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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.


Article
Design of a Nonlinear PID Neural Trajectory Tracking Controller for Mobile Robot based on Optimization Algorithm
تصميم مسيطر تتابع مسار عصبي لأخطي تناسبي تكاملي تفاضلي لإنسان آلي متنقل مبني على أساس الخوارزمية ألأمثليه

Authors: Khulood E. Dagher --- Ahmed Al-Araji
Journal: Engineering and Technology Journal مجلة الهندسة والتكنولوجيا ISSN: 16816900 24120758 Year: 2014 Volume: 32 Issue: 4 Part (A) Engineering Pages: 973-985
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

This paper presents a trajectory tracking control algorithm for a non-holonomic wheeled mobile robot using optimization technique based nonlinear PID neural controller in order to follow a pre-defined a continuous path. As simple and fast tuning algorithms, particle swarm optimization algorithm is used to tune the nonlinear PID neural controller's parameters to find best velocity control actions for the mobile robot. Simulation results show the effectiveness of the proposed nonlinear PID 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.

يقدم هذا البحث, خوارزمية المسيطر ألتتابعي لمسار عجلة الإنسان الآلي المتحرك باستخدام التقنية ألأمثليه أساسه المسيطر التناسبي التكاملي التفاضلي العصبي اللاخطي لكي يتبع مسار مستمر معرف مسبقا. أن الخوارزمية المستخدمة تتميز بسرعة وببساطة تنغيم عناصر المسيطر اللاخطي التناسبي التكاملي التفاضلي وذلك باستخدام خوارزمية حشد الجسيمات ألأمثليه وإيجاد أفضل أشارة سرعة للإنسان الآلي المتحرك. من خلال نتائج المحاكاة, أن فعالية خوارزمية المسيطر اللاخطي المقترح تقوم بتقليل الخطأ ألتتابعي لمسار الإنسان الآلي المتحرك مع توليد أشارة سرعة ناعمة, برغم من وجود التأثير الاضطرابي الخارجي.

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