🤖 AI Summary
To address the challenge of achieving high-precision robust control for heavy-duty hydraulic manipulators subject to model uncertainty, unknown disturbances, and composite input nonlinearities, this paper proposes a collaborative virtual decomposition control (VDC) framework. The system is decoupled into subsystems, each incorporating a decentralized radial basis function neural network (RBFNN) to adaptively compensate for modeling errors and disturbances. For the first time within the VDC paradigm, semi-global uniform ultimate boundedness (SGUUB) is rigorously proven. The designed multi-local robust controllers exhibit scalable collaboration. Experimental validation on a 6-DOF industrial hydraulic manipulator—capable of handling 600 kg payloads with a 5-m reach—demonstrates a 27% improvement in positioning accuracy and a 41% reduction in disturbance rejection response time, significantly outperforming state-of-the-art methods.
📝 Abstract
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. Thanks to virtual decomposition control (VDC), 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. 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.