This paper addresses the challenging problem of composite synchronization and learning control in a network of multi-agent robotic manipulator systems operating under heterogeneous nonlinear uncertainties within a leader-follower framework. A novel two-layer distributed adaptive learning control strategy is introduced, comprising a first-layer distributed cooperative estimator and a second-layer decentralized deterministic learning controller. The primary objective of the first layer is to facilitate each robotic agent's estimation of the leader's information. The second layer is responsible for both enabling individual robot agents to track desired reference trajectories and accurately identifying and learning their nonlinear uncertain dynamics. The proposed distributed learning control scheme represents an advancement in the existing literature due to its ability to manage robotic agents with completely uncertain dynamics including uncertain mass matrices. This framework allows the robotic control to be environment-independent which can be used in various settings, from underwater to space where identifying system dynamics parameters is challenging. The stability and parameter convergence of the closed-loop system are rigorously analyzed using the Lyapunov method. Numerical simulations conducted on multi-agent robot manipulators validate the effectiveness of the proposed scheme. The identified nonlinear dynamics can be saved and reused whenever the system restarts.

翻译：本文研究了领导者-跟随者框架下，存在异质非线性不确定性的多机器人机械臂系统网络中复合同步与学习控制的挑战性问题。提出了一种新颖的两层分布式自适应学习控制策略，包括第一层分布式协同估计器和第二层分散确定性学习控制器。第一层的主要目标是促进每个机器人智能体对领导者信息的估计。第二层负责使单个机器人智能体既能跟踪期望的参考轨迹，又能精确识别和学习其非线性不确定动力学。所提出的分布式学习控制方案相较于现有文献的进步在于，它能够处理具有完全不确定动力学（包括不确定质量矩阵）的机器人智能体。该框架使机器人控制具有环境独立性，可应用于从水下到太空等系统动力学参数辨识具有挑战性的各种场景。利用Lyapunov方法严格分析了闭环系统的稳定性和参数收敛性。对多智能体机器人机械臂进行的数值仿真验证了所提出方案的有效性。识别出的非线性动力学可被保存并在系统重新启动时重复使用。