Inverse modeling of untethered electromagnetic actuators using machine learning

Untethered electromagnetic actuation becomes an appealing concept for developing applications in microscale motion control. Although actuator modeling is critical, there is a lack of inverse modeling methods for untethered electromagnetic actuators (EMA) for control design and implementation. Herein, we focused on a machine learning-based framework to obtain inverse models of untethered EMAs. The inverse model is defined as a model which takes a point in the workspace of EMA together with the magnetic field at that point as input and gives the current(s) and position(s) of electromagnets as output. To obtain the inverse model; initially, the Maxwell Equations are solved for the defined set of coil currents and electromagnet positions numerically. Then, the classification problem is defined by concerning the obtained magnetic field values as data and corresponding the input values (currents and positions) as labels. The Random Forest Classifier is trained to obtain an inverse model to match the given magnetic field vector at a position with input values. The proposed approach is employed for three common structures: Single, Double, and Quadruple EMA. The performance test showed that the obtained inverse model is capable of giving the required magnetic field with accuracy of 1.43% Moreover, experimental study shown that the obtained inverse model is also capable of simulating the real-time behavior of EMA systems.