Название: Robot Control and Calibration: Innovative Control Schemes and Calibration Algorithms
Автор: Xin Luo, Zhibin Li, Long Jin, Shuai Li
Издательство: Springer
Серия: SpringerBriefs in Computer Science
Год: 2023
Страниц: 132
Язык: английский
Формат: pdf (true), epub
Размер: 29.1 MB

This book mainly shows readers how to calibrate and control robots. In this regard, it proposes three control schemes: an error-summation enhanced Newton algorithm for model predictive control; recurrent neural network (RNN) for solving perturbed time-varying underdetermined linear systems; and a new joint-drift-free scheme aided with projected ZNN, which can effectively improve robot control accuracy. Moreover, the book develops four advanced algorithms for robot calibration – Levenberg-Marquarelt with diversified regularizations; improved covariance matrix adaptive evolution strategy; quadratic interpolated beetle antennae search algorithm; and a novel variable step-size Levenberg-Marquardt algorithm – which can effectively enhance robot positioning accuracy. We develop some control and calibration schemes for enhancing the tracking and positioning accuracy of the robot. The first model is a Model Predictive Control (MPC) scheme for robot trajectory tracking, which can optimize tracking error, velocity norm, and acceleration norm. The second model is a new recurrent neural network (RNN), which has better performance in dealing with time-varying underdetermined linear systems with double limits. The third model is a novel Joint Drift Free (JDF) scheme synthesized by the Projection Zeroing Neural Network (PZNN) model, which can effectively solve the motion generation and control of redundant manipulators under disturbance.

Название: Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions
Автор: Ivan Gridin
Издательство: BPB Publications
Год: 2022
Страниц: 317
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Explore the infinite possibilities offered by Artificial Intelligence and Neural Networks. This book is amid at teaching the readers how to apply the deep learning techniques to the time series forecasting challenges and how to build prediction models using PyTorch. The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the programme has been developed. You will try to use machine learning to identify the patterns that can help us forecast the future results. It covers methodologies such as Recurrent Neural Network, Encoder-decoder model, and Temporal Convolutional Network, all of which are state-of-the-art neural network architectures. Furthermore, for good measure, we have also introduced the neural architecture search, which automates searching for an ideal neural network design for a certain task.