Power Tool Motor Field Weakening With ML Kickback Detection
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Solution Overview
Problem
Existing power tools rely on hard-coded thresholds that fail to adapt to changing conditions and cannot effectively detect and respond to complex situations like kickback, limiting their operational flexibility and effectiveness.
Innovation Solution
The implementation of a machine learning controller that processes sensor data from power tools to generate adjustable thresholds and control motor operations, using algorithms like artificial neural networks and support vector machines to identify applications and conditions, such as kickback, and adjust parameters like advance and freewheel angles dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hard-coded thresholds are used for motor control, then device complexity is reduced and ease of manufacture is improved, but adaptability to changing conditions and ability to detect complex situations deteriorates
Solution Approach 1:
The patent replaces hard-coded threshold logic with a machine learning-based electronic control system that processes sensor data to dynamically determine motor control parameters. This substitution enables the system to adapt to varying conditions while maintaining manufacturability through integrated electronic control architecture.
Solution Approach 2:
The control system transitions from static hard-coded thresholds to dynamic machine learning models that continuously adapt control parameters based on real-time sensor data analysis. This allows the system to respond flexibly to changing operational conditions while maintaining a fixed physical hardware structure.
2Adaptability or versatility
If machine learning controller is implemented to dynamically adjust control parameters, then adaptability and operational efficiency are improved, but device complexity increases
Solution Approach 1:
The electronic control assembly serves multiple functions: it processes sensor data, runs machine learning inference, generates control waveforms, and monitors motor operation. By consolidating these functions into a single multi-functional controller, the patent manages complexity while achieving high adaptability through software-based control logic.
Solution Approach 2:
The machine learning model acts as an intermediary layer between sensor inputs and motor control outputs. This intermediary processes complex sensor data and translates it into appropriate control parameters, managing the complexity of adapting to various conditions while maintaining a clear control architecture.
3Reliability
If machine learning algorithms process sensor data in real-time, then ability to detect and respond to complex conditions improves, but processing time and computational requirements increase
Solution Approach 1:
The machine learning model is pre-trained offline to learn optimal control strategies from historical data. During real-time operation, the pre-trained model performs rapid inference on incoming sensor data, enabling reliable detection of complex conditions without the computational overhead of real-time training or complex processing.
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.