G05B2219/41345

MOTOR TRACKING ERROR REDUCTION METHOD AND IMPLEMENTATION DEVICE BASED ON MICRO-DRIVE UNIT
20220060125 · 2022-02-24 ·

The present disclosure relates to the technical field of mechanical precision manufacturing, in particular to a motor tracking error reduction method and an implementation device based on a micro-drive unit. A motor tracking error reduction method based on micro-drive unit includes: providing a motor mover as the working output end, and feeding back the position information of the motor mover to the micro-drive controller in real time by the sensor; controlling the micro-drive unit to compensate the displacement of the motor mover by the micro-drive controller; correcting the tracking error of the motor mover after the displacement compensation, and feeding back the tracking error information after correction to the motor controller. The error reduction method and implementation device in the present disclosure reduce the motor tracking error and solve the problem of coupling interference. In addition, the single position feedback is used to reduce the production cost.

SYSTEM AND METHOD FOR MACHINE-LEARNING-ENABLED MICRO-OBJECT DENSITY DISTRIBUTION CONTROL WITH THE AID OF A DIGITAL COMPUTER
20220187785 · 2022-06-16 ·

System and method that to shape micro-object density distribution (how densely the micro-objects are assembled in particular spatial regions) are provided. A high speed camera tracks existing object density distribution. An array of photo-transistor-controlled electrodes is used to generate a dynamic potential energy landscape for manipulating objects with both DEP and EP forces, and a video projector is used actuate the array. One or more computing devices are used to: process images captured by the camera to estimate existing density distribution of objects; receive a desired density distribution of micro-objects; define a model describing a variation of micro-object density over time due to capacitance-based interactions; generate a sequence of electrode potential that when generated would minimize error between the existing density distribution and a desired density distribution; and use the sequences of electrode potentials to actuate the electrodes.

Motor tracking error reduction method and implementation device based on micro-drive unit

The present disclosure relates to the technical field of mechanical precision manufacturing, in particular to a motor tracking error reduction method and an implementation device based on a micro-drive unit. A motor tracking error reduction method based on micro-drive unit includes: providing a motor mover as the working output end, and feeding back the position information of the motor mover to the micro-drive controller in real time by the sensor; controlling the micro-drive unit to compensate the displacement of the motor mover by the micro-drive controller; correcting the tracking error of the motor mover after the displacement compensation, and feeding back the tracking error information after correction to the motor controller. The error reduction method and implementation device in the present disclosure reduce the motor tracking error and solve the problem of coupling interference. In addition, the single position feedback is used to reduce the production cost.

System and method for machine-learning-enabled micro-object density distribution control with the aid of a digital computer

System and method that to shape micro-object density distribution (how densely the micro-objects are assembled in particular spatial regions) are provided. A high speed camera tracks existing object density distribution. An array of photo-transistor-controlled electrodes is used to generate a dynamic potential energy landscape for manipulating objects with both DEP and EP forces, and a video projector is used actuate the array. One or more computing devices are used to: process images captured by the camera to estimate existing density distribution of objects; receive a desired density distribution of micro-objects; define a model describing a variation of micro-object density over time due to capacitance-based interactions; generate a sequence of electrode potential that when generated would minimize error between the existing density distribution and a desired density distribution; and use the sequences of electrode potentials to actuate the electrodes.