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.

翻译：本文针对多移动机器人分布式编队控制面临的挑战，提出了一种增强实际应用可行性的新方法。首先，采用变结构设计结合级联设计技术构建分布式估计器，无需导数信息即可提升实时性能。其次，基于生物启发神经动力学方法开发运动学跟踪控制器，可生成平滑控制输入并有效解决速度跳变问题。进一步，针对机器人完全未知动力学与外部扰动问题，设计了一种基于学习的鲁棒动态控制器，该控制器能在保持抗扰动鲁棒性的同时实现参数实时估计。通过严格的数学分析证明了所提方法的整体稳定性。最后，多项综合仿真研究验证了该方法的优势与有效性。