级联设计技术引入分布式估计器，消除了对导数信息的需求以提高实时性能，然后采用仿生神经动力学方法开发了一种运动学跟踪控制方法，旨在提供平滑的控制输入并有效解决速度跳跃问题，此外还提出了一种基于学习的鲁棒动态控制器，以解决在完全未知动力学和干扰下操作的机器人的挑战，该控制器在实时参数估计的同时保持了其对干扰的鲁棒性，并通过严密的数学分析证明了所提方法的整体稳定性，最后，多个综合仿真研究表明了所提方法的优势和有效性。