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

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.

Energy-Cautious Designation of Kinematic Parameters for a Sustainable Parallel-Serial Heavy-Duty Manipulator Driven by Electromechanical Linear Actuator

Electrification, a key strategy in combating climate change, is transforming industries, and off-highway machines (OHM) will be next to transition from combustion engines and hydraulic actuation to sustainable fully electrified machines. Electromechanical linear actuators (EMLAs) offer superior efficiency, safety, and reduced maintenance, and they unlock vast potential for high-performance autonomous operations. However, a key challenge lies in optimizing the kinematic parameters of OHMs' on-board manipulators for EMLA integration to exploit the full capabilities of actuation systems and maximize their performance. This work addresses this challenge by delving into the structural optimization of a prevalent closed kinematic chain configuration commonly employed in OHM manipulators. Our approach aims to retain the manipulator's existing capabilities while reducing its energy expenditure, paving the way for a greener future in industrial automation, one in which sustainable and high-performing robotized OHMs can evolve. The feasibility of our methodology is validated through simulation results obtained on a commercially available parallel-serial heavy-duty manipulator mounted on a battery electric vehicle. The results demonstrate the efficacy of our approach in modifying kinematic parameters to facilitate the replacement of conventional hydraulic actuators with EMLAs, all while minimizing the overall energy consumption of the system.