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

TL;DR

This paper introduces a surrogate-enhanced modeling and adaptive control framework for all-electric heavy-duty robotic manipulators, demonstrating high accuracy and stability through simulations and experiments.

Contribution

It develops a novel surrogate-enhanced actuator model integrated into a hierarchical control architecture with stability guarantees.

Findings

Achieved sub-centimeter Cartesian tracking accuracy in simulations.

Validated control strategy on a 1-DoF platform under realistic loads.

Demonstrated real-time, modular control of heavy-duty manipulators.

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.