This paper addresses the problem of composite synchronization and learning control in a network of multi-agent robotic manipulator systems with heterogeneous nonlinear uncertainties under 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 first layer is to facilitate each robotic agent's estimation of the leader's information. The second layer is responsible for both controlling individual robot agents to track desired reference trajectories and accurately identifying/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 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 validate the effectiveness of the proposed scheme.

翻译：本文研究了领导者-跟随者框架下具有异构非线性不确定性的多智能体机械臂系统网络的复合同步与学习控制问题。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。第一层用于促进每个机器人智能体对领导者信息的估计，第二层则负责控制各机器人智能体跟踪期望参考轨迹，并精确辨识/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之处在于，其能够处理包含不确定质量矩阵在内的完全不确定动力学的机器人智能体。这使得机器人控制能够实现环境无关性，可应用于从水下到太空等系统动力学参数难以辨识的各种场景。利用李雅普诺夫方法严格分析了闭环系统的稳定性与参数收敛性。数值仿真验证了所提方案的有效性。