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Adaptive Task-Space Control for Hydraulic Excavators Based on the High-Order Fully Actuated System Approach.

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Summary

This summary is machine-generated.

This study introduces a new control framework for hydraulic excavators using a high-order fully actuated (HOFA) system. The novel approach enhances precision by addressing uncertainties and integrating sensory feedback for advanced excavator control.

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Area of Science:

Robotics and Control Systems
Mechanical Engineering
Hydraulic Systems

Background:

Hydraulic excavators are complex machines with inherent uncertainties affecting control precision.
Existing control methods often struggle with multisource uncertainties like calibration errors and parameter variations.

Purpose of the Study:

To propose a novel control framework for hydraulic excavators using a high-order fully actuated (HOFA) system approach.
To enhance the precision and robustness of excavator control by addressing multisource uncertainties.

Main Methods:

Developed a comprehensive HOFA model encompassing task, joint, and drive spaces.
Integrated adaptive neural network compensation for multisource uncertainties.
Decoupled kinematic and dynamic loops for simplified controller design.
Incorporated task-space sensory feedback for direct task specification.

Main Results:

The proposed control algorithm effectively compensates for kinematic calibration errors and dynamic/actuator parameter uncertainties.
Decoupling and sensory feedback integration led to simplified analysis and high-precision bucket tip control.
Lyapunov-based analysis confirmed asymptotic convergence of task-space tracking errors.

Conclusions:

The novel HOFA-based control framework offers a robust and effective solution for high-precision hydraulic excavator operation.
The integration of adaptive neural networks and sensory feedback significantly improves control performance in the presence of uncertainties.