Zeng Danping, Wang Yaonan, Jiang Yiming, Tan Haoran, Miao Zhiqiang, Feng Yun
IEEE Trans Neural Netw Learn Syst. 2025 Jul;36(7):12363-12376. doi: 10.1109/TNNLS.2024.3478215.
Existing cooperative manipulation methods for multiple manipulator systems usually assume that the grasp matrix and the desired trajectory of each manipulator are known in advance. In this work, distributed neural adaptive impedance control (AIC) strategies integrating fully distributed observers are proposed to remove both limitations. Specifically, two fully distributed finite-time observers are designed to estimate the actual and ideal states of the reference point without using global information. The estimates of the grasp matrix and the desired trajectory of each end-effector (EE) are then obtained by kinematic constraints and the estimates of the reference point's states. At the controller development, a distributed adaptive impedance model is established to achieve an adaptive trade-off between tracking performance and compliance. Then, distributed neural network (NN)-based tracking control strategies are developed to asymptotically realize the desired adaptive impedance dynamics in the presence of uncertainties. Additionally, a virtual energy tank (EK) is employed to interact with the impedance system to correct the adaptive impedance laws for system passivity. A simulation for four mobile manipulators tightly cooperative transport an unknown object is carried out to demonstrate the established results.
现有的多机械臂系统协同操作方法通常假定每个机械臂的抓握矩阵和期望轨迹是预先已知的。在这项工作中，提出了集成全分布式观测器的分布式神经自适应阻抗控制（AIC）策略，以消除这两个限制。具体而言，设计了两个全分布式有限时间观测器，在不使用全局信息的情况下估计参考点的实际状态和理想状态。然后，通过运动学约束和参考点状态估计，获得每个末端执行器（EE）的抓握矩阵和期望轨迹的估计值。在控制器设计方面，建立了分布式自适应阻抗模型，以在跟踪性能和柔顺性之间实现自适应权衡。然后，开发了基于分布式神经网络（NN）的跟踪控制策略，以在存在不确定性的情况下渐近实现期望的自适应阻抗动态。此外，采用虚拟能量罐（EK）与阻抗系统相互作用，以校正系统无源性的自适应阻抗律。进行了四个移动机械臂紧密协同运输未知物体的仿真，以验证所得到的结果。
