Power Tool Motor Field Weakening With Machine Learning Control
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Solution Overview
Problem
Existing power tools rely on hard-coded thresholds for motor control, which are inflexible and unable to adapt to changing conditions or complex scenarios like kickback, limiting their effectiveness in various applications.
Innovation Solution
The implementation of a machine learning controller that processes sensor data to generate adjustable field weakening parameters, such as advance and freewheel angles, using algorithms like neural networks and support vector machines, to optimize motor control based on real-time operational data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hard-coded thresholds are used for motor control, then the control system is simple and reliable, but the system cannot adapt to changing conditions or complex scenarios
Solution Approach 1:
The patent implements a machine learning controller that dynamically adjusts motor control parameters based on real-time sensor data and learned patterns. The system transitions from static hard-coded thresholds to dynamic, adaptive control that can respond to changing operational conditions and complex scenarios like kickback events.
Solution Approach 2:
The machine learning controller is trained using operational data from the power tool itself, allowing the system to self-improve and adapt without external intervention. The controller learns from the tool's own performance data to optimize motor control parameters for specific applications and conditions.
2Adaptability or versatility
If machine learning control is implemented, then the system can adapt to different applications and conditions, but the device complexity increases
Solution Approach 1:
The machine learning controller serves multiple functions: it processes sensor data, generates motor control parameters, detects complex scenarios like kickback, and adapts to different applications. This multi-functional approach consolidates what would otherwise require separate control systems into a single intelligent controller.
Solution Approach 2:
The system changes the parameters being controlled from fixed threshold values to dynamically generated parameters based on machine learning models. The controller adjusts motor control parameters such as advance angle and freewheel angle based on learned patterns from training data, enabling adaptation without proportionally increasing hardware complexity.
3Productivity
If field weakening parameters are dynamically adjusted, then motor control efficiency is improved, but the control algorithm complexity increases
Solution Approach 1:
The machine learning controller is pre-trained with operational data to learn optimal field weakening parameters for various conditions. This preliminary training allows the controller to make real-time adjustments without requiring complex calculations during operation, as the decision-making patterns have already been established during the training phase.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring sensor data and adjusting field weakening parameters based on actual motor performance. The controller compares expected versus actual motor behavior and dynamically adjusts parameters to optimize efficiency while maintaining control stability.
Data Source
AI summary
Power tools described herein include a housing, a motor supported by the housing, a battery pack configured to provide electrical power to the power tool, a user input configured to provide an input signal corresponding to a target speed of the motor, a plurality of sensors supported by the housing and configured to generate sensor data indicative of an operational parameter of the power tool, and an electronic controller. The electronic controller includes an electronic processor and a memory. The memory includes a machine learning control program for execution by the electronic processor. The electronic controller is configured to receive the target speed, receive the sensor data, process the sensor data using the machine learning control program, generate, using the machine learning control program, an output based on the sensor data, the output including one or more field weakening parameters, and control the motor based on the generated output.