This paper presents a computationally efficient method for modelling an impact of the converter drive on the power grid. The formalized variable structure method (FVSM) allows for comprehensive studies of the effect on the power grid and examining the relation between this effect and the number of drive and feeding line parameters. In order to obtain a comprehensive model along with the model of the power grid, the parameters that are applied originate from a drive of a coal-fired power station. These parameters have been determined based on assessment and estimation. The estimation process was conducted with the aid of a model that allows for the commutation of power electronic elements. The authors confirmed that the model was correct by comparing empirical and theoretical voltage and current waveforms. Harmonic content of the voltage and current in the power grid which feeds the drive are considered to be the measure of the converter drive impact on the power grid. The standard method for the reduction of a harmonic content in the voltage and current involves the application of line reactors and distribution or converter transformers. As an example, the authors determine the impact of the drive on the power grid with respect to the adopted parameters of the line reactor. This example presents FVSM abilities with regard to simulation of complex systems that contain power grid components and converter drives.

This paper focuses on the design and control of an active suspension system, where a tubular linear motor is integrated into a spring damper system of a vehicle. The spring takes up the weight of the vehicle. Therefore the electric linear motor can be designed very compact as it has to provide forces to adjust the damping characteristic only. Design and construction of the active suspension system, a control strategy and validation measurements at a test bench are presented.

The paper presents a method for designing a neural speed controller with use of Reinforcement Learning method. The controlled object is an electric drive with a synchronous motor with permanent magnets, having a complex mechanical structure and changeable parameters. Several research cases of the control system with a neural controller are presented, focusing on the change of object parameters. Also, the influence of the system critic behaviour is researched, where the critic is a function of control error and energy cost. It ensures long term performance stability without the need of switching off the adaptation algorithm. Numerous simulation tests were carried out and confirmed on a real stand.

The paper describes a novel online identification algorithm for a two-mass drive system. The multi-layer extended Kalman Filter (MKF) is proposed in the paper. The proposed estimator has two layers. In the first one, three single extended Kalman filters (EKF) are placed. In the second layer, based on the incoming signals from the first layer, the final states and parameters of the two-mass system are calculated. In the considered drive system, the stiffness coefficient of the elastic shaft and the time constant of the load machine is estimated. To improve the quality of estimated states, an additional system based on II types of fuzzy sets is proposed. The application of fuzzy MKF allows for a shorter identification time, as well as improves the accuracy of estimated parameters. The identified parameters of the two-mass system are used to calculate the coefficients of the implemented control structure. Theoretical considerations are supported by simulations and experimental tests.