Advanced Optimization for Motion Control Systems Advanced Optimization for Motion Control Systems

Advanced Optimization for Motion Control Systems

Jun Ma and Others
    • $69.99
    • $69.99

Publisher Description

Precision motion control is strongly required in many fields, such as precision engineering, micromanufacturing, biotechnology, and nanotechnology. Although great achievements have been made in control engineering, it is still challenging to fulfill the desired performance for precision motion control systems. Substantial works have been presented to reveal an increasing trend to apply optimization approaches in precision engineering to obtain the control system parameters. In this book, we present a result of several years of work in the area of advanced optimization for motion control systems.

The book is organized into two parts: Part I focuses on the model-based approaches, and Part II presents the data-based approaches. To illustrate the practical appeal of the proposed optimization techniques, theoretical results are verified with practical examples in each chapter. Industrial problems explored in the book are formulated systematically with necessary analysis of the control system synthesis.

By virtue of the design and implementation nature, this book can be used as a reference for engineers, researchers, and students who want to utilize control theories to solve the practical control problems. As the methodologies have extensive applicability in many control engineering problems, the research results in the field of optimization can be applied to full-fledged industrial processes, filling in the gap between research and application to achieve a technology frontier increment.

GENRE
Professional & Technical
RELEASED
2020
January 24
LANGUAGE
EN
English
LENGTH
182
Pages
PUBLISHER
CRC Press
SELLER
Taylor & Francis Group
SIZE
9.2
MB
The Economics of Air Pollution in China The Economics of Air Pollution in China
2016
Fast, Low-Resource, Accurate Robust Organ and Pan-cancer Segmentation Fast, Low-Resource, Accurate Robust Organ and Pan-cancer Segmentation
2025
Medical Image Segmentation Foundation Models. CVPR 2024 Challenge: Segment Anything in Medical Images on Laptop Medical Image Segmentation Foundation Models. CVPR 2024 Challenge: Segment Anything in Medical Images on Laptop
2025
Social Robotics Social Robotics
2025
Likelihood Methods in Survival Analysis Likelihood Methods in Survival Analysis
2024
Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT
2024