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

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.