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A Unified Hybrid Control Architecture for Multi-DOF Robotic Manipulators

Xinyu Qiao, Yongyang Xiong, Yu Han, Keyou You

TL;DR

A unified hybrid control architecture that integrates model predictive control with feedback regulation with feedback regulation is proposed, together with a stability analysis of the proposed scheme that mitigates the optimization difficulty associated with high-dimensional nonlinear systems and enhances overall control performance.

Abstract

Multi-degree-of-freedom (DOF) robotic manipulators exhibit strongly nonlinear, high-dimensional, and coupled dynamics, posing significant challenges for controller design. To address these issues, this work proposes a unified hybrid control architecture that integrates model predictive control (MPC) with feedback regulation, together with a stability analysis of the proposed scheme. The proposed approach mitigates the optimization difficulty associated with high-dimensional nonlinear systems and enhances overall control performance. Furthermore, a hardware implementation scheme based on machine learning (ML) is proposed to achieve high computational efficiency while maintaining control accuracy. Finally, simulation and hardware experiments under external disturbances validate the proposed architecture, demonstrating its superior performance, hardware feasibility, and generalization capability for multi-DOF manipulation tasks.

A Unified Hybrid Control Architecture for Multi-DOF Robotic Manipulators

TL;DR

A unified hybrid control architecture that integrates model predictive control with feedback regulation with feedback regulation is proposed, together with a stability analysis of the proposed scheme that mitigates the optimization difficulty associated with high-dimensional nonlinear systems and enhances overall control performance.

Abstract

Multi-degree-of-freedom (DOF) robotic manipulators exhibit strongly nonlinear, high-dimensional, and coupled dynamics, posing significant challenges for controller design. To address these issues, this work proposes a unified hybrid control architecture that integrates model predictive control (MPC) with feedback regulation, together with a stability analysis of the proposed scheme. The proposed approach mitigates the optimization difficulty associated with high-dimensional nonlinear systems and enhances overall control performance. Furthermore, a hardware implementation scheme based on machine learning (ML) is proposed to achieve high computational efficiency while maintaining control accuracy. Finally, simulation and hardware experiments under external disturbances validate the proposed architecture, demonstrating its superior performance, hardware feasibility, and generalization capability for multi-DOF manipulation tasks.
Paper Structure (17 sections, 25 equations, 6 figures, 2 tables, 2 algorithms)

This paper contains 17 sections, 25 equations, 6 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Dynamics Modeling
  3. Dynamic Modeling Framework
  4. Forward Kinematics and Angular Motion Propagation
  5. Backward Recursion
  6. Dynamic Analysis
  7. Design of the Controller
  8. Controller Architecture
  9. Stability Analysis
  10. Machine Learning-Based MPC Torque Emulator
  11. Experiments and Results
  12. Experimental Condition Design
  13. Controller Parameter Design
  14. Simulation and Experimental Validation
  15. Conclusion
  16. ...and 2 more sections

Figures (6)

  • Figure 1: Spatial relationship between adjacent links $L_i$ and $L_{i+1}$ with their corresponding coordinate frames $\{i\}$ and $\{i+1\}$.
  • Figure 2: Block diagram of the proposed unified MPC--feedback control architecture.
  • Figure 3: Experimental platforms of the UR5 manipulator. (a) Simulation environment. (b) Hardware prototype.
  • Figure 4: Training performance under optimal and uniform sampling strategies.
  • Figure 5: Average joint tracking error profiles for different controllers in simulation.
  • ...and 1 more figures