The goal of this work is modelling and design of an inverted pendulum prototype. The work presents a mathematical model of a rotary pendulum, modelling of a BLDC motors and also a 3D model of the pendulum prototype is present. The work mentions design of the state space controller and swing up control of the inverted pendulum. Dynamics obtained from the mathematical model is used to create a 3D dynamic model of a pendulum, with the use of the Simscape toolbox. The work deals with control of a BLDC motors with use of vector control. The algorithm is implemented on the CompactRIO platform. Later, hardware is developed, containing STMicroelectronis microcontroller, capable of replacing the CompactRIO platform.

The diploma thesis is focused on comparison of sensored and sensorless control of 3phase BLDC motor. This diploma thesis was compiled with engineers from Honeywell, spol. s r. o. and NXP Semiconductors comapny. Theoretical part of the diploma thesis is describing princip of function BLDC motor and different methods of control. The testing of NXP sensored and sensorless application is done in the practical part. Regarding to the fact, that applications were provided by NXP, furthemore the diploma thesis additionally contains a state machine using sensorless application from NXP. Finally, the author is evaluating a cooperation with NXP company and he thinks about next possible cooperation.

This work is focused on the design of the peristaltic pump drive for medical purposes. Its initial goal is closer introduction of peristaltic pumps and stepper motors. Then the design of BLDC motor will be made and the use of this motor will be compared with stepper motor. Design of the BLDC motor will be made using RMxprt. Last but not least simulations of both types of motors for the peristaltic pump will be created in Simplorer. Finally, the program LabVIEW will be introduced and a program for controlling a stepper motor will be created.

In the context of this master’s thesis are solved algorithms and hardware for control BLDC motor. The first was designed algorithm in LabView called "six-step". This algorithm causes a torque ripple, so in the final version was used vector control. It is a sensor control, so was necessary to design a measurement of rotor position. It was solved using a magnetic position sensor. After testing the control algorithm was designed my own hardware for control the BLDC motor. The main control unit was chosen microprocessor MC56F82743, which is designed specifically for BLDC motor control. The microprocessor was programmed in the CodeWarrior development environment, using a JTAF and USB-TAP.

This thesis deals with the design and implementation of the electronic speed controller for a BLDC motor that will be used in a quadcopter. The first part of the thesis explains the methods of control and sensorless commutation respectively. The following part describes the design of the hardware controller. The control firmware was programmed and debugged for this controller.

This work is concerned with creating the control algorithm in microcontroller dsPIC30F6015 for electric brake with BLDC motor which will dynamically load testing BLDC motors. The work also includes programming user interface for controlling and settings parameters the control unit.

This thesis is focused on the sensorless control of BLDC motor using the Extended Kalman filter. In the first section, process of EKF implementation for estimating rotor speed and electrical angle is described. For this estimation, EKF uses non-linear BLDC motor model and some measurement containing random noise. Second part deals with designing methodology to measure and estimate the quality of BLDC motor sensorless control. Best results were achieved with total current entering power electronics ripple analyzation. In the last section, several BLDC sensorless control algorithms were evaluated.

Hydraulic main stages for off highway machines have usually electromagnetic driven pilot valves. You rarely find stepper motor driven pilot systems that are directly positioning the main spool in the sectional control valve. The presented concept shows the development of an actuator in a unique setup to fulfill the requirements of most off- highway applications. Precise positioning, strength, speed and fail safe requirements were the main goals of the concept. The concept has a two phase BLDC transversal flux motor with a single gear stage transmission. The software and control unit are specially designed for this electric motor setup. On a test bench some results of the first samples reveal the technical potential of this concept. The development of the actuator was done in-house of Thomas Magnete GmbH (mechanical, electronical, and software development).

Climate change pressures fossil-driven vehicles into electrification to sustain a healthy planet and environment for its inhabitants. Air traffic contributes to the ongoing climate crisis, and electric aircraft is taking its toll on the avionics market. The electrification of aircraft is complex and requires specialists and researchers to engage in the subject to haste the process of electric aircraft becoming commercial.  The electrification of aircraft comes with challenges in electromagnetic compatibility (EMC). Part of the aim of this study was to get a broad view of what EMC problems electric aircraft are facing. The leading purpose was to understand electric drive with brushless DC (BLDC) motors and to design and construct an active cancellation filter (ACF) for reducing harmonic noise in electric propulsion systems. A literature study including recent research on EMC issues onboard, outside of, and within the system of electric aircraft deepened the view of the specifications and limits in the electronic design of the airplane. The literature study also covered electric propulsion using BLDC motors and ACF topologies for suppressing harmonics originating from the power inverter in the electric propulsion system.  Measurements on a Cypress motor kit with a BLDC motor showed ringings on the motor phase voltages. The parasitic drain-source capacitances in the MOSFETs of the motor inverter caused the ringings, and damping resistors managed to overcome the ringings in simulations and when measured. However, adding damping resistors line-to-line on the motor phase was determined to be ineffective, but the knowledge of the ringings and how to manage them will become relevant for future work. This study proposed an ACF and a hybrid filter for attenuating harmonics caused by a square wave. The proposed hybrid filter was the ACF filter with an LC filter added to it. Simulations showed that the ACF suppresses harmonics up to 2 MHz, and the hybrid filter had no impact. The measured results showed that the ACF and hybrid filter suppress harmonics up to 10 MHz, and beyond 10 MHz, the filters stop the attenuation of harmonics. The purpose of the hybrid filter was to eliminate harmonics in the higher frequency band, where the performance of the ACF started to lack. However, the hybrid filter showed no influence on the ACF performance, which might have been due to software issues with the Fourier transform.  Although the filters did attenuate harmonics from the voltage pulse source, it could not be stated whether the ACF and hybrid filter topology will perform the same when applied to the drive system of an electric aircraft. Future work will determine how the filters perform on the Cypress motor kit, and eventually, with electric aircraft.

The use of machine learning for predictive maintenance has been the focus of many studies, usually utilizing industrial setups consisting of actual industrial motors. This work examines the possibility of creating a simple setup to develop a machine learning model to detect electric motor failures, eliminating the need to rely on having access to industrial equipment in the early stages. The work conducted in this thesis leverages autoencoders, a specific type of neural network, to detect motor faults based on vibration readings from an accelerometer. The final model detected anomalies with 100% accuracy at three different speeds when a constant load was applied to the motor. However, it should be improved when a variation in load is introduced as it only had 85.1% Accuracy and 90.1% F1-score with 82.0% Recall and 99.8% Precision. In conclusion, the setup and the model developed show promise as an initial setup for testing and experimenting with electric motors and machine learning for predictive maintenance.