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A Novel Design of a Neural Network-Based Fractional PID Controller for Mobile Robots Using Hybridized Fruit Fly and Particle Swarm Optimization
المؤلفون المشاركون
Azar, Ahmad Taher
Ibraheem, Ghusn Abdul Redha
Ibraheem, Ibraheem Kasim
Humaidi, Amjad J.
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-18، 18ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-04-29
دولة النشر
مصر
عدد الصفحات
18
التخصصات الرئيسية
الملخص EN
The design of a swarm optimization-based fractional control for engineering application is an active research topic in the optimization analysis.
This work offers the analysis, design, and simulation of a new neural network- (NN) based nonlinear fractional control structure.
With suitable arrangements of the hidden layer neurons using nonlinear and linear activation functions in the hidden and output layers, respectively, and with appropriate connection weights between different hidden layer neurons, a new class of nonlinear neural fractional-order proportional integral derivative (NNFOPID) controller is proposed and designed.
It is obtained by approximating the fractional derivative and integral actions of the FOPID controller and applied to the motion control of nonholonomic differential drive mobile robot (DDMR).
The proposed NNFOPID controller’s parameters consist of derivative, integral, and proportional gains in addition to fractional integral and fractional derivative orders.
The tuning of these parameters makes the design of such a controller much more difficult than the classical PID one.
To tackle this problem, a new swarm optimization algorithm, namely, MAPSO-EFFO algorithm, has been proposed by hybridization of the modified adaptive particle swarm optimization (MAPSO) and the enhanced fruit fly optimization (EFFO) to tune the parameters of the NNFOPID controller.
Firstly, we developed a modified adaptive particle swarm optimization (MAPSO) algorithm by adding an initial run phase with a massive number of particles.
Secondly, the conventional fruit fly optimization (FFO) algorithm has been modified by increasing the randomness in the initialization values of the algorithm to cover wider searching space and then implementing a variable searching radius during the update phase by starting with a large radius which decreases gradually during the searching phase.
The tuning of the parameters of the proposed NNFOPID controller is carried out by reducing the MS error of 0.000059, whereas the MSE of the nonlinear neural system (NNPID) is equivalent to 0.00079.
The NNFOPID controller also decreased control signals that drive DDMR motors by approximately 45 percent compared to NNPID and thus reduced energy consumption in circular trajectories.
The numerical simulations revealed the excellent performance of the designed NNFOPID controller by comparing its performance with that of nonlinear neural (NNPID) controllers on the trajectory tracking of the DDMR with different trajectories as study cases.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ibraheem, Ghusn Abdul Redha& Azar, Ahmad Taher& Ibraheem, Ibraheem Kasim& Humaidi, Amjad J.. 2020. A Novel Design of a Neural Network-Based Fractional PID Controller for Mobile Robots Using Hybridized Fruit Fly and Particle Swarm Optimization. Complexity،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1141260
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ibraheem, Ghusn Abdul Redha…[et al.]. A Novel Design of a Neural Network-Based Fractional PID Controller for Mobile Robots Using Hybridized Fruit Fly and Particle Swarm Optimization. Complexity No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1141260
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ibraheem, Ghusn Abdul Redha& Azar, Ahmad Taher& Ibraheem, Ibraheem Kasim& Humaidi, Amjad J.. A Novel Design of a Neural Network-Based Fractional PID Controller for Mobile Robots Using Hybridized Fruit Fly and Particle Swarm Optimization. Complexity. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1141260
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1141260
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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