Abstract
Vast industrial investment along with increased academic research on hydraulic heavy-duty manipulators has unavoidably paved the way for their automatization, necessitating the design of robust and high-precision controllers. In this study, an orchestrated robust controller is designed to address the mentioned issue. To do so, the entire robotic system is decomposed into subsystems, and a robust controller is designed at each local subsystem by considering unknown model uncertainties, unknown disturbances, and compound input constraints, thanks to virtual decomposition control (VDC). As such, radial basic function neural networks (RBFNNs) are incorporated into VDC to tackle unknown disturbances and uncertainties, resulting in novel decentralized RBFNNs. All these robust local controllers designed at each local subsystem are, then, orchestrated to accomplish high-precision control. In the end, for the first time in the context of VDC, a semi-globally uniformly ultimate boundedness is achieved under the designed controller. The validity of the theoretical results is verified by performing extensive simulations and experiments on a 6-degrees-of-freedom industrial manipulator with a nominal lifting capacity of $600\, kg$ at $5$ meters reach. Comparing the simulation result to state-of-the-art controller along with provided experimental results, demonstrates that the proposed method established all the promises and performed excellently.
Abstract (translated)
巨大的工业投资和液压重型操纵器增加的学术研究不可避免地为它们的自动化开辟了道路，迫使设计具有稳健和高精度的控制器。在这项研究中，设计了一个有组织的稳健控制器来解决上述问题。为了做到这一点，整个机器人系统被分解为子系统，然后通过虚拟分解控制（VDC）在每个局部子系统中设计一个稳健的控制器，考虑未知模型不确定性、未知干扰和复合输入约束。因此，径向基本功能神经网络（RBFNNs）被引入到VDC中解决未知干扰和不确定性，从而实现新颖的分布式RBFNN。所有在局部子系统设计的稳健控制器都被编排成实现高精度控制。最后，在VDC的背景下，实现了半全局均匀终极有界性。通过在具有名义提升能力为600公斤的6度自由度工业吊车上的广泛仿真和实验来验证理论结果，证明了所提出的方法取得了巨大的成功。
URL
https://arxiv.org/abs/2312.06304
