Robust Observer-Based Modular Control Strategy for Electromechanical Linear Actuator-Driven Heavy-Duty Robotic Manipulators

Основные понятия

The paper proposes a robust subsystem-based adaptive (RSBA) control strategy enhanced by an adaptive state observer to effectively address the complexities and uncertainties in electromechanical linear actuator-driven heavy-duty robotic manipulators, ensuring exponential stability and high control performance.

Аннотация

The paper presents a comprehensive approach to modeling and controlling heavy-duty robotic manipulators (HDRMs) equipped with electromechanical linear actuators (EMLAs) driven by permanent magnet synchronous motors (PMSMs).
Key highlights:

Detailed dynamic modeling of EMLA mechanisms, including PMSMs, gearboxes, and screw mechanisms, to capture the system's nonlinearities and complexities.
Trajectory generation for the HDRM joints using direct collocation with B-Spline curves to define a control task.
Design of a robust adaptive state observer to accurately estimate the true linear position and velocity states of EMLAs, compensating for sensor inaccuracies.
Proposal of a robust subsystem-based adaptive (RSBA) control strategy that addresses non-triangular uncertainties and both torque and voltage disturbances, ensuring exponential stability of the entire EMLA-driven HDRM system.
Modular control architecture that enables the design of a single generic equation form applicable to all EMLA-actuated joints, simplifying the control system and facilitating extensions to other complex applications.
Simulation results demonstrating the effectiveness of the proposed control approach in achieving highly accurate and fast tracking of reference trajectories while reducing torque effort, outperforming recent studies.

Статистика

The paper provides the following key data and figures:

Technical specifications of the selected EMLAs (lift, tilt, and telescope) used to actuate the joints of the 3-DoF HDRM (Table II).
Parameters of the motion generation optimization algorithm for the 3-DoF HDRM (Table III).
Forces, velocities, positions, and accelerations in the lift, tilt, and telescope pistons of the 3-DoF HDRM (Figs. 5-6).

Цитаты

"This paper presents a subsystem-based approach, enhanced by a robust state observer to (1) substantially mitigate the impact of uncertainties and disturbances, (2) alleviate the computational burden and complexity of the targeted system, (3) prove mathematical stability, and (4) offer highly accurate and fast tracking performance."
"The proposed approach employs the dynamic motion of the studied electromechanical-linear-actuator-actuated heavy-duty robotic manipulator, decomposing it into distinct subsystems and introducing a unified generic equation control for all subsystems. This modularity feature paves the way for researchers to extend the proposed approach to address other intricate applications."

Ключевые выводы из

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

by Mehdi Heydar... в arxiv.org 04-09-2024

https://arxiv.org/pdf/2311.15843.pdf

Дополнительные вопросы

How can the proposed RSBA control strategy be extended to address other complex robotic systems beyond the EMLA-driven HDRM, such as multi-legged robots or reconfigurable modular manipulators

The proposed RSBA control strategy can be extended to address other complex robotic systems beyond the EMLA-driven HDRM by adapting the modular control framework to suit the specific dynamics and requirements of the new system. For multi-legged robots, the control strategy can be modified to accommodate the unique kinematics and locomotion patterns of such robots. By decomposing the control architecture into subsystems and introducing a unified generic equation control for each subsystem, the RSBA approach can be tailored to handle the complexities of multi-legged robot control. Additionally, for reconfigurable modular manipulators, the modularity feature of the RSBA control can be leveraged to easily adapt the control system to different configurations and tasks. By defining the subsystems based on the manipulator's modules and introducing a flexible control framework, the RSBA strategy can effectively control the reconfigurable manipulator in various operating modes.

What are the potential limitations or challenges in implementing the robust observer-based modular control approach in real-world EMLA-driven HDRM applications, and how could they be addressed

One potential limitation in implementing the robust observer-based modular control approach in real-world EMLA-driven HDRM applications is the computational complexity and real-time processing requirements of the control system. The integration of the adaptive state observer and the robust control methodology may introduce additional computational overhead, which could impact the real-time performance of the system. To address this challenge, optimization techniques such as parallel processing, hardware acceleration, and efficient algorithm design can be employed to streamline the computational tasks and reduce processing time. Additionally, thorough testing and validation of the control system in simulation environments and experimental setups can help identify and mitigate any performance bottlenecks before deployment in real-world applications.

Given the growing importance of energy efficiency in robotic systems, how could the proposed control strategy be further enhanced to optimize the energy consumption of EMLA-driven HDRMs while maintaining high performance

To optimize the energy consumption of EMLA-driven HDRMs while maintaining high performance, the proposed control strategy can be further enhanced by incorporating energy-efficient control algorithms and optimization techniques. One approach is to integrate energy-aware control strategies that prioritize energy-efficient operation without compromising the manipulator's performance. This can involve optimizing the trajectory planning and motion control algorithms to minimize energy consumption during task execution. Additionally, implementing energy recovery systems, such as regenerative braking or energy storage mechanisms, can help capture and reuse energy during deceleration or idle periods, improving overall energy efficiency. By combining advanced control strategies with energy-saving mechanisms, the proposed control strategy can be enhanced to achieve optimal energy consumption in EMLA-driven HDRMs.