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2 edition of Adaptive robot control using artificial neural networks found in the catalog.

Adaptive robot control using artificial neural networks

A. M. S. Zalzala

Adaptive robot control using artificial neural networks

an application in the theory of cognition

by A. M. S. Zalzala

  • 32 Want to read
  • 22 Currently reading

Published by University of Sheffield, Dept. of Control Engineering in Sheffield .
Written in English


Edition Notes

Statementby Ali M.S. Zalzala and Alan S. Morris.
SeriesResearch report / University of Sheffield. Department of Control Engineering -- no.374, Research report(University of Sheffield. Department of Control Engineering) -- no.374.
ContributionsMorris, A. S.
ID Numbers
Open LibraryOL13964062M

Proceedings of the 19th World Congress The International Federation of Automatic Control Cape Town, South Africa. August , Control of Uncertain Teleoperators with Time-Delays using Artificial Neural Networks Cheasare Miranda, Alberto De-la-Mora, Emmanuel Nu~ o n Electronics :// Sliding mode control for trajectory tracking of a non- holonomic mobile robot using adaptive neural networks, Control Engineering and Applied Informatics, 16, 12–21, Sabahi, F. (). Introducing validity into self-organizing fuzzy neural network applied to impedance force control, Fuzzy Sets and Systems, , –,

Tzafestas S and Rigatos G () Neural and Neurofuzzy FELA Adaptive Robot Control Using Feedforward and Counterpropagation Networks, Journal of Intelligent and Robotic Systems, , (), Online publication date: 1-Oct The controller has been designed by an adaptive neural network (NN) based on the feedback system. Son et al. [16] proposed a novel control system combining adaptively feed-forward neural controller and PID controller to control the joint-angle position of the SCARA parallel robot using the pneumatic artificial muscle (PAM) ://

  The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural ://   Adaptive kinetic structural behavior through machine learning: Optimizing the process of kinematic transformation using artificial neural networks - Volume 29 Issue 4 - Odysseas Kontovourkis, Marios C. Phocas, Ifigenia Lamprou


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Adaptive robot control using artificial neural networks by A. M. S. Zalzala Download PDF EPUB FB2

Robot Control Using Neural Networks With Adaptive Learning Steps Conference Paper (PDF Available) in Proceedings of SPIE - The International Society for Optical Engineering November with Tzafestas S.G. () Neural Networks in Robot Control. In: Tzafestas S.G., Verbruggen H.B.

(eds) Artificial Intelligence in Industrial Decision Making, Control and Automation. Microprocessor-Based and Intelligent Systems Engineering, vol An approach using artificial neural networks (ANN) to solve the ASV anti-collision problem is presented in [19] [20][21], where ANN was used to control the autonomous robot.

3D mobile (3D LiDAR Neural networks in the kinematic control results in a more robust and versatile movement, with the robot being able to apply corrective measures for variations in working conditions, such as joint An adaptive robust control system is considered for dual-arm manipulators (DAM) using the combination of second-order sliding mode control (SOSMC) and neural :// Revel A and Gaussier P Designing neural control architectures for an autonomous robot using vision to solve complex learning tasks Biologically inspired robot behavior engineering, () Leray P New advances in neuro -visual simulation and symbolic extraction for real world computing, 3D image analysis and 3D object digitization Proceedings Neural networks, which feature high-speed parallel distributed processing, and can be readily implemented by hardware, have been recognized as a powerful tool for real-time processing and successfully applied widely in various control systems.

Particularly, using neural networks for the control of robot manipulators have attracted much Neural Systems for Robotics represents the most up-to-date developments in the rapidly growing aplication area of neural networks, which is one of the hottest application areas for neural networks technology.

The book not only contains a comprehensive study of neurocontrollers in complex Robotics systems, written by highly respected researchers   @article{osti_, title = {Adaptive pattern recognition and neural networks}, author = {Pao, Yohhan}, abstractNote = {The application of neural-network computers to pattern-recognition tasks is discussed in an introduction for advanced students.

Chapters are devoted to the nature of the pattern-recognition task, the Bayesian approach to the estimation of class membership, the fuzzy-set Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created.

The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living organisms to integrate Mai et al. adopted another adaptive control strategy using the fuzzy wavelet neural networks for robot manipulators trajectory tracking [28].

Among these strategies, the adaptive fuzzy strategies Torras C. () "Neural Learning for Robot Control" Proceedings European Conference on Artificial Intelligence A. Cohn ed. John Wiley and sons Ltd. Tzirkel-Hancock E., Fallside F.,() "Stable Control of Nonlinear Systems using Neural Networks", International Journal of Robust and Nonlinear Control, John Wiley Vol.

2, Venaille C The most concurrent advances in the area of artificial neural networks (ANNs) have provided the potential for dealing with such a challenging task. “Gaussian Networks for Direct Adaptive Control,” IEEE Trans. Neural Networks, Vol.

3,No. 6, pp Intelligent Control Using Dynamic Neural Networks with Robotic Applications. In: Fuzzy   In the book emphasis is put on development of sound theory of neural adaptive control for nonlinear control systems, but firmly anchored in the engineering context of industrial practice.

Therefore the contributors are both renowned academics and practitioners from major industrial users of   This chapter will present detailed procedures for using adaptive networks to solve certain common problems in adaptive control and system identification.

The bulk of the chapter will give examples using artificial neural networks (ANNs), but the mathematics are general. In Reinforcement learning agents are adaptive, reactive, and self-supervised. making reinforcement learning more practical for realistic robot tasks: (1) Reinforcement learning can be naturally integrated with artificial neural networks to obtain high-quality generalization, resulting in a Robot Manipulator Control Using Neural Networks.

Authors; Authors and affiliations; Duc Truong Pham M.K. () Artificial neural network based control of nonlinear systems with application to robotic manipulators, PhD thesis J.J. and Li, W.

() On the adaptive control of robot manipulators, Int. of Robotics Research, 6(3), 49 An adaptive control for robot manipulators based on multiple incremental fuzzy neural networks (FNNs) is proposed in this paper.

The overall controller is comprised of a feedback controller and multiple FNNs which learn inverse dynamics of the robot manipulator for different tasks. The multiple FNNs are switched or blended to improve the transient response when manipulating objects are :// This book should be of interest to everyone involved in the design of walking robots, to engineers and scientists interested in the application of neural networks and optimal control to electromechanical systems, and even to professors teaching philosophy of science who /Adaptive-Neural-Control-of-Walking-Robots.

Hendzel Z Adaptive critic neural networks for identification of wheeled mobile robot Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing, () Song S, Song Z, Chen X and Duan G Adaptive wavelet neural network friction compensation of mechanical systems Proceedings of the Third international.

An adaptive dynamic balance scheme was implemented and tested on an experimental biped. The control scheme used pre-planned but adaptive motion sequences. CMAC neural networks were responsible for the adaptive control of side-to-side and front-to-back balance, as well as for maintaining good foot contact.

Qualitative and quantitative test results show that the biped performance improved   IEEE Transactions on Neural Networks ; 13(1): – [3] Efe MO, Kaynak O. Stabilizing and robustifying the learning mechanisms of artificial neural networks in control engineering applications.

International Journal of Intelligent Systems ; 15(5): – [4] Ge SS, Hang CC, Lee TH, Zhang T. Stable Adaptive Neural Network Locomotion control Motor control Artificial neural networks Sensory-motor coordination Humanoid robotics Adaptive behavior Central pattern generator Bio-inspired robots This is a preview of subscription content, log in to check ://