Comparative Analysis of a Three Phase Inverter with Different Loads

DC–AC inverters play a crucial factor in the modern era to satisfying the demand of energy conversion, which is definitely for a better lifestyle and developing smart facilities. In this paper a deeper study has been taken place to understand the behavior of the inverter base on three phase bridge inverters. The scenarios for the inverters respectively with pure resistive loads R and inductive resistive loads RL are both discussed. The term load is referring to the brushless DC motor (BLDC) which is more reliable than regular or permanent magnet DC motors, this study will help to understand the effect of these components in the inverters. These effects could be related to friction and other electrical problem such as spark which they are associated with the DC components’ operation. Moreover, solving differential equations numerically using powerful software such as MTLAB, which certainly solidify the comprehension of the system operation. The inverter was simulated using MATLAB as a variable DC and AC power supply to in investigate the effect of varying the parameters of the reference input signals such as carrier frequency, duty cycle and the output load such as inductance. Finally, a flowchart of the system is included described both hardware and software.

CONTROL SYSTEM OF ROBOT MANIPULATOR BASED ON DIGITAL CONTROLLER

In the last years, many researches have proposed concerning the motion control and position regulation for manipulators. Motion control has important applications in many areas, for example industrial robotics. In this paper control system based on digital controller for the position control of robotic arm manipulator are build. The proposed control system was simulated on a robot manipulator driven by permanent magnet dc motors. Simulation results show the effectiveness of the control approach.

Fault Diagnosis of Permanent Magnet DC Motors Based on Multi-Segment Feature Extraction

For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. Only using the signal features of current in a single segment is not conducive to fault diagnosis for PMDCMs. In this work, multi-segment feature extraction is presented for improving the effect of fault diagnosis of PMDCMs. Additionally, a support vector machine (SVM), a classification and regression tree (CART), and the k-nearest neighbor algorithm (k-NN) are utilized for the construction of fault diagnosis models. The time domain features extracted from several successive segments of current signals make up a feature vector, which is adopted for fault diagnosis of PMDCMs. Experimental results show that multi-segment features have a better diagnostic effect than single-segment features; the average accuracy of fault diagnosis improves by 19.88%. This paper lays the foundation of fault diagnosis for PMDCMs through multi-segment feature extraction and provides a novel method for feature extraction.

Position - Velocity Control for Two PMDC Motors Connected by a Cross-Coupling Technique with Butterflies Optimization Algorithm

In this paper, two contributions are presented. the first is to design two cascade controllers to control the velocity and position for two Permanent Magnet DC motors (PMDC) working together at the same time for use in many applications such as CNC machines, robotics, and others. Furthermore, the cross-coupling technique is used to connect these motors and adjust the precise synchronization of their movement on the axes. The second contribution is the use of the butterfly’s optimization algorithm (BOA) with the objective function Integral Time Absolute Error (ITAE) to extract the optimal parameter values for the two cascade controllers and the synchronization controller in order to obtain the best accurate results. The simulation results showed high accuracy to reach the desired position at a regular velocity of both the PMDC motors with accurate synchronization and tracking trajectory on the axes. In addition, a very small position deviation of 0.021 rad was observed, and the system returned to a steady-state after 2 seconds of applying the full load.

Power management strategy based sugeno fuzzy logic rules in an electric wheelchair

Power management in multi-power supply electrical systems to manage the general system behavior is essential to improve autonomy and efficiency. In this paper, a proposed fuzzy-logic power management-based sugeno rule is applied in a hybrid PV/battery electric wheelchair to ameliorate the battery life cycle and the overall autonomy. Besides, the increment conductance INC MPPT is used to maximize PVpower. The electric wheelchair's general topology comprises photovoltaic energy resources as the main source and the battery energy storage system device as the auxiliary source. This hybrid power source system supplied the electric wheelchair composed two permanent magnet DC motors controlled by a PI controller. MATLAB/Simulink program is used to implement the overall control scheme. The simulation results that were obtained and the detailed study demonstrate the feasibility and performance of this intelligent strategy.

Analysis of a permanent magnet DC motor explosion-removal robot system based on thermal energy optimization control

Based on the quasi-limit theory?s design principle, the paper analyzes the steady-state and transient temperature rise laws of the motor with different overload currents from the heat transfer perspective of permanent magnet DC motors. The paper takes the EOD robot as an example. Using this law, the current overload, and temperature rise of the robot wheel motor is analyzed. The study found that the continuous-duty motor (S1) selected is conservative and has an extensive design margin.