# US 12,005,540 B2 · Power tool including a machine learning block for controlling field weakening of a permanent magnet motor > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,005,540 B2 - Document ID: US12005540B2 - Status: Granted - Kind: B2 - Application number: 17/732,192 - Filed: 2022-04-28 - Published: 2022-11-03 - Granted: 2024-06-11 - Earliest claimed: 2021-04-28 - Assignees: Milwaukee Electric Tool Corporation - Applicants: Milwaukee Electric Tool Corporation - Inventors: Jonathan E. Abbott; Alexander T. Huber - Web page: https://patentgrep.com/patent/US12005540B2 ## CPC - B23Q 15/12: Adaptive control, i.e. adjusting itself to have a performance which is optimum according to a preassigned criterion - G05B 13/0265: {the criterion being a learning criterion} - B23Q: DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING (tools of the kind used in lathes or boring machines B23B27/00); MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) ## Abstract 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. ## Claims ### Claim 1 A power tool comprising: a housing; a motor supported by the housing; a battery pack supported by the housing and 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; an electronic controller, the electronic controller including an electronic processor and a memory, the memory including a machine learning control program for execution by the electronic processor, the electronic controller 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, the one or more field weakening parameters including a conduction angle value that modifies a conduction angle of the motor, and control the motor based on the generated output to achieve the target speed. ### Claim 2 (depends on 1) The power tool of claim 1, wherein the machine learning control program is generated on an external system device through training based on example sensor data and associated outputs, and is received by the power tool from the external system device. ### Claim 3 (depends on 1) The power tool of claim 1, wherein the machine learning control program is one of a static machine learning control program and a trainable machine learning control program. ### Claim 4 (depends on 1) The power tool of claim 1, wherein the sensor data includes one or more of a motor current, a battery pack impedance, a battery pack voltage, and a motion of the power tool. ### Claim 5 (depends on 1) The power tool of claim 1, wherein the one or more field weakening parameters also include one or more of an advance angle value, and a freewheel angle value, the freewheel angle value corresponding to when a motor winding of the motor is disconnected from an excitation voltage. ### Claim 6 (depends on 1) The power tool of claim 1, wherein the electronic controller is configured to filter the field weakening parameters using one or more filters. ### Claim 7 (depends on 6) The power tool of claim 6, wherein the one or more filters include one or more of a slew rate filter, a low pass filter, and a hysteresis filter. ### Claim 8 (depends on 1) The power tool of claim 1, wherein the electronic controller is further configured to receive one or more priority parameter values, wherein the machine learning control program generates the output based on the sensor data and the priority parameter values. ### Claim 9 A method of operating a power tool to control field weakening, the method comprising: generating, by a sensor of the power tool, sensor data indicative of an operational parameter of the power tool; receiving, by an electronic controller of the power tool, the sensor data, the controller including an electronic processor and a memory, wherein the memory includes a machine learning control program for execution by the electronic processor; processing the sensor data using a machine learning control program of the machine learning controller; generating, using the machine learning control program, an output based on the sensor data, wherein the output includes one or more field weakening parameters, the one or more field weakening parameters including a conduction angle value that modifies a conduction angle of the motor; and controlling, by the electronic controller, a motor of the power tool based on the output to achieve the target speed. ### Claim 10 (depends on 9) The method of claim 9, wherein the machine learning control program is generated on an external system device based on example sensor data and associated outputs, and is received by the power tool from the external system device. ### Claim 11 (depends on 10) The method of claim 10, wherein the machine learning control program is one of a static machine learning control program and a trained machine learning control program. ### Claim 12 (depends on 9) The method of claim 9, wherein the sensor data includes one or more of a motor current, a battery pack impedance, a battery pack voltage, and a motion of the power tool. ### Claim 13 (depends on 9) The method of claim 9, wherein the one or more field weakening parameters also include one or more of an advance angle value, and a freewheel angle value, the freewheel angle value corresponding to when a motor winding of the motor is disconnected from an excitation voltage. ### Claim 14 (depends on 9) The method of claim 9, further comprising filtering the one or more field weakening parameters using one or more filters. ### Claim 15 (depends on 14) The method of claim 14, wherein the one or more filters include one or more of a slew rate filter, a low pass filter, and a hysteresis filter. ### Claim 16 (depends on 9) The method of claim 9, further comprising: receiving, by the electronic controller, one or more priority parameter values; and generating, by the machine learning control program, the output based on the sensor data and the priority parameter values. ### Claim 17 A power tool comprising: a housing; a motor supported by the housing; a battery pack supported by the housing and 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; an electronic controller, the electronic controller including an electronic processor and a memory, the memory including a machine learning control program for execution by the electronic processor, the electronic controller 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, one or more priority parameters based on the received target speed and the received sensor data, generate an output based on the one or more priority parameters and the received sensor data, the output including one or more field weakening parameters, and control the motor based on the generated output. ### Claim 18 (depends on 17) The power tool of claim 17, wherein the one or more priority parameters include one or more of a speed control parameter, a maximum speed parameter, a maximum power parameter, and a maximum efficiency parameter. ### Claim 19 (depends on 17) The power tool of claim 17, wherein the one or more field weakening parameters include one or more of an advance angle, a conduction angle, and a freewheel angle. ### Claim 20 (depends on 17) The power tool of claim 17, wherein the electronic controller is configured to filter the field weakening parameters using one or more filters, wherein the one or more filters include one or more of a slew rate filter, a low pass filter, and a hysteresis filter.