System-Level Efficient Performance of EMLA-Driven Heavy-Duty Manipulators via Bilevel Optimization Framework With a Leader-Follower Scenario

The global push for sustainability and energy efficiency is driving significant advancements across various industries, including the development of electrified solutions for heavy-duty mobile manipulators (HDMMs). Electromechanical linear actuators (EMLAs), powered by permanent magnet synchronous motors, present an all-electric alternative to traditional internal combustion engine (ICE)-powered hydraulic actuators, offering a promising path toward an eco-friendly future for HDMMs. However, the limited operational range of electrified HDMMs, closely tied to battery capacity, highlights the need to fully exploit the potential of EMLAs that drive the manipulators. This goal is contingent upon a deep understanding of the harmonious interplay between EMLA mechanisms and the dynamic behavior of heavy-duty manipulators. To this end, this paper introduces a bilevel multi-objective optimization framework, conceptualizing the EMLA-actuated manipulator of an electrified HDMM as a leader-follower scenario. At the leader level, the optimization algorithm maximizes EMLA efficiency by considering electrical and mechanical constraints, while the follower level optimizes the manipulator’s motion through a trajectory reference generator that adheres to manipulator limits. This optimization approach ensures that the system operates with a synergistic trade-off between the most efficient operating region of the actuation system, achieving a total efficiency of 70.3%. Furthermore, to complement this framework and ensure precise tracking of the generated optimal trajectories, a robust decomposed system control (RDSC) strategy is developed with accurate control and exponential stability. The proposed methodologies are validated on a 3-degrees-of-freedoms (DoFs) manipulator, demonstrating significant efficiency improvements while maintaining high-performance operation. Finally, experiments are conducted on an EMLA test bed under predefined optimal trajectories, simulating dynamic load conditions of the manipulator’s lift joint and controlled with the developed RDSC. The results validate the effectiveness of the optimization framework and the control strategy. Note to Practitioners—The increasing demand for energy efficiency and environmental sustainability is driving the transition in the working machine industry toward electric actuation systems. For heavy-duty mobile manipulators (HDMMs), this means replacing conventional hydraulic and internal combustion engine (ICE)-powered hydraulic actuators with fully-electric electromechanical linear actuators (EMLAs) driven by advanced electric motors such as permanent magnet synchronous motors. This new actuation mechanism represents a clean-tech solution that helps reduce the environmental impact of these working machines. However, one major challenge with EMLA-equipped HDMMs is their limited operating time, constrained by battery capacity. To address this challenge, this paper presents a bilevel optimization framework that focuses on EMLA-driven manipulators, aiming to maximize actuator efficiency while also ensuring that manipulator performance criteria are taken into account. The bilevel optimization aligns the manipulator’s trajectory with the EMLAs’ most efficient operating regions, improving overall actuation system efficiency to 70.3%. This approach ensures lower energy consumption, which could pave the way to extend operational time for the HDMMs. To integrate the control system with the optimization framework for precise trajectory tracking, a robust, adaptive, and modular control design is developed in this paper. The modularity of the control system not only reduces the complexity but also simplifies troubleshooting and upgrades, thereby contributing to lower maintenance costs and facilitating seamless integration into existing industrial setups. Experimental validation on a 1-DoF EMLA test bed confirms the effectiveness of the proposed optimization and control approach.