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A Cognitive PID Neural Controller Design for Mobile Robot Based on Slice Genetic Algorithm
تصميم مسيطر عصبي تناسبي تكاملي تفاضلي مدرك لإنسان آلي متنقل مبني على أساس خوارزمية الشرائح الجينية

Author: Ahmed Sabah Al-Araji
Journal: Engineering and Technology Journal مجلة الهندسة والتكنولوجيا ISSN: 16816900 24120758 Year: 2015 Volume: 33 Issue: 1 Part (A) Engineering Pages: 208-222
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

The main core of this paper is to design a trajectory tracking control algorithm for mobile robot using a cognitive PID neural controller based slice genetic optimization in order to follow a pre-defined a continuous path. Slice Genetic Optimization Algorithm (SGOA) is used to tune the cognitive PID neural controller's parameters in order to find best velocities control actions of the right wheel and left wheel for the mobile robot. Pollywog wavelet activation function is used in the structure of the cognitive PID neural controller. Simulation results and experimental work show the effectiveness of the proposed cognitive PID neural tuning control algorithm; This is demonstrated by the minimized tracking error and the smoothness of the velocity control signal obtained, especially with regards to the external disturbance attenuation problem.

أن المحور الرئيسي لهذا البحث هو تصميم خوارزمية مسيطر تتابع مسار لإنسان آلي متنقل باستخدام مسيطر عصبي تناسبي تكاملي تفاضلي أساسه أمثلية الشرائح الجينية لكي يتبع مسار مستمر معرف مسبقا.لقد تم استخدام خوارزمية الشرائح الجينية لتنغيم عناصر المسيطر العصبي التناسبي التكاملي التفاضلي المدرك لكي يجد أفضل أشارة سرعة للإنسان الآلي المتحرك. أن الدالة الفعالة التي استخدمت في هيكلية المسيطر العصبي التناسبي التكاملي التفاضلي المدرك هي (Pollywog wavelet).من خلال نتائج المحاكاة والأعمال التجريبية, أن فعالية خوارزمية تنغيم المسيطر المقترح تقوم بتقليل الخطأ ألتتابعي لمسار الإنسان الآلي المتحرك مع توليد أشارة سرعة ناعمة, برغم من وجود التأثير الاضطرابي الخارجي.


Article
Cognitive Neural Controller for Mobile Robot

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Abstract

Abstract – This paper proposes a cognitive neural controller to guide a nonholonomic mobile robot during continuous and non-continuous trajectory tracking and to navigate through static obstacles with collision-free and minimum tracking error. The structure of the controller consists of two layers; the first layer is a neural network topology that controls the mobile robot actuators in order to track a desired path based on back-stepping technique and posture identifier. The second layer of the controller is cognitive layer that collects information from the environment and plans the optimal path. In addition to this, it detects if there is any obstacle in the path so it can be avoided by re-planning the trajectory using particle swarm optimization (PSO) technique. The stability and convergence of control system are proved by using the Lyapunov criterion. Simulation results and experimental work show the effectiveness of the proposed cognitive neural control algorithm; this is demonstrated by minimizing tracking error and obtaining the smooth torque control signal, especially when the robot navigates through static obstacles with collision-free and the external disturbances applied.

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