An improved particle swarm optimization using long short-term memory model for positioning control of a coplanar stage

Abstract

This paper presents XXY stage alignment with an image feedback system consisting of two charge-coupled devices (CCDs) and a proportional–integral–derivative (PID) image servo system tuned by particle swarm optimization (PSO). The initial stop values for the PSO algorithm often cause problems in calculation. A long short-term memory (LSTM) deep learning model can identify long-term dependences and sequential model data. Using LSTM to improve the PSO algorithm for searching best fitness value. LSTM predicts the fitness value of PSO, eliminating the need to preassess fitness, value, and uses the predicted fitness value to adjust the inertia weights of PSO adaptively. This allows the PSO search to be terminated in an early stage and reduces the time required for the search. Proposed method was applied to a visual servo system consisting of two CCD cameras and a personal computer–based PID controller for XXY stage motion. The experimental results indicate that LSTM can reduce the time required for PSO fitness search for controlling XXY stage motion under different conditions successfully. Through the training of the LSTM model, the stage positioning error and time of finding optimal control parameters for a coplanar XXY stage can reduced significantly for in-line inspection processes.