Dynamic Neural Networks for Robot Systems: Data-Driven and Model-Based Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dynamic Neural Networks for Robot Systems: Data-Driven and Model-Based Applications write by Long Jin. This book was released on 2024-07-24. Dynamic Neural Networks for Robot Systems: Data-Driven and Model-Based Applications available in PDF, EPUB and Kindle. Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.
Neural Networks in Robotics
Neural Networks in Robotics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neural Networks in Robotics write by George A. Bekey. This book was released on 2012-12-06. Neural Networks in Robotics available in PDF, EPUB and Kindle. 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 perceptual inputs smoothly with motor responses, even in the presence of novel stimuli and changes in the environment. The ability of living systems to learn and to adapt provides the standard against which robotic systems are judged. In order to emulate these abilities, a number of investigators have attempted to create robot controllers which are modelled on known processes in the brain and musculo-skeletal system. Several of these models are described in this book. On the other hand, connectionist (artificial neural network) formulations are attractive for the computation of inverse kinematics and dynamics of robots, because they can be trained for this purpose without explicit programming. Some of the computational advantages and problems of this approach are also presented. For any serious student of robotics, Neural Networks in Robotics provides an indispensable reference to the work of major researchers in the field. Similarly, since robotics is an outstanding application area for artificial neural networks, Neural Networks in Robotics is equally important to workers in connectionism and to students for sensormonitor control in living systems.
Data-Driven Science and Engineering
Data-Driven Science and Engineering - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data-Driven Science and Engineering write by Steven L. Brunton. This book was released on 2022-05-05. Data-Driven Science and Engineering available in PDF, EPUB and Kindle. A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Competition-Based Neural Networks with Robotic Applications
Competition-Based Neural Networks with Robotic Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Competition-Based Neural Networks with Robotic Applications write by Shuai Li. This book was released on 2017-05-30. Competition-Based Neural Networks with Robotic Applications available in PDF, EPUB and Kindle. Focused on solving competition-based problems, this book designs, proposes, develops, analyzes and simulates various neural network models depicted in centralized and distributed manners. Specifically, it defines four different classes of centralized models for investigating the resultant competition in a group of multiple agents. With regard to distributed competition with limited communication among agents, the book presents the first distributed WTA (Winners Take All) protocol, which it subsequently extends to the distributed coordination control of multiple robots. Illustrations, tables, and various simulative examples, as well as a healthy mix of plain and professional language, are used to explain the concepts and complex principles involved. Thus, the book provides readers in neurocomputing and robotics with a deeper understanding of the neural network approach to competition-based problem-solving, offers them an accessible introduction to modeling technology and the distributed coordination control of redundant robots, and equips them to use these technologies and approaches to solve concrete scientific and engineering problems.
Modeling Dynamic Systems for Multi-step Prediction with Recurrent Neural Networks
Modeling Dynamic Systems for Multi-step Prediction with Recurrent Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Modeling Dynamic Systems for Multi-step Prediction with Recurrent Neural Networks write by Nima Mohajerin. This book was released on 2017. Modeling Dynamic Systems for Multi-step Prediction with Recurrent Neural Networks available in PDF, EPUB and Kindle. This thesis investigates the applicability of Recurrent Neural Networks (RNNs) and Deep Learning methods for multi-step prediction of robotic systems. The unmodeled dynamics and simplifying assumptions in classic modeling methods result in models that yield rapidly diverging predictions when the model is used in an iterative fashion, i.e., for multi-step prediction. However, the effect of the unmodeled dynamics can be captured by collecting datasets of the system. Deep Learning provides a strong set of tools to extract patterns from data, however, large datasets are commonly required for the methods to work well. Collecting a large amount of data from a robotic system can be a cumbersome and expensive approach. In this work, Deep Learning methods, particularly RNNs, are studied and employed for the purpose of learning models of two aerial vehicles from experimental data. The feasibility of employing RNNs is first studied to learn a model of a quadrotor based on a simulated dataset, which yields a Multi Layer Fully Connected (MLFC) architecture. Models can be learned for multi-step prediction, recovering excellent predictions over 500 timesteps in the presence of simulated disturbances to the robot and noise on the measurements. To learn models from experimental data, the RNN state initialization problem is defined and formulated. It is shown that the RNN state initialization problem can be addressed by creating and training an initialization network jointly with the multi-step prediction network, and the combination can be used in a black-box modeling approach such that the model produces predictions which are immediately accurate. The RNN based black-box methods are trained on an experimental dataset gathered from a quadrotor and a publicly available helicopter dataset. The quadrotor dataset, which encompasses approximately 4 hours of flight data in various regimes, has been released and is now available publicly online. Finally, a hybrid network, which combines the proposed RNN based black-box models with a physics based quadrotor model into a single RNN-based modeling system is introduced. The proposed hybrid network solves many of the limitations of the existing state of the art in long-term prediction for robotics systems. Trained on the quadrotor dataset, the hybrid model provides accurate body angular rate and velocity predictions of the vehicle over almost 2 seconds which is suitable to be used in a variety of model-based controller applications.
