Motor Control External Force Estimation Without Force Sensors
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Solution Overview
Problem
Existing robot control systems face challenges in accurately estimating external forces acting on driven objects without using force sensors, as these systems struggle to differentiate between external forces and internal components like inertia, friction, and weight, which affects the precision of motor control and contact detection.
Innovation Solution
A control system that generates driving force commands, estimates forces based on these commands, and uses a profile generated from past estimations to accurately calculate external forces on driven objects, enabling sensorless external force estimation and improved motor control by accounting for difficult-to-model components like friction and weight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If force sensors are used to measure external forces, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces mechanical force sensors with a computational approach that uses motor current, voltage, and motion data to estimate external forces. The control device calculates external force by comparing actual motor behavior with predicted behavior from dynamic models, eliminating the need for physical force sensing hardware.
Solution Approach 2:
The motor control system uses its own operational data (current, voltage, speed, position) to self-determine external forces acting on the driven object. The system serves its own measurement needs by leveraging data already collected for control purposes, without requiring separate sensing hardware.
2Measurement precision
If disturbance observers are used to estimate external forces, then measurement precision improves, but the ability to differentiate external forces from internal forces (inertia, friction, weight) deteriorates
Solution Approach 1:
The patent segments the total force into distinct components: internal forces (inertia, friction, weight) calculated from dynamic models, and external forces derived as the difference between actual motor output and predicted internal forces. This segmentation allows clear differentiation between force types.
Solution Approach 2:
The patent introduces dynamic models and comparison algorithms as intermediaries between motor commands and external force estimation. These intermediaries process motor operational data and model predictions to isolate external force components from internal force effects.
Data Source
AI summary
A control system may include: a motor configured to power a driven object; and circuitry configured to: generate a first driving force command to drive the motor during a first control; estimate a first force acting on the motor during the first control based, at least in part, on the first driving force command; generate a second driving force command to drive the motor during a second control after the first control; estimate a second force acting on the motor during the second control based, at least in part, on the second driving force command; and estimate an external force acting on the driven object during the second control based, at least in part, on a comparison between the first force and the second force.