A Compensation Model for Optical Encoders at Various Speeds Based on Improved DQN

To address the issue of error compensation for optical encoders in various measurement environments, a compensation model based on an improved deep reinforcement learning (DRL) framework has been designed. This marks the first attempt to introduce DRL into the compensation problem of optical encoders. To achieve this, an improved deep Q-learning network (DQN) has been developed to construct the compensation model. Specifically, the network enhances measurement accuracy by first compensating the input grating images and then cascading with a positioning and decoding network. Experimental results demonstrate that the designed compensation network is suitable for various speed measurement environments, and the measurement accuracy achieved using this compensation network surpasses that of existing compensation methods.