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

TL;DR

This paper introduces an orchestrated robust control approach for heavy-duty hydraulic manipulators, combining virtual decomposition control with neural networks to achieve high precision and robustness against uncertainties and disturbances.

Contribution

It presents a novel decentralized RBFNN-enhanced VDC method for robust, high-precision control of industrial manipulators, with theoretical guarantees and experimental validation.

Findings

Achieved semi-global boundedness in control performance.

Demonstrated superior robustness and precision over existing controllers.

Validated effectiveness through extensive simulations and real-world experiments.

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.