Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Mahdi Hejrati, Jouni Mattila

TL;DR

This paper tackles robust, high-precision control of generic 6-DoF heavy-duty hydraulic manipulators by introducing an orchestrated robust controller (ORC) that builds on Virtual Decomposition Control (VDC) and decentralized DRBFNNs. By modeling the system as rigid-body and actuator subsystems and addressing compound input nonlinearities with adaptive deadzone-backlash inverses plus DRBFNN-based uncertainty estimation, the authors establish semi-global uniformly ultimate boundedness (SGUUB) for the overall closed-loop. The approach yields significant improvements in tracking accuracy and disturbance rejection, demonstrated through both simulations and real-world experiments on a 6-DoF HHM with substantial payload. The results suggest strong practical impact for industrial automation, offering a modular controller framework that maintains stability and performance under uncertainties and nonlinearities while remaining computationally feasible on standard hardware.

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.

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

TL;DR

This paper tackles robust, high-precision control of generic 6-DoF heavy-duty hydraulic manipulators by introducing an orchestrated robust controller (ORC) that builds on Virtual Decomposition Control (VDC) and decentralized DRBFNNs. By modeling the system as rigid-body and actuator subsystems and addressing compound input nonlinearities with adaptive deadzone-backlash inverses plus DRBFNN-based uncertainty estimation, the authors establish semi-global uniformly ultimate boundedness (SGUUB) for the overall closed-loop. The approach yields significant improvements in tracking accuracy and disturbance rejection, demonstrated through both simulations and real-world experiments on a 6-DoF HHM with substantial payload. The results suggest strong practical impact for industrial automation, offering a modular controller framework that maintains stability and performance under uncertainties and nonlinearities while remaining computationally feasible on standard hardware.

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.
Paper Structure (38 sections, 5 theorems, 148 equations, 19 figures, 4 tables)

This paper contains 38 sections, 5 theorems, 148 equations, 19 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Heavy-duty Hydraulic Manipulators
  3. State-of-the-Art Review
  4. Model-based controller
  5. Input nonlinearities
  6. Unmodeled uncertainties
  7. VDC scheme
  8. Aim and Contributions
  9. Aim
  10. Contributions
  11. Mathematical preliminaries
  12. Modeling the Rigid body subsystem
  13. Kinematics of the Manipulator
  14. Kinematics of Object 1
  15. Kinematics of Object 2
  16. ...and 23 more sections

Key Result

Lemma 1

lee2018natural. For $\mathcal{L}_{A}$ defined in Definition (Lemma: 2), Bregman divergence with the log-det function can be defined as, with the time derivative of, The Bregman divergence denotes the distance between the actual value $\mathcal{L}_{A}$ and its estimation $\hat{\mathcal{L}}_{A}$ over the manifold $\mathcal{M} \simeq \lbrace \mathcal{L}_{A} \in \mathcal{S}(4): \mathcal{L}_{A} \succ

Figures (19)

  • Figure 1: a) Heavy-duty hydraulic manipulator schematic with kinematic detail, b) Decomposition of the robot into objects: object 1 contains a base joint with hydraulic rack and pinion mechanism, object 2 includes two parallel mechanisms, and object 3 encompasses a spherical hydraulic wrist.
  • Figure 2: VDC frames of object 1
  • Figure 3: a) VDC frames of object 2, b) Closed chain mechanism with hydraulic actuator
  • Figure 4: VDC frames of object 3. RHA is the abbreviation for rotary hydraulic actuator.
  • Figure 5: a) The closed-loop scheme of the original VDC in the presence of compound input nonlinearities and unknown model and actuator uncertainties, b) proposed method with deadzone-backlash and uncertainty compensator
  • ...and 14 more figures

Theorems & Definitions (22)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5
  • Definition 6
  • Remark 1
  • Definition 7
  • Lemma 1
  • Corollary 1
  • ...and 12 more