Robot Joint Impedance Control Without End-Effector Force Sensing
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Solution Overview
Problem
Current impedance control systems for robot manipulators are prone to position tracking errors due to reliance on end-effector torque/force measurements, which can be impractical and unreliable, and are sensitive to model inaccuracies and external disturbances, especially in applications like surgical robotics.
Innovation Solution
A method for controlling mechanical systems with driven joints that measures torques or forces and implements an impedance control algorithm using mass, damper, and spring terms, allowing for target configuration formation and drive signal generation without relying on end-effector torque/force measurements, and operates in joint space to reduce computational complexity and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If impedance control is implemented using end-effector torque/force measurements, then control flexibility and adaptability are improved, but measurement reliability and practical feasibility deteriorate
Solution Approach 1:
The patent introduces joint torque measurements as an intermediary substitute for end-effector torque measurements. By measuring torques at the joints and using transformation relationships, the system achieves impedance control without requiring direct end-effector torque sensors, thus resolving the contradiction between control flexibility and measurement reliability
Solution Approach 2:
The patent replaces the mechanical measurement approach at the end-effector with a computational approach using joint space measurements and transformations. This substitution eliminates the need for complex end-effector torque sensing while maintaining control capabilities through mathematical relationships between joint and end-effector torques
2Measurement precision
If impedance control relies on accurate robot manipulator models, then control precision is improved, but sensitivity to model inaccuracies worsens
Solution Approach 1:
The patent implements feedback mechanisms that continuously measure actual joint torques and positions, then use this information to correct control commands. This feedback loop compensates for model inaccuracies by adapting to actual system behavior, reducing sensitivity to initial model errors while maintaining control precision
Solution Approach 2:
The patent dynamically adjusts control parameters based on measured joint states and torque information. By changing parameters in real-time according to actual system conditions rather than relying solely on fixed model parameters, the system maintains precision while becoming less sensitive to model inaccuracies
3Productivity
If inverse kinematics is coupled with position control loop, then computational efficiency is improved, but control accuracy and reliability deteriorate
Solution Approach 1:
The patent segments the control system into separate functional modules: impedance control module that handles force/torque relationships, inverse kinematics module that handles position calculations, and position control module that handles actuator commands. This segmentation allows each module to operate independently with optimized computational approaches, improving both efficiency and reliability
Solution Approach 2:
The patent extracts inverse kinematics calculations from the inner position control loop and places them in an outer loop. This extraction reduces the computational burden in the time-critical inner loop while maintaining accuracy, as inverse kinematics can be computed less frequently without affecting real-time control performance
4Ease of operation
If impedance control is implemented in Cartesian space, then physical interpretability is improved, but computational complexity and sensitivity to disturbances worsen
Solution Approach 1:
The patent inverts the traditional approach by implementing impedance control directly in joint space rather than transforming everything to Cartesian space. This inversion simplifies computations by working with the natural coordinates of the robot system, reducing computational complexity while maintaining physical interpretability through the direct relationship between joint torques and joint positions
Data Source
AI summary
A method for controlling a mechanical system having a plurality of components interlinked by a plurality of driven joints, the method comprising: measuring torques or forces about or at the driven joints and forming a load signal representing the measured torques or forces; receiving a motion demand signal representing a desired state of the system; implementing an impedance control algorithm in dependence on the motion demand signal and the load signal to form a target signal indicating a target configuration for each of the driven joints; measuring the configuration of each of the driven joints and forming a state signal representing the measured configurations; and forming a set of drive signals for the joints by, for each joint, comparing the target configuration of that joint as indicated by the target signal to the measured configuration of that joint as indicated by the state signal.