Dual-Rate Feedback Control for Low-Frequency Vibration Suppression

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Solution Overview

Problem

Feedback control devices that do not use machine learning struggle to effectively suppress low-frequency disturbance vibrations due to short sampling times, which limit the input data available for machine learning-based control systems, preventing adequate suppression of these vibrations.

Innovation Solution

A feedback control device that incorporates both a classical PID controller and a machine learning-based neural network, where the machine learning system processes control deviation data sampled at a predetermined period, allowing for a longer time length of input data and improved suppression of low-frequency disturbances without increasing computational load.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If the sampling frequency of the control system is increased to improve responsiveness, then the sampling time is shortened and responsiveness is improved, but the time length of control deviation data input to the neural network becomes shorter, preventing effective suppression of low-frequency disturbance vibration

Engineering Contradiction:

ImproveresponsivenessVSAvoidsuppression of low-frequency disturbance vibration

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The control system is divided into two independent control units: a first control unit that processes control deviation data at high sampling frequency for responsive control, and a second control unit that processes control deviation data at low sampling frequency for suppressing low-frequency disturbance vibration. This segmentation allows each unit to optimize its sampling frequency for its specific function without compromising the other.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A sampling unit acts as an intermediary between the control deviation data acquisition and the second control unit. This sampling unit selectively samples control deviation data at a lower frequency specifically for the machine learning-based control unit, enabling it to process longer time-length data for low-frequency vibration suppression while the main control system maintains high sampling frequency for responsiveness.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If the sampling time is extended to include more low-frequency disturbance vibration in the control deviation data, then machine learning can proceed well and suppress low-frequency disturbance vibration, but the sampling frequency must be reduced which decreases responsiveness

Engineering Contradiction:

Improvesuppression of low-frequency disturbance vibrationVSAvoidresponsiveness

Core Design Contradiction:

ReliabilityVSSpeed

Solution Approach 1:

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts sampling frequency based on the control unit and data requirements. The sampling unit adaptively selects which control deviation data to sample at what frequency, providing high-frequency data to the first control unit for responsiveness and low-frequency data to the second control unit for vibration suppression, thereby optimizing performance across different operational requirements.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS11815861B2Feedback control device that suppresses disturbance vibration using machine learning, article manufacturing method, and feedback control method

Publication Date: 2023.11.14 CANON KK

AI summary

The feedback control device takes information regarding a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object; comprising:a first control unit that takes information regarding the control deviation as input, and outputs a first control amount for the controlled object; a second control unit that takes information regarding the control deviation as input and outputs a second control amount for the controlled object, and in which a parameter for calculating the second control amount is determined by machine learning;an operation unit that operates the controlled object using the first control amount output from the first control unit and the second control amount output from the second control unit; and a sampling unit for thinning out at a predetermined period information regarding the control deviation input to the second control unit.