Azimuth Position Control for Deep Space Antenna using Fuzzy Logic Controller

Yakubu, Halima; Thomas, Sadiq; Hussein, Suleiman U.; Anye, Vitalis C.; Koyunlu, Gokhan; Oshiga, Omotayo

doi:10.1109/icecco48375.2019.9043208

Cited by 3 publications

(4 citation statements)

References 13 publications

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Section: Control System Designmentioning

confidence: 99%

“…As mentioned above, implementing the FPI controller requires several parts: quantization and scaling factors, fuzzification, rule base, inference rules, and defuzzification. These issues are briefly discussed below [21]. membership functions, "N" represents the "Negative" fuzzy set, "Z" represents "Zero," "P" is "Positive," "S" is "Small," "M" is "Medium," and finally "B" is "Big" [22].…”

Section: Fpi Controllermentioning

confidence: 99%

“…Figures 20 and 21 show the actual running track of the antenna. Figures 22 and 23 are error curves of antenna attitude.The nPCtrl curve in Figs [20][21][22][23]. indicates the tracking result without ordinary position feedback control and only the motor speed is controlled.…”

mentioning

confidence: 99%

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Kinematic modeling and motion control of a parallel robotic antenna pedestal

He,

Duan,

et al. 2023

Robotica

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This paper deals with kinematic modeling and motion control of a novel antenna pedestal based on parallel robotic mechanism. Its active joints are two sliders equipped with DC motors on a circular rail. The synchronous and asynchronous motions of the two sliders enable the antenna to rotate in azimuth and pitch. In order to solve the problem of trajectory deviation caused by nonlinear friction and other uncertain disturbances, the structure of the antenna is described at the beginning, and then, the kinematic model is established based on geometry method. Then, the fuzzy PI (FPI) control system is designed based on the position feedback of the driving slider to realize the trajectory tracking of the desired curve. Finally, the parameters of the FPI controller are optimized and validated by Simulink simulation experiments. The tracking performance and the effectiveness of the control algorithm on prototype are verified by experiments of typical trajectory following control.

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Section: Control System Designmentioning

confidence: 99%

Section: Fpi Controllermentioning

confidence: 99%

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Kinematic modeling and motion control of a parallel robotic antenna pedestal

He,

Duan,

et al. 2023

Robotica

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“…Fuzzy logic techniques are also used in the design and analysis of microwave devices [102]. Starting from the design of microwave transistors [103], continuing in the field of microwave circuits [104] and ending with completed microwave devices, such as waveguides [105], antennas [106], power amplifiers [107], power converters [108] and others [109]. The fuzzy logic techniques help not only in the design and analysis of microwave devices, but also in the procedure of the manufacture of microwave devices as it is presented in [110].…”

Section: Fuzzy Logicmentioning

confidence: 99%

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

et al. 2022

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The usage of techniques of the artificial neural networks (ANNs) in the field of microwave devices has recently increased. The advantages of ANNs in comparison with traditional full-wave methods are that the prediction speed when the traditional time-consuming iterative calculations are not required and also the complex mathematical model of the microwave device is no longer needed. Therefore, the design of microwave device could be repeated many times in real time. However, methods of artificial neural networks still lag behind traditional full-wave methods in terms of accuracy. The prediction accuracy depends on the structure of the selected neural network and also on the obtained dataset for the training of the network. Therefore, the paper presents a systematic review of the implementation of ANNs in the field of the design and analysis of microwave devices. The guidelines for the systematic literature review and the systematic mapping research procedure, as well as the Preferred Report Items for Systematic Reviews and Meta-Analysis statements (PRISMA) are used to conduct literature search and report the results. The goal of the paper is to summarize the application areas of usage of ANNs in the field of microwave devices, the type and structure of the used artificial neural networks, the type and size of the dataset, the interpolation and the augmentation of the training dataset, the training algorithm and training errors and also to discuss the future perspectives of the usage of ANNs in the field of microwave devices.

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