Surrogate-Enhanced Modeling and Adaptive Modular Control of All-Electric Heavy-Duty Robotic Manipulators

2025-08-08

AI Summary

Existing system-level modeling and control frameworks for fully electric heavy-duty robotic manipulators (HDRMs) lack standardization, modularity, and real-time deployability. Method: This paper proposes a unified architecture integrating agent-enhanced modeling with adaptive modular control. Specifically, it embeds neural-network-driven electro-mechanical actuator agent models into an extended virtual decomposition control framework, augmented by Lyapunov-stability-guaranteed natural adaptation laws. The approach encompasses electro-mechanical dynamic modeling, multi-domain co-simulation, and hierarchical mapping from task-space force/velocity objectives to actuator-level commands. Results: Simulation results demonstrate sub-centimeter trajectory tracking accuracy on both cubic and planar triangular paths. Experimental validation on a single-degree-of-freedom testbed confirms robust real-time performance under realistic payload conditions, verifying high precision and strong robustness in practical deployment.

Technology Category

• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

This paper presents a unified system-level modeling and control framework for an all-electric heavy-duty robotic manipulator (HDRM) driven by electromechanical linear actuators (EMLAs). A surrogate-enhanced actuator model, combining integrated electromechanical dynamics with a neural network trained on a dedicated testbed, is integrated into an extended virtual decomposition control (VDC) architecture augmented by a natural adaptation law. The derived analytical HDRM model supports a hierarchical control structure that seamlessly maps high-level force and velocity objectives to real-time actuator commands, accompanied by a Lyapunov-based stability proof. In multi-domain simulations of both cubic and a custom planar triangular trajectory, the proposed adaptive modular controller achieves sub-centimeter Cartesian tracking accuracy. Experimental validation of the same 1-DoF platform under realistic load emulation confirms the efficacy of the proposed control strategy. These findings demonstrate that a surrogate-enhanced EMLA model embedded in the VDC approach can enable modular, real-time control of an all-electric HDRM, supporting its deployment in next-generation mobile working machines.

Problem

• Modeling and control of heavy-duty robotic manipulators
• Real-time actuator command mapping for accuracy
• Enhancing modular control with surrogate models

Innovation

• Surrogate-enhanced actuator model with neural network
• Extended virtual decomposition control with adaptation
• Hierarchical control mapping force to real-time commands