Displacement sensorless control of electromagnetic linear actuator based on an improved sliding mode observer

A displacement estimation technique based on BP neural networks is suggested to enhance the super-twisting sliding mode observer in order to address the installation challenges, cost rise, and stability reduction of electromagnetic linear actuators induced by the usage of displacement sensors. Combined with an adaptive integral robust control algorithm, the displacement sensorless control of the electromagnetic linear actuator is realized. A non-singular fast terminal sliding mode surface is designed with the continuous hyperbolic tangent function as the switching function in order to reduce the buffeting phenomenon and enhance the displacement estimation performance of the super-twisting sliding mode observer; in terms of the observer’s parameter adjustment, the BP neural network is designed to dynamically adjust the super-twisting sliding mode observer’s gain using the input of the mover speed. The motion control performance test platform of the electromagnetic linear actuator is established, and the displacement estimation and feedback control results are analyzed. The results show that the maximum displacement estimation error of the improved sliding mode observer is reduced by 16.22% under the step condition and 9.10% under the sine condition with the frequency of 2 Hz compared with the super-twisting sliding mode observer; the control performance of displacement sensorless control is equivalent to that of displacement sensor control. The steady state error of the two is 0.03 mm under the 8 mm step condition, and the maximum error is 0.43 mm under the sinusoidal condition with the frequency of 2 Hz. This proves the effectiveness and practicability of the displacement sensorless control of the electromagnetic linear actuator based on an improved super-twisting sliding mode observer.