Hydraulic Manipulator Force Soft-Sensing Under High Dynamic Loads
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Solution Overview
Problem
Hydraulic manipulators face challenges in accurately perceiving terminal force due to damage from heavy loads and large inertia, and existing force estimation methods are limited by the need for accurate dynamic parameters and fail in high dynamic conditions.
Innovation Solution
A terminal force soft-sensing method is developed, which establishes a dynamic model of the hydraulic manipulator, uses a minimum inertia parameter set, and generates an excitation trajectory through finite Fourier series to solve for dynamic parameters, allowing for precise force estimation without a force sensor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a force sensor is used to perceive terminal force, then measurement precision is improved, but reliability deteriorates due to sensor damage from heavy loads and large inertia
Solution Approach 1:
The patent creates a virtual copy of the force sensor functionality through software algorithms. Instead of using a physical force sensor that can be damaged, the system uses dynamic model-based calculations to generate a virtual force signal that replicates what a real sensor would measure, thereby eliminating the reliability issue while maintaining measurement precision
Solution Approach 2:
The patent replaces the mechanical force sensor with a computational model. The dynamic model of the hydraulic manipulator, combined with motor torque and state information, substitutes for the physical sensing mechanism, converting a mechanical measurement problem into a computational estimation problem that avoids physical sensor damage
2Device complexity
If direct external force estimation based on inverse kinematics model is used, then device complexity is reduced, but measurement precision deteriorates due to inability to obtain accurate dynamic parameters
Solution Approach 1:
The patent incorporates feedback mechanisms where the estimated force information is continuously refined using actual system responses. The dynamic model predictions are compared with actual motor torques and state measurements, and the estimates are adjusted accordingly, creating a closed-loop system that improves precision without adding complex hardware
Solution Approach 2:
The patent transforms the problem from requiring precise physical parameters to using measurable operational parameters. By changing from a model requiring accurate mass, inertia, and friction parameters to one using measurable motor torques, joint positions, and velocities, the system achieves high precision force estimation without complex parameter identification
3Ease of operation
If Extended State Observer (ESO) is designed for force estimation, then ease of operation is improved, but measurement precision deteriorates in high dynamic operating conditions
Solution Approach 1:
The patent explicitly designs the dynamic model to account for high dynamic conditions. The model incorporates time-varying parameters, acceleration terms, and dynamic coupling effects that are particularly important during rapid movements. This dynamic approach maintains precision in high-speed operations where static or simplified models would fail
Solution Approach 2:
The patent performs preliminary establishment of a comprehensive dynamic model that anticipates all relevant dynamic effects before operation. The model pre-includes inertia, Coriolis, centrifugal, and friction effects, so when high dynamic conditions occur, the system is already prepared to accurately estimate forces without requiring real-time model adaptation or complex tuning
Data Source
AI summary
The embodiments of the present disclosure provide a terminal force soft-sensing method of a hydraulic manipulator. The method includes: establishing a dynamic model of the hydraulic manipulator, performing linearization processing on the dynamic model, and establishing a minimum inertial parameter set of the hydraulic manipulator and a linear model of a regression matrix corresponding to the minimum inertial parameter set; generating an excitation trajectory by solving a finite Fourier series coefficient; determining a hydraulic driving torque by collected values of pressure sensors of two chambers of hydraulic cylinders when controlling the hydraulic manipulator to operate the excitation trajectory under a no-load condition; determining a total regression matrix by bringing joint angles, joint angular velocities, and joint angular accelerations at each moment into the regression matrix; and determining dynamic parameters of the hydraulic manipulator based on the hydraulic driving torque and the regression matrix.


