Antenna azimuth position control with fuzzy logic and self-tuning fuzzy logic controllers

Okumuş, Halil İbrahim; Şahin, Erdinç; Akyazı, Ömür

doi:10.1109/eleco.2013.6713888

Cited by 8 publications

(10 citation statements)

References 2 publications

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“…Here, using the database and the linguistic control elements available in the rule base, the blurred results are acquired. By being sent to the defuzzification unit, these fuzzy interferences acquired are converted into precise numbers and thus the control signal required for the system to be checked becomes having been achieved [14]. The rule table composed of five memberships and twenty-five rules has been shown in Figure 4 and the linguistic labels employed have been expressed as negative big (NB), negative small (NS), zero (Z), positive big (PB) and positive small (PS).…”

Section: Fuzzy Logic Controlmentioning

confidence: 99%

Ga-Based Fuzzy Logic Controller With Booster Type Dc-Dc Converter

Akyazı¹

2018

NWSA

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In this study, a booster-type DC-DC converter is designed by Matlab/Simulink Environment and an output voltage control has been analyzed for different load conditions. The designed DC-DC converter is controlled for different scenarios such as variable input-constant output voltage and constant input-variable output voltage references. The converter controller performance is observed with using classical Proportional+Integral (PI) controller and Fuzzy Logic Controller (FLC). On the other hand, the controller parameters are determined by employing Genetic Algorithm (GA) according to Integral of the Time weighted Absolute Error (ITAE) to improve the controller performance. Thus, more efficient and intelligent controllers are obtained for the DC-DC converter system. The system simulation is realized by the Matlab/Simulink and results are compared with each other.

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Section: Fuzzy Logic Controlmentioning

confidence: 99%

Ga-Based Fuzzy Logic Controller With Booster Type Dc-Dc Converter

Akyazı¹

2018

NWSA

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“…In this case, the knowledge of the human operator would be put in the form of a set of fuzzy linguistic rules [15]. These rules provide to generate multi-level control signal values between from zero to one which increases the controller performance and efficiency [16][17][18]. The fuzzy controller is composed of three main sections.…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

“…In this section, linguistic control rules and inference mechanism are used to generate fuzzfied results. These results are defuzzified in the defuzzification section and crisp control signal is applied to the system to be controlled [18][19][20]. Fuzzification and defuzzification operations are given in Figure 4 [15][16][17][18][19][20].…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

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Pso Based Fuzzy Logi̇c Control Desi̇gn for a Dc/Dc Boost Converter

Akyazı¹

2017

Mugla Journal of Science and Technology

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In this study, a DC / DC boost converter was designed for wind energy systems in Matlab / Simulink and output voltage control was examined under variable load conditions. The designed converter was controlled to obtain desired constant output voltage when the input voltage was variable or to obtain desired variable output voltage when the input voltage was constant. The system performance is compared using classical PI controller and Fuzzy Logic Control (FLC). Also, some system parameters are optimized with PSO to improve system performance using the weighted absolute error (ITAE) criteria as a fitness function. Thus smarter and more efficient control is provided for the proposed system. The simulation study is realized via Matlab/Simulink and the results are evaluated.

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“…The research of [5] and that of [6] is basically using PID and Linear Quadratic Gaussian (LQG) for the antenna azimuth position control system; both researchers were faced with similar shortcomings of degraded performance due to system nonlinearities and delay in reaching setpoint. A much better settling time and less overshoot were achieved by [7] where the Fuzzy logic controller (FLC) and a self-tuning fuzzy logic controller (STFLC) was utilized in the design but chattering phenomena were reported as the setback. All these make the antenna There is need for a high-performance controller; hence the motivation of this paper is to come up with a better solution to the mentioned problems.…”

Section: Introductionmentioning

confidence: 99%

Antenna Azimuth Position Control System using PID Controller & State-Feedback Controller Approach

Uthman

Sudin

2018

IJECE

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This paper analyzed two controllers with the view to improve the overall control of an antenna azimuth position. Frequency ranges were utilized for the PID controller in the system; while Ziegler-Nichols was used to tune the PID parameter gains. A state feedback controller was formulated from the state-space equation and pole-placements were adopted to ensure the model design complied with the specifications to meet transient response. MATLAB Simulink platform was used for the system simulation. The system response for both the two controllers were analyzed and compared to ascertain the best controller with best azimuth positioning for the antenna. It was observed that state-feedback controller provided the best azimuth positioning control with a little settling time, some value of overshoot and no steady-state error is detected. Keyword:Azimuth position control system response MATLAB simulation PID controlle Pole placement State-feedback controller Zeglior-tunung method Copyright © 2018 Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:Aveen Utman, Departement of Electrical and Computer Engineering, Sulaimani Polytechnics University SPU, Sulaimaniyah, Kurdistan Region, Iraq. Email: aveen.hassan@spu.edu.iq INTRODUCTIONThe issue of antenna azimuth position control has become one of the many aspects that have drawn the attention of researchers in the control of antenna placement [1]. These interests are due to the major roles and enhance performances that would be derived from correct azimuth positioning of an antenna. Several techniques were proposed to achieve an optimum control of the azimuth position but such positioning remains a control challenging problem.Hoi et al [2] conducted a research on satellite tracking using fuzzy proportional integral derivative PID control techniques and the conventional PID controller to get the best in performance on satellite tracking. Both the step response in the two techniques presented evidence of chattering phenomena which may lead to poor and weak result and conclusion respectively. Xuan et al [3] introduced a discrete control system using PID controller to achieve a better control on satellite antenna angle deviation. The shortcoming of this work is lack of proper tuning of the PID gains for precise results. Soltana et al [4] considered the case of overseas satellite telecommunication where the control system was applied to an on-board motorized antenna; the separate controller was designed to detect a fault in the satellite control. Though the controller was not robust enough to enable handle the detected fault in the controlled signal. The research of [5] and that of [6] is basically using PID and Linear Quadratic Gaussian (LQG) for the antenna azimuth position control system; both researchers were faced with similar shortcomings of degraded performance due to system nonlinearities and delay in reaching setpoint. A much better settling time and less overshoot were achieved by [7] where the Fuzzy logic controller (FLC) and a self-...

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Antenna azimuth position control with fuzzy logic and self-tuning fuzzy logic controllers

Cited by 8 publications

References 2 publications

Ga-Based Fuzzy Logic Controller With Booster Type Dc-Dc Converter

Ga-Based Fuzzy Logic Controller With Booster Type Dc-Dc Converter

Pso Based Fuzzy Logi̇c Control Desi̇gn for a Dc/Dc Boost Converter

Antenna Azimuth Position Control System using PID Controller & State-Feedback Controller Approach

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