Identifying method for lifting and putting down electric self-balancing monocycle
An identification method and unicycle technology, applied in motor vehicles, bicycles, motorcycles, etc., can solve problems such as low user experience and potential safety hazards, and achieve the effects of low computational complexity, improved user comfort, and guaranteed reliability.
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[0028] Example 1: Such as figure 1 — image 3 , The electric self-balancing unicycle 1 mainly achieves balance by controlling the processor, attitude sensor and synchronous servo motor (PMSM). The attitude sensor (with internal acceleration sensor, gyroscope and Kalman filter module) constantly measures the car at a certain frequency Attitude (the actual angle information of the vehicle body), and output the actual angle information to the control processor to compare with the preset given angle to obtain the angle error, and the control processor continuously adjusts the PMSM motor based on the obtained angle error value Rotation direction and speed; in addition, the control system of the electric self-balancing unicycle 1 also includes a Hall sensor, an acceleration sensor and a touch sensor 3. The Hall sensor is built into the PMSM motor and used to measure the speed information of the PMSM motor driving the wheel The acceleration sensor is used to measure the overall acceler...
Example Embodiment
[0042] Example 2: Such as image 3 As an improvement of the present invention, the touch sensor 3 selects any one of a capacitive touch sensor, a pressure sensor and a light sensor. The touch sensor 3 is installed at the pick-up handle 2 of the electric self-balancing unicycle 1, It is used to detect whether the user's hand touches the pick-up handle 2 and the contact time between the hand and the pick-up handle 2. The rest of the structure and advantages are exactly the same as the first embodiment.
Example Embodiment
[0043] Example 3: Such as figure 1 — image 3 As an improvement of the present invention, the order of the speed filtering is median filtering and then sliding average filtering. The median filtering can first remove the burrs in the speed data to ensure the smoothness of the speed value, and then Using moving average filtering to further improve the smoothness of the speed value. The rest of the structure and advantages are exactly the same as the first embodiment.
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