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. 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 basis 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 results with the state-of-the-art controllers along with provided experimental results, demonstrates that proposed method fulfilled all promises and performed excellently. Note to Practitioners—Heavy-duty hydraulic manipulators (HHMs) play a pivotal role in various industrial applications, where their automation is critical for enhancing productivity, precision, and operational efficiency. However, automating HHMs presents significant challenges due to their complex dynamics, highly nonlinear behavior, and presence of unknown real-world uncertainties, such as unknown disturbances, non-ideal devices, unmodeled dynamics, and limited computational resources. These challenges underscore the need for control algorithms that are robust to such uncertainties, computationally efficient for real-time deployment, and capable of achieving high accuracy for reliable execution of free-motion tasks. This study designs a control scheme for generic 6-degree-of-freedom (DoF) HHMs, which ensures robustness and accuracy. Theoretical analysis along with experimental validations on a 6-DoF industrial HHM perfectly display stability of the method, robustness to real-world unknown uncertainties, efficiency in real-time implementation, and excellent accuracy in task execution. These results highlight the scheme’s reliability and universality for industrial HHM applications, making it a significant contribution to advancing automation in this field. Additionally, detailed performance index provided with results enhances replicability and measurability, paving the way for meaningful comparisons in the future.