Vibration Actuator Control via Dual Machine Learning Models
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Solution Overview
Problem
Vibration-type motors exhibit non-linear characteristics and varying controllability due to factors like drive conditions and temperature, making it difficult to adjust PID control gains effectively, especially across different speed regions and environmental changes.
Innovation Solution
A control device utilizing machine learning-based models to output control amounts for a vibration-type actuator, with adaptive learning units updating parameters based on control deviations, allowing for improved control of the actuator's speed and position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If PID control is used for vibration-type actuator, then basic control functionality is achieved, but controllability varies according to drive conditions and temperature environments
Solution Approach 1:
The patent implements dynamic adaptation by continuously updating control parameters based on detected drive conditions and temperature environments. The control device transitions from static PID gains to dynamic parameter adjustment, where control parameters are modified in real-time according to operating conditions, enabling the system to adapt to varying drive conditions and temperature changes.
Solution Approach 2:
The patent employs feedback mechanisms by detecting actual drive conditions and temperature environments, then using this information to adjust control parameters. The control device receives feedback about operating conditions and automatically modifies PID parameters or other control amounts to maintain optimal controllability across different conditions.
2Measurement precision
If multiple control parameters (frequency, phase difference, voltage amplitude) are adjusted, then control precision is improved, but adjustment complexity increases
Solution Approach 1:
The control device performs self-adjustment by automatically modifying control parameters based on detected operating conditions. Instead of requiring manual adjustment of multiple parameters, the system autonomously optimizes frequency, phase difference, and voltage amplitude by detecting drive conditions and temperature, then automatically setting appropriate control amounts without user intervention.
Solution Approach 2:
The patent changes control parameters dynamically based on operating conditions. Rather than fixing parameters or requiring manual adjustment, the system automatically modifies control parameters (frequency, phase difference, voltage amplitude) according to detected drive conditions and temperature, simplifying the adjustment process while maintaining control precision.
3Speed
If PID control gains are adjusted for different speed regions, then speed control accuracy is improved, but control system complexity increases
Solution Approach 1:
The patent implements dynamic control parameter adjustment based on detected speed regions. Instead of using fixed PID gains or requiring manual switching between different gain sets, the control device automatically adapts control parameters according to the current speed region, maintaining accurate speed control across the entire operating range without increasing user-facing complexity.
Solution Approach 2:
The control system uses feedback about the current speed region to automatically adjust control gains. The device detects the operating speed region and uses this information to select or calculate appropriate PID parameters, enabling accurate speed control across different regions without requiring manual intervention or complex user configuration.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances controllability and adaptability of the vibration-type actuator by learning and adjusting control parameters in real-time, improving performance across varying conditions and reducing the complexity of PID control adjustments.
Implementation Method 1
The vibration-type motor applies an alternating-current voltage to an electro-mechanical energy conversion element, such as a piezoelectric element, coupled to an elastic body, to cause the electro-mechanical energy conversion element to generate a high-frequency vibration
Data Source
AI summary
A control device for a vibration-type actuator includes a control unit including first and second output units. The first output unit includes a first learned model subjected to machine learning in such a way as to output a first control amount for causing the contact body to relatively move with respect to the vibrator. The second output unit includes a second learned model subjected to machine learning in such a way as to output a second control amount, which is data of the same data format as that of the first control amount. The control unit updates parameters of the first learned model and parameters of the second learned model based on a control deviation, which is a difference between the first control amount and the second control amount output within the same sampling period as that of the first control amount.