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Proceedings ArticleDOI

Neural Networks Identification and Control of Mobile Robot Using Adaptive Neuro Fuzzy Inference System

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TLDR
This paper developed and investigates the performance of intelligent algorithms in order to stabilize the robot when it is tracking to the reference signal using Adaptive Network Based Fuzzy Inference System ANFIS to improve the system performance.
Abstract
This paper developed and investigates the performance of intelligent algorithms in order to stabilize the robot when it is tracking to the reference signal. One type of robot is a Two Wheeled Balancing Mobile Robot (TWBMR) that requires control for both balancing and maneuvering. Combination artificial intelligence, Neural Networks (NNs) and Fuzzy Logic Control (FLC) have been recognized as the main tools to improve the performance of coupling nonlinear robot system without using any mathematical model. The input-output data of TWBMR generated from closed loop control system is used to develop a neural network model. In this study, neural networks model can be trained offline and then transferred into a process where an adaptive online learning is carried out using Adaptive Network Based Fuzzy Inference System ANFIS to improve the system performance. The simulation results verify that the considered identification and control strategies can achieve favorable control performance. The ANFIS control design approach does not require an accurate model of the plant. In addition, high-level knowledge of the system is not needed to build a set of rules for a fuzzy controller.

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References
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Book

Neural network design

TL;DR: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules, as well as methods for training them and their applications to practical problems.
Journal ArticleDOI

Neuro-fuzzy modeling and control

TL;DR: The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models, which possess certain advantages over neural networks.
Journal ArticleDOI

Neural network-based motion control of an underactuated wheeled inverted pendulum model.

TL;DR: Inspired by human control strategy of inverted pendulum, the tilt angular motion in the passive subsystem Σb has been indirectly controlled using the dynamic coupling with planar forward motion of subsystemΣa, such that the satisfactory tracking of set tilt angle can be guaranteed.
Journal ArticleDOI

Design and Implementation of Integral Sliding-Mode Control on an Underactuated Two-Wheeled Mobile Robot

TL;DR: This paper presents a novel implementation of an integral sliding-mode controller (ISMC) on a two-wheeled mobile robot (2 WMR) and implements a linear nominal controller, which is found adequate in stabilizing the sliding manifold in a range around the equilibrium.
Proceedings ArticleDOI

An Interval Type-2 Fuzzy Logic Toolbox for Control Applications

TL;DR: The development and design of a graphical user interface and a command line programming toolbox for construction, edition and observation of interval type-2 fuzzy inference systems are presented.
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