Learning Control

Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical systems. Computational methods for control systems, particularly those used for developing AI and other machine learning techniques, are also discussed at length. Provides foundational control theory concepts, along with advanced techniques and the latest advances in adaptive control and robotics Introduces state-of-the-art learning-based control technologies and their applications in robotics and other complex dynamical systems Demonstrates computational techniques for control systems Covers iterative learning impedance control in both human-robot interaction and collaborative robots

Produk Detail:

  • Author : Dan Zhang
  • Publisher : Elsevier
  • Pages : 280 pages
  • ISBN : 0128223154
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKLearning Control

Learning Control

Learning Control
  • Author : Dan Zhang,Bin Wei
  • Publisher : Elsevier
  • Release : 05 December 2020
GET THIS BOOKLearning Control

Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical

Iterative Learning Control with Passive Incomplete Information

Iterative Learning Control with Passive Incomplete Information
  • Author : Dong Shen
  • Publisher : Springer
  • Release : 16 April 2018
GET THIS BOOKIterative Learning Control with Passive Incomplete Information

This book presents an in-depth discussion of iterative learning control (ILC) with passive incomplete information, highlighting the incomplete input and output data resulting from practical factors such as data dropout, transmission disorder, communication delay, etc.—a cutting-edge topic in connection with the practical applications of ILC. It describes in detail three data dropout models: the random sequence model, Bernoulli variable model, and Markov chain model—for both linear and nonlinear stochastic systems. Further, it proposes and analyzes two major compensation

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence
  • Author : Thomas Duriez,Steven L. Brunton,Bernd R. Noack
  • Publisher : Springer
  • Release : 02 November 2016
GET THIS BOOKMachine Learning Control – Taming Nonlinear Dynamics and Turbulence

This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear

Iterative Learning Control for Multi-agent Systems Coordination

Iterative Learning Control for Multi-agent Systems Coordination
  • Author : Shiping Yang,Jian-Xin Xu,Xuefang Li,Dong Shen
  • Publisher : John Wiley & Sons
  • Release : 03 March 2017
GET THIS BOOKIterative Learning Control for Multi-agent Systems Coordination

A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, showcasing recent advances and industrially relevant applications Explores the synergy between the important topics of iterative learning control (ILC) and multi-agent systems (MAS) Concisely summarizes recent advances and significant applications in ILC methods for power grids, sensor networks and control processes Covers basic theory, rigorous mathematics as well as engineering practice

Iterative Learning Control for Systems with Iteration-Varying Trial Lengths

Iterative Learning Control for Systems with Iteration-Varying Trial Lengths
  • Author : Dong Shen,Xuefang Li
  • Publisher : Springer
  • Release : 29 January 2019
GET THIS BOOKIterative Learning Control for Systems with Iteration-Varying Trial Lengths

This book presents a comprehensive and detailed study on iterative learning control (ILC) for systems with iteration-varying trial lengths. Instead of traditional ILC, which requires systems to repeat on a fixed time interval, this book focuses on a more practical case where the trial length might randomly vary from iteration to iteration. The iteration-varying trial lengths may be different from the desired trial length, which can cause redundancy or dropouts of control information in ILC, making ILC design a challenging

Motor Learning and Control

Motor Learning and Control
  • Author : Richard A. Magill
  • Publisher : McGraw-Hill Humanities, Social Sciences & World Languages
  • Release : 25 May 2022
GET THIS BOOKMotor Learning and Control

Designed for introductory students, this text provides the reader with a solid research base and defines difficult material by identifying concepts and demonstrating applications for each of those concepts. Motor Learning and Control: Concepts and Applications also includes references for all relevant material to encourage students to examine the research for themselves.

Linear and Nonlinear Iterative Learning Control

Linear and Nonlinear Iterative Learning Control
  • Author : Jian-Xin Xu,Ying Tan
  • Publisher : Springer
  • Release : 04 September 2003
GET THIS BOOKLinear and Nonlinear Iterative Learning Control

This monograph summarizes the recent achievements made in the field of iterative learning control. The book is self-contained in theoretical analysis and can be used as a reference or textbook for a graduate level course as well as for self-study. It opens a new avenue towards a new paradigm in deterministic learning control theory accompanied by detailed examples.

Biological Learning and Control

Biological Learning and Control
  • Author : Reza Shadmehr,Sandro Mussa-Ivaldi
  • Publisher : MIT Press
  • Release : 27 January 2012
GET THIS BOOKBiological Learning and Control

A novel theoretical framework that describes a possible rationale for the regularity in how we move, how we learn, and how our brain predicts events. In Biological Learning and Control, Reza Shadmehr and Sandro Mussa-Ivaldi present a theoretical framework for understanding the regularity of the brain's perceptions, its reactions to sensory stimuli, and its control of movements. They offer an account of perception as the combination of prediction and observation: the brain builds internal models that describe what should happen

Automation and Control

Automation and Control
  • Author : Constantin Volosencu,Serdar Küçük,José Guerrero,Oscar Valero
  • Publisher : BoD – Books on Demand
  • Release : 21 April 2021
GET THIS BOOKAutomation and Control

The book presents recent theoretical and practical information about the field of automation and control. It includes fifteen chapters that promote automation and control in practical applications in the following thematic areas: control theory, autonomous vehicles, mechatronics, digital image processing, electrical grids, artificial intelligence, and electric motor drives. The book also presents and discusses applications that improve the properties and performances of process control with examples and case studies obtained from real-world research in the field. Automation and Control is

Iterative Learning Control

Iterative Learning Control
  • Author : David H. Owens
  • Publisher : Springer
  • Release : 31 October 2015
GET THIS BOOKIterative Learning Control

This book develops a coherent and quite general theoretical approach to algorithm design for iterative learning control based on the use of operator representations and quadratic optimization concepts including the related ideas of inverse model control and gradient-based design. Using detailed examples taken from linear, discrete and continuous-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text

Bio-Inspired Collaborative Intelligent Control and Optimization

Bio-Inspired Collaborative Intelligent Control and Optimization
  • Author : Yongsheng Ding,Lei Chen,Kuangrong Hao
  • Publisher : Springer
  • Release : 06 November 2017
GET THIS BOOKBio-Inspired Collaborative Intelligent Control and Optimization

This book presents state-of-the-art research advances in the field of biologically inspired cooperative control theories and their applications. It describes various biologically inspired cooperative control and optimization approaches and highlights real-world examples in complex industrial processes. Multidisciplinary in nature and closely integrating theory and practice, the book will be of interest to all university researchers, control engineers and graduate students in intelligent systems and control who wish to learn the core principles, methods, algorithms, and applications.

Human-in-the-Loop Robot Control and Learning

Human-in-the-Loop Robot Control and Learning
  • Author : Luka Peternel,Jan Babič,Erhan Oztop,Tetsunari Inamura,Dingguo Zhang
  • Publisher : Frontiers Media SA
  • Release : 22 January 2020
GET THIS BOOKHuman-in-the-Loop Robot Control and Learning

In the past years there has been considerable effort to move robots from industrial environments to our daily lives where they can collaborate and interact with humans to improve our life quality. One of the key challenges in this direction is to make a suitable robot control system that can adapt to humans and interactively learn from humans to facilitate the efficient and safe co-existence of the two. The applications of such robotic systems include: service robotics and physical human-robot