Multi-axis motor position compensation in ophthalmic surgical laser system using deep learning

A motor position compensation method implemented in a system comprising a plurality of motors each configured to drive a moving component of the system, each motor having a corresponding encoder configured to measure actual motor positions of the motor, the system further comprising a controller operatively coupled to the plurality of motors to command the motors and receive the actual motor positions, the method comprising:

• providing a recurrent neural network (RNN) model implemented in the controller;
• generating training data by transmitting a first plurality of motor commands to the plurality of motors to command the plurality of motors to perform a plurality of action sequences while receiving actual motor positions of the plurality of motors from the plurality of encoders, each of the first plurality of motor commands including a commanded motor position, wherein the training data includes a plurality of data points each being a pair of a commanded motor position contained in one of the first plurality of motor commands and a corresponding actual motor position;
• training the RNN model using the training data to produce a trained RNN model;
• calculating a plurality of original commanded motor positions for each motor based on an intended action sequence;
• inputting the original commanded motor positions to the trained RNN model to produce corresponding predicted actual motor positions;
• based on differences between the predicted actual motor positions and the corresponding original commanded motor positions, adjusting the plurality of original commanded motor positions to obtain a corresponding plurality of adjusted commanded motor positions;
• and
• transmitting a second plurality of motor commands to the plurality of motors to command the plurality of motors, the second plurality of motor commands including the plurality of adjusted commanded motor positions.