Orchestrated Robust Controller for Precision Control of Heavy-duty Hydraulic Manipulators

Vast industrial investment along with increased academic research on heavy-duty hydraulic 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 for generic manipulators with an anthropomorphic arm and spherical wrist. 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 nonlinearities, 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 robust local controllers designed at each local subsystem, then, are 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 the state-of-the-art controller along with provided experimental results, demonstrates that proposed method established all promises and performed excellently.