LSTM-based Position Observer Design for Permanent Magnet Synchronous Linear Motors

Permanent magnet synchronous linear motors (PM-SLM) are widely used in high-precision manufacturing and high-speed rail transit systems. Accurate acquisition of mover position is essential for reliable motor control. Traditional model-based position observers are constrained by their dependency on precise motor parameters, idealized modeling assumptions, and high design complexity. To overcome these limitations, this paper proposes an intelligent position observer based on the Long Short-Term Memory (LSTM) deep learning model. The observer is trained using measurable current and voltage signals collected from the motor drive system. A data-driven position estimation model is developed by leveraging the LSTM networks capability to capture temporal dependencies in sequential motor signals. Experimental results show that the proposed LSTM-based observer can accurately estimate both the position and velocity of the PMSLM mover. The effectiveness and robustness of the method under various operating conditions demonstrate its feasibility and performance advantages compared with conventional approaches.