Automated Inertia and Friction Estimation for Motion Control Systems
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Solution Overview
Problem
Conventional motion control systems face challenges in accurately estimating inertia and friction coefficients, which are crucial for optimizing motion speed and stability, often requiring cumbersome manual calculations and trial-and-error approaches for tuning controller gain coefficients.
Innovation Solution
An automated system that estimates inertia, viscous friction coefficient, and Coulomb friction coefficient by running the mechanical system through a testing sequence with a continuously varying torque control signal, allowing for the calculation of these parameters based on measured velocity curves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calculations are used to estimate inertia and friction parameters, then the tuning process becomes cumbersome and time-consuming, but the system requires accurate parameter estimates for optimal performance
Solution Approach 1:
The system performs self-diagnosis by automatically measuring inertia and friction parameters through executed motion sequences. The controller autonomously commands the motor to perform acceleration and deceleration sequences, measures the resulting velocity curves, and calculates the parameters without requiring external manual intervention or complex physical measurements.
Solution Approach 2:
The patent replaces manual mechanical measurement methods with an automated computational approach. Instead of physically measuring inertia and friction through complex mechanical procedures, the system uses the motor controller to execute test sequences and compute parameters from the measured velocity responses, substituting mechanical measurement with electrical control and digital calculation.
2Ease of operation
If trial-and-error approaches are used for tuning controller gain coefficients, then the process becomes complex and time-consuming, but accurate parameters are needed to optimize the trade-off between motion speed and system stability
Solution Approach 1:
The controller automatically determines optimal gain coefficients by executing motion sequences and analyzing the measured velocity curves. The system self-adjusts the controller parameters based on the calculated inertia and friction parameters, eliminating the need for operators to perform trial-and-error tuning manually.
Solution Approach 2:
The system uses feedback from the measured velocity curves to automatically adjust controller gain coefficients. By monitoring the motor's actual velocity response during test sequences and comparing it with expected behavior, the controller iteratively optimizes the gain parameters to achieve desired performance characteristics.
3Speed
If high torque is applied to achieve fast position transitions, then motion speed improves, but system stability deteriorates due to overshoot and oscillations
Solution Approach 1:
The system dynamically adjusts controller gain coefficients based on the calculated inertia and friction parameters to optimize the balance between speed and stability. By changing the control parameters according to the actual mechanical characteristics, the system achieves fast response without excessive overshoot or oscillation.
Data Source
AI summary
Systems and methods for estimating an inertia, a Coulomb friction coefficient, and a viscous friction coefficient for a controlled mechanical system are provided. In one or more embodiments, an inertia and friction estimation system can generate a torque command signal that varies continuously over time during a testing sequence. The velocity of a motion system in response to the time-varying torque command signal is measured and recorded during the testing sequence. The estimation system then estimates the inertia and the friction coefficients of the motion system based on the torque command data sent to the motion system and the measured velocity data. In some embodiments, the estimation system estimates the inertia and the friction coefficients based on integrals of the torque command data and the velocity data.