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Robustness-Guaranteed Observer-Based Control Strategy with Modularity for Cleantech EMLA-Driven Heavy-Duty Robotic Manipulator

Mehdi Heydari Shahna, Mohammad Bahari, Jouni Mattila

TL;DR

The paper tackles robust, high-precision control of fully electrified HDRMs actuated by multiple PMSM-powered EMLAs under sensor and modeling uncertainties. It introduces a robust subsystem-based adaptive (RSBA) control framework coupled with an adaptive state observer, organized in a modular two-level architecture, and provides a unified equation form for all subsystems. Through Lyapunov-based analysis, it proves uniformly exponential stability for each joint and the entire $n_a$-DoF HDRM, even with non-triangular uncertainties and time-varying disturbances, and validates the approach with simulations and experiments on a 3-DoF HDRM with a 470 kg payload. The results demonstrate improved tracking accuracy, reduced torque effort, and strong robustness, indicating the method’s practicality for scalable, energy-efficient, clean-tech HDRMs in real-world industrial settings.

Abstract

This paper introduces an innovative observer-based modular control strategy in a class of n_a-degree-of-freedom (DoF) fully electrified heavy-duty robotic manipulators (HDRMs) to (1) guarantee robustness in the presence of uncertainties and disturbances, (2) address the complexities arising from several interacting mechanisms, (3) ensure uniformly exponential stability, and (4) enhance overall control performance. To begin, the dynamic model of HDRM actuation systems, which exploits the synergy between cleantech electromechanical linear actuators (EMLAs) and permanent magnet synchronous motors (PMSMs), is investigated. In addition, the reference trajectories of each joint are computed based on direct collocation with B-spline curves to extract the key kinematic and dynamic quantities of HDRMs. To guarantee robust tracking of the computed trajectories by the actual motion states, a novel control methodology, called robust subsystem-based adaptive (RSBA) control, is enhanced through an adaptive state observer. The RSBA control addresses inaccuracies inherent in motion, including modeling errors, non-triangular uncertainties, and both torque and voltage disturbances, to which the EMLA-driven HDRM is susceptible. Furthermore, this approach is presented in a unified generic equation format for all subsystems to mitigate the complexities of the overall control system. By applying the RSBA architecture, the uniformly exponential stability of the EMLA-driven HDRM is proven based on the Lyapunov stability theory. The proposed RSBA control performance is validated through simulations and experiments of the scrutinized PMSM-powered EMLA-actuated mechanisms.

Robustness-Guaranteed Observer-Based Control Strategy with Modularity for Cleantech EMLA-Driven Heavy-Duty Robotic Manipulator

TL;DR

The paper tackles robust, high-precision control of fully electrified HDRMs actuated by multiple PMSM-powered EMLAs under sensor and modeling uncertainties. It introduces a robust subsystem-based adaptive (RSBA) control framework coupled with an adaptive state observer, organized in a modular two-level architecture, and provides a unified equation form for all subsystems. Through Lyapunov-based analysis, it proves uniformly exponential stability for each joint and the entire -DoF HDRM, even with non-triangular uncertainties and time-varying disturbances, and validates the approach with simulations and experiments on a 3-DoF HDRM with a 470 kg payload. The results demonstrate improved tracking accuracy, reduced torque effort, and strong robustness, indicating the method’s practicality for scalable, energy-efficient, clean-tech HDRMs in real-world industrial settings.

Abstract

This paper introduces an innovative observer-based modular control strategy in a class of n_a-degree-of-freedom (DoF) fully electrified heavy-duty robotic manipulators (HDRMs) to (1) guarantee robustness in the presence of uncertainties and disturbances, (2) address the complexities arising from several interacting mechanisms, (3) ensure uniformly exponential stability, and (4) enhance overall control performance. To begin, the dynamic model of HDRM actuation systems, which exploits the synergy between cleantech electromechanical linear actuators (EMLAs) and permanent magnet synchronous motors (PMSMs), is investigated. In addition, the reference trajectories of each joint are computed based on direct collocation with B-spline curves to extract the key kinematic and dynamic quantities of HDRMs. To guarantee robust tracking of the computed trajectories by the actual motion states, a novel control methodology, called robust subsystem-based adaptive (RSBA) control, is enhanced through an adaptive state observer. The RSBA control addresses inaccuracies inherent in motion, including modeling errors, non-triangular uncertainties, and both torque and voltage disturbances, to which the EMLA-driven HDRM is susceptible. Furthermore, this approach is presented in a unified generic equation format for all subsystems to mitigate the complexities of the overall control system. By applying the RSBA architecture, the uniformly exponential stability of the EMLA-driven HDRM is proven based on the Lyapunov stability theory. The proposed RSBA control performance is validated through simulations and experiments of the scrutinized PMSM-powered EMLA-actuated mechanisms.
Paper Structure (30 sections, 127 equations, 38 figures, 8 tables, 2 algorithms)

This paper contains 30 sections, 127 equations, 38 figures, 8 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background Context
  3. Introduction to the EMLA Mechanism
  4. Literature Review and Control Challenges
  5. Sensor Inaccuracies
  6. Existence of Non-Triangular Uncertainties
  7. Influence of Time-Varying Disturbances
  8. Management of System Interactions
  9. Paper Contributions and Organization
  10. Section \ref{['system_modeling']}
  11. Section \ref{['paper']}
  12. Section \ref{['simulation']}
  13. Section \ref{['experiment']}
  14. System Modeling of EMLA-Equipped HDRM
  15. EMLA Motion Dynamic Modeling
  16. ...and 15 more sections

Figures (38)

  • Figure 1: Illustration of comparative key performance factors influencing EHAs and EMLAs
  • Figure 2: 1-DoF EMLA mechanism and controller schematic
  • Figure 3: Schematic of the RSBA control system for an EMLA-actuated joint of the HDRM
  • Figure 4: Decomposition of each PMSM-powered EMLA-actuated joint of a $n_a$-DoF HDRM into two main subsystems: the motion dynamics of EMLA and energy conversion formulation of PMSM, along with the modularity feature of the RSBA control framework.
  • Figure 5: Convergence of the proposed adaptive observer, signifying the trade-off between noise filtering and real-time responsiveness.
  • ...and 33 more figures