In this paper, an adaptive speed controller of the electrical drive is presented. The main part of the control structure is based on the Recurrent Wavelet Neural Network (RWNN). The mechanical part of the plant is considered as an elastic connection of two DC machines. Oscillation damping and robustness against parameter changes are achieved using network parameters updates (online). Moreover, the various combinations of the feedbacks from the state variables are considered. The initial weights of the neural network and the additional gains are tuned using a modified version of the Grey Wolf Optimizer. Convergence of the calculation is forced using a new definition. For theoretical analysis, numerical tests are presented. Then, the RWNN is implemented in a dSPACE card. Finally, the simulation results are verified experimentally.

The paper is focused on issues related to the control of electrical drives with oscillations of state variables. The main problem deals with the construction of the mechanical part, which contains elastic elements used as a coupling between the motor machine and the load. In such cases, strict tracking of the reference trajectory is difficult, so damping of the disturbances is necessary. For this purpose, the full state vector of the object is applied as the feedback signal for the speed controller. This method is efficient and relatively easy to implement (including the hardware part). However, the control accuracy is dependent on the quality of the parameters identification and the invariance of the object. Thus, two adaptive structures are proposed for the two-mass system. Moreover, selected coefficients were optimized using metaheuristic algorithms (symbiotic organism search and flower pollination algorithm). After presentation of the preliminaries and mathematical background, tests were conducted, and the numerical simulations are shown. Finally, the experimental verification for the 0.5 kW DC machines was performed. The results confirm the theoretical concept and the initial assumptions: the state controller leads to the precise control of the drive with a long shaft; recalculation of the parameters can improve the work of the drive under changes of time constants; modern design tools are appropriate for this application.

This paper deals with the implementation of an adaptive speed controller applied for two electrical machines coupled by a long shaft. The two main parts of the study are the synthesis of the neural adaptive controller and hardware implementation using a low-cost system based on an STM Discovery board. The framework between the control system, the power converters, and the motors is established with an ARM device. A radial basis function neural network (RBFNN) is used as an adaptive speed controller. The net coefficients are updated (online mode) to ensure high dynamics of the system and correct work under disturbance. The results contain transients achieved in simulations and experimental tests.

In this paper, the problem of the remote control of electric drives with a complex mechanical structure is discussed. Oscillations of state variables and control precision are the main issues found in such applications. The article proposes a smart, IoT-enabled controller, which allows remote communication with a drive. To solve the problem of speed oscillations and to make the system robust to parameter uncertainty, an adaptive controller with two neural networks is designed. First, numerical tests are conducted in a Matlab/Simulink environment to examine the operation of the proposed control strategy. Afterwards, the obtained results are verified in a laboratory setup equipped with a 0.5 kW electric motor. Remote access is provided by a low-cost, ARM-based ESP32 microcontroller. Usually, virtual instruments used to communicate with remote devices require specific software, which may be expensive and pose compatibility problems. Therefore, the main contribution of the article is the creation of a low-cost, web-based Human-Machine Interface (HMI) with an asynchronous server utility provided by the ESP32 that allows remote control and data acquisition of electric drive state variables.

The purpose of this paper is to show possibility and advantages of initial control plane reproduction for an adaptive fuzzy controller. Usually the fuzzy control is used when the object is not very well known. Yet the truth is, however, that some, at least general information about the object, is available. Usually, in such a case, optimization algorithms are used to tune the control structure. The purpose of this article is to show how to find a starting point that is closer to optimum than a statistically random point, and this way to obtain better results in a shorter time.

Sterowanie napędem elektrycznym oparte o wektor zmiennych stanu umożliwia precyzyjne odtwarzanie sygnału zadanego. Jednak zakłócenia parametryczne lub pomiarowe zdecydowanie utrudniają zachowanie poprawności działania struktury sterowania. W związku z tym, w niniejszej publikacji, zaproponowano rozszerzenie regulatora o część bazującą na logice rozmytej. Zastosowany element wpływa, w zależności od uchybu prędkości, na wartości wzmocnień w sprzężeniach zwrotnych, w efekcie odpowiednio kształtowane są zmienne stanu oraz dynamika napędu. W procesie projektowania zastosowany został optymalizacyjny algorytm metaheurystyczny - Symbiotic Organisms Search (SOS). Obiektem sterowanym jest układ napędowy z elastycznym sprzęgłem, które zostało wprowadzone do części mechanicznej. Wyniki badań symulacyjnych potwierdzają poprawność analizowanej koncepcji. Przeprowadzone zostały również testy eksperymentalne (algorytm został zaimplementowany w karcie dSPACE1103, napęd zawierał dwa silniki prądu stałego o mocy 0,5kW).