Abstract
A tunnel magnetic resistance (TMR) sensor is a magnetic detection sensor with low power consumption and high sensitivity. The TMR sensor has promising applications in the position detection of micro permanent magnet linear actuators since the surrounding magnetic field of the micro actuator in this paper will be changed by its movement. Firstly, according to the air-gap magnetic field distribution characteristics of the micro permanent magnet linear actuator and the detection principle of the TMR sensor, the integrated installation parameters of TMR sensors were determined. Then, with the help of TMR technology, the backpropagation neural network (BPNN) algorithm and improved BPNN using particle swarm optimization (PSO) algorithm were used to study the position identification strategy of the slider, and the algorithm strategy with the minimum error was selected. Finally, the experimental results show that the slider position identification strategy based on the PSO-BPNN algorithm can achieve position tracking error within 0.1 mm under the given step position tracking and sinusoidal position tracking, and the movements can be repeated well.
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