This paper deals with the application of a fuzzy-logic-based controller for a drive with a BLDC motor. Two main aspects of the work are presented in this paper. The first of them is focused on the design process of the fuzzy model. For this purpose, the rules of the applied fuzzy system are defined according to the Lyapunov function. The gain coefficients of the controller are optimized using the Chameleon Swarm Algorithm. Various issues and aspects were analyzed in the simulation tests (robustness against parameter changes, the influence of the controller parameters on the precision of control, stages of optimization, etc.). The presented work confirms the assumptions regarding precision and stable operation (also in the presence of changes in the object parameters—the mechanical time constants of the engine) in connection with the applied fuzzy speed controller. Moreover, the second part of the manuscript presents the low-cost power electronic device developed for laboratory tests. It uses STM32 ARM-based microcontrollers (state variable measurement, control algorithm calculation, control signal generation) and a Raspberry Pi 4B microcomputer (control application—reference value and parameter definition). The experiment performed for the control structure—an electric drive with a BLDC motor—verified the theoretical considerations.

The article is focused on the application of neural models for state-variables estimation. The estimators are applied in the control structure (with the state speed controller) of the electric drive with an elastic shaft. The extended amount of feedback is an additional argument for the estimation of the signal. The calculations are performed for three deep neural structures based on the Convolutional Neural Network (CNN) and the long short-term memory (LSTM). The design stages and the overall concept in this case are completely different than with the applications of classical observers (e.g., the Luenberger, the Kalman filter) often used for similar objects. The direct identification of the mechanical part of the drive is not necessary. The parameters and the equations describing the plant are not used. Instead, the signals are used for training the neural networks. The results (performed for the nominal values of the two-mass system and presenting the robustness of the estimators) present the high precision of the signal estimation. The second part of the work deals with the hardware implementation of the neural estimators in the low-cost programmable device with the ARM core. The experimental transients confirm the features of the neural estimators noticed in the simulations.

External disturbances, uncertainties, and nonlinear behavior are problems that are commonly encountered by control system designers. In order to save on energy and materials, mechanical structures have become lighter and more flexible, which only exacerbates the control problem. To resolve this issue, robust and adaptive control strategies have been proposed and have recently gained a lot of interest in modern scientific literature. This article proposes a combination of both approaches: a sliding mode—radial basis function neural network controller applied to an electrical drive with a sophisticated mechanical structure. The proposed sliding surface provides robustness against parameter uncertainties, while the neural network adjusts itself to the current state of the drive and mitigates the oscillations resulting from the elastic connection with the load machine. This article proves the stability of the proposed control algorithm in the sense of Lyapunov, provides an in-depth numerical analysis, and compares those results with the experimental tests. The algorithm was implemented in a 1103 dSPACE fast-prototyping card and was used to control a 0.5 kW DC motor connected to the load machine by a long (thin) steel shaft.

The paper is focused on improvements of the conventional speed controller based on Active Disturbance Rejection Control (ADRC) applied for a two-mass electric drive system. The described ADRC structure is based on load-side speed measurement. The paper compares conventional structure dynamics with overall system behavior in the case plant parameters are changed. The proposed ADRC algorithm extension performs soft controller parameters adjustment to improve the dynamics and plant response. The presented approach accomplishes adaptation capabilities with the use of a Radial Basis Function Neural Network (RBFNN). The article presents the dynamic response of the plant controlled by the conventional ADRC algorithm and the designed neural adaptation extension. The results are based on the performed experimental tests.

This paper presents the operation of a remotely controlled, wheel-legged robot. The developed Wi-Fi connection framework is established on a popular ARM microcontroller board. The implementation provides a low-cost solution that is in congruence with the newest industrial standards. Additionally, the problem of limb structure and motor speed control is solved. The design process of the mechanical structure is enhanced by a nature-inspired metaheuristic optimization algorithm. An FOC-based BLDC motor speed control strategy is selected to guarantee dynamic operation of the drive. The paper provides both the theoretical considerations and the obtained prototype experimental results.

With the current trends in manipulator design, plentiful examples of machines with flexible links and joints can be instantiated. Lighter construction that allows bending and torsion offers multitude of advantages, such as lower energy consumption and better operation safety when collision is possible in the working environment. However, precise control of applications with such mechanical constructions is very challenging. The state variables might be affected by torsional vibrations, and identification of controller parameters is more difficult, which makes the controller tuning complicated. Therefore, this work focuses on tackling the issues related to speed control of electric drives with sophisticated, and elastic couplings. The robustness against parameter uncertainty is provided through the use of a fuzzy logic system. The speed controller design process incorporates the selection of the rule base, designation of membership functions, as well as controller gain optimization using a nature-inspired technique – the flower pollination algorithm (FPA). Increased damping of torsional vibrations, as well as decreased sensitivity to inertia changes is expected compared to other conventional control solutions, such as PI control. In this study both numerical and experimental studies are conducted.