Effects of Integrated Fuzzy Logic PID Controller on Satellite Antenna Tracking System

Abstract

An electrical device that transforms the electricity into the waves of radio and vice versa is termed the antenna. Its main deployment is in the transmitter and receiver of the antenna. While transmission, the transmitter of radio at the extremities of the antenna furnishes the electricity which oscillates at the frequency of radio wave and energy is released as current as em waves. Some of the voltage is formed from the em wave that is invaded at the point of receiving to amplify the receiver. This study focuses on the analysis of the satellite system to aid in mobile antenna tracking. It also examines the techniques for fuzzy control which make up traditional networks that are used. Initially, a basic idea of tracking loops with stabilized antennas was suggested in light of the requirement for the margin of phase and bandwidth. If the gain of the track is reduced due to changes in attributes and throughput, it will be reduced. In addition, fuzzy regulators and PID constituents are used to enhance the loop. The results indicate that the higher and lower antenna tracking gains within the loop were the best fit and the loop's fluctuations are reduced. A controller based on fuzzy logic can be most efficient due to its simplicity and robustness. It is also discovered that fuzzy logic controllers are evaluated by their behavior in relation. This paper presents an evaluation of the controllers in fuzzy logic, which is based on its integration with conventional controllers. There are three gains in PID's regulator PID and every gain can be used to control the variables of inputs and outcomes. The effects of the responses were analyzed and were compared. The commonality was discovered in the results according to the increase in time for II/6 and II/3 based on PID's regulator PID stability, it can be improved by this system, and there is a reduction in the duration of stability. Furthermore, the period of stability may be reduced through the fusion of PID and fuzzy. The effectiveness of the system could be enhanced by the implementation of the neural network. It is also possible to design the two types of control that could be used to control the proposed solid platform.

Transient Analysis of the Fuzzy Logic-based Speed Control of a Three-phase BLDC Motor

“…The hysteresis control takes care of the limit of the load current within a limited range [7]. The internal process of the fuzzy logic system is shown in Figure 3 [18]. The membership of the error input and its change spread in (-13, 13) and (-5, 5), respectively [19].…”

Implementation of a High Power Quality BLDC Motor Drive Using Bridgeless DC to DC Converter with Fuzzy Logic Controller

“…Duty ratios, ranging from 0 to 0.5, are divided into FLC MFs for the defuzzified output variable, as shown in Figure 7. Table V shows the fuzzy rule base defined for the FLC [23]. The selection of an appropriate output for the converter pulse duty ratio is decided on the error between the reference and the actual DC output voltage of the converter.…”