This research presents a method for the simulation of the magneto-mechanical system dynamics taking motion and eddy currents into account. The major contribution of this work leans on the coupling the field-motion problem considering windings as the current forced massive conductors, modelling of the rotor motion composed of two conductive materials and the torque calculation employing the special optimal predictor combined with the modified Maxwell stress tensor method. The 3D model of the device is analysed by the time stepping finite element method. Mechanical motion of the rotor is determined by solving the second order motion equation. Both magnetic and mechanical equations are coupled in the iterative solving process. Presented method is verified by solving the TEAM Workshop Problem 30.

This research presents the novel control strategy of the brushless DC motor. The optimal current driver is designed using Linear Quadratic Regulator and feedback linearization. Additionally, the current reshaping strategy is applied to control the motor torque. Thus, the torque controller is built based on the optimal current driver. The motor is simulated using the FEM analysis.

This research presents a 3D FE method for the simulation of the variable reluctance stepper motor dynamics. The proposed model is used to obtain the optimal minimum energy control law that minimizes the energy injected by the controller. The method is based on the strong coupling of field - circuit equations and extended to eddy current, motion and nonlinearity problem. The linearization technique for the coupled problem is presented. Also the lamination of the motor core is considered. In the paper the open - loop control problem is analyzed. The proposed model is validated by the comparison with measurements. Next, to demonstrate the effectiveness of the proposed optimal minimum energy control method is applied. In both cases, the examination of the variable reluctance stepper motor dynamics and the steel loss in the core is presented and compared.

This research presents a method of modeling and numerical simulation of a reluctance stepper motor using reduced finite-element time-stepping technique. In presented model, the circuit equations are reduced to non-stationary differential equations, i.e. the inductance mapping technique is used to find relationship between coil inductance and rotor position. A strongly coupled field-circuit model of the stepper motor is presented. In analyzed model the magnetostatic field partial differential equations are coupled with rotor motion equation and solved simultaneously in each iterative step. The nonlinearity problem is solved using Newton-Raphson method with spline approximation of the B-H curve.

Identification plays an important role in relation to control objects and processes as it enables the control system to be properly tuned. The identification methods described in this paper use the Stochastic Gradient Descent algorithms, which have so far been successfully presented in machine learning. The article presents the results of the Adam and AMSGrad algorithms for online estimation of the Dielectric Electroactive Polymer actuator (DEAP) parameters. This work also aims to validate the learning by batch methodology, which allows to obtain faster convergence and more reliable parameter estimation. This approach is innovative in the field of identification of control systems. The researchwas supplemented with the analysis of the variable amplitude of the input signal. The dynamics of the DEAP parameter convergence depending on the normalization process was presented. Our research has shown how to effectively identify parameters with the use of innovative optimization methods. The results presented graphically confirm that this approach can be successfully applied in the field of control systems.