Predictive Learning Control For Unknown Nonaffine Nonlinear Systems

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Predictive Learning Control for Unknown Nonaffine Nonlinear Systems

Qiongxia Yu,Ting Lei,Fengchen Tian,Zhongsheng Hou,Xuhui Bu
Predictive Learning Control for Unknown Nonaffine Nonlinear Systems Book PDF

Author : Qiongxia Yu,Ting Lei,Fengchen Tian,Zhongsheng Hou,Xuhui Bu
Publisher : Springer Nature
Page : 219 pages
File Size : 53,8 Mb
Release : 2023-02-17
Category : Technology & Engineering
ISBN : 9789811988578

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Predictive Learning Control for Unknown Nonaffine Nonlinear Systems by Qiongxia Yu,Ting Lei,Fengchen Tian,Zhongsheng Hou,Xuhui Bu Pdf

This book investigates both theory and various applications of predictive learning control (PLC) which is an advanced technology for complex nonlinear systems. To avoid the difficult modeling problem for complex nonlinear systems, this book begins with the design and theoretical analysis of PLC method without using mechanism model information of the system, and then a series of PLC methods is designed that can cope with system constraints, varying trial lengths, unknown time delay, and available and unavailable system states sequentially. Applications of the PLC on both railway and urban road transportation systems are also studied. The book is intended for researchers, engineers, and graduate students who are interested in predictive control, learning control, intelligent transportation systems and related fields.

Data-Driven Iterative Learning Control for Discrete-Time Systems

Ronghu Chi,Yu Hui,Zhongsheng Hou
Data Driven Iterative Learning Control for Discrete Time Systems Book PDF

Author : Ronghu Chi,Yu Hui,Zhongsheng Hou
Publisher : Springer Nature
Page : 239 pages
File Size : 40,8 Mb
Release : 2022-11-15
Category : Technology & Engineering
ISBN : 9789811959509

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Data-Driven Iterative Learning Control for Discrete-Time Systems by Ronghu Chi,Yu Hui,Zhongsheng Hou Pdf

This book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system’s output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.

Advanced Optimal Control and Applications Involving Critic Intelligence

Ding Wang,Mingming Ha,Mingming Zhao
Advanced Optimal Control and Applications Involving Critic Intelligence Book PDF

Author : Ding Wang,Mingming Ha,Mingming Zhao
Publisher : Springer Nature
Page : 283 pages
File Size : 49,7 Mb
Release : 2023-01-21
Category : Technology & Engineering
ISBN : 9789811972911

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Advanced Optimal Control and Applications Involving Critic Intelligence by Ding Wang,Mingming Ha,Mingming Zhao Pdf

This book intends to report new optimal control results with critic intelligence for complex discrete-time systems, which covers the novel control theory, advanced control methods, and typical applications for wastewater treatment systems. Therein, combining with artificial intelligence techniques, such as neural networks and reinforcement learning, the novel intelligent critic control theory as well as a series of advanced optimal regulation and trajectory tracking strategies are established for discrete-time nonlinear systems, followed by application verifications to complex wastewater treatment processes. Consequently, developing such kind of critic intelligence approaches is of great significance for nonlinear optimization and wastewater recycling. The book is likely to be of interest to researchers and practitioners as well as graduate students in automation, computer science, and process industry who wish to learn core principles, methods, algorithms, and applications in the field of intelligent optimal control. It is beneficial to promote the development of intelligent optimal control approaches and the construction of high-level intelligent systems.

Deep Learning for Unmanned Systems

Anis Koubaa,Ahmad Taher Azar
Deep Learning for Unmanned Systems Book PDF

Author : Anis Koubaa,Ahmad Taher Azar
Publisher : Springer Nature
Page : 731 pages
File Size : 51,9 Mb
Release : 2021-10-01
Category : Technology & Engineering
ISBN : 9783030779399

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Deep Learning for Unmanned Systems by Anis Koubaa,Ahmad Taher Azar Pdf

This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.

Robust Discrete-Time Flight Control of UAV with External Disturbances

Shuyi Shao,Mou Chen,Peng Shi
Robust Discrete Time Flight Control of UAV with External Disturbances Book PDF

Author : Shuyi Shao,Mou Chen,Peng Shi
Publisher : Springer Nature
Page : 207 pages
File Size : 48,7 Mb
Release : 2020-09-26
Category : Technology & Engineering
ISBN : 9783030579579

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Robust Discrete-Time Flight Control of UAV with External Disturbances by Shuyi Shao,Mou Chen,Peng Shi Pdf

This book studies selected discrete-time flight control schemes for fixed-wing unmanned aerial vehicle (UAV) systems in the presence of system uncertainties, external disturbances and input saturation. The main contributions of this book for UAV systems are as follows: (i) the proposed integer-order discrete-time control schemes are based on the designed discrete-time disturbance observers (DTDOs) and the neural network (NN); and (ii) the fractional-order discrete-time control schemes are developed by using the fractional-order calculus theory, the NN and the DTDOs. The book offers readers a good understanding of how to establish discrete-time tracking control schemes for fixed-wing UAV systems subject to system uncertainties, external wind disturbances and input saturation. It represents a valuable reference guide for academic research on uncertain UAV systems, and can also support advanced / Ph.D. studies on control theory and engineering.

Advanced Control Engineering Methods in Electrical Engineering Systems

Mohammed Chadli,Sofiane Bououden,Salim Ziani,Ivan Zelinka
Advanced Control Engineering Methods in Electrical Engineering Systems Book PDF

Author : Mohammed Chadli,Sofiane Bououden,Salim Ziani,Ivan Zelinka
Publisher : Springer
Page : 576 pages
File Size : 55,6 Mb
Release : 2018-09-10
Category : Technology & Engineering
ISBN : 9783319978161

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Advanced Control Engineering Methods in Electrical Engineering Systems by Mohammed Chadli,Sofiane Bououden,Salim Ziani,Ivan Zelinka Pdf

This book presents the proceedings of the Third International Conference on Electrical Engineering and Control (ICEECA2017). It covers new control system models and troubleshooting tips, and also addresses complex system requirements, such as increased speed, precision and remote capabilities, bridging the gap between the complex, math-heavy controls theory taught in formal courses, and the efficient implementation required in real-world industry settings. Further, it considers both the engineering aspects of signal processing and the practical issues in the broad field of information transmission and novel technologies for communication networks and modern antenna design. This book is intended for researchers, engineers, and advanced postgraduate students in control and electrical engineering, computer science, signal processing, as well as mechanical and chemical engineering.

Parameter Estimation and Adaptive Control for Nonlinear Servo Systems

Shubo Wang,Jing Na,Xuemei Ren
Parameter Estimation and Adaptive Control for Nonlinear Servo Systems Book PDF

Author : Shubo Wang,Jing Na,Xuemei Ren
Publisher : Elsevier
Page : 304 pages
File Size : 52,9 Mb
Release : 2024-02-01
Category : Technology & Engineering
ISBN : 9780443155758

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Parameter Estimation and Adaptive Control for Nonlinear Servo Systems by Shubo Wang,Jing Na,Xuemei Ren Pdf

Parameter Estimation and Adaptive Control for Nonlinear Servo Systems presents the latest advances in observer-based control design, focusing on adaptive control for nonlinear systems such as adaptive neural network control, adaptive parameter estimation, and system identification. This book offers an array of new real-world applications in the field. Written by eminent scientists in the field of control theory, this book covers the latest advances in observer-based control design. It provides fundamentals, algorithms, and it discusses key applications in the fields of power systems, robotics and mechatronics, flight and automotive systems. Presents a clear and concise introduction to the latest advances in parameter estimation and adaptive control with several concise applications for servo systems Covers a wide range of applications usually not found in similar books, such as power systems, robotics, mechatronics, aeronautics, and industrial systems Contains worked examples which make it ideal for advanced courses as well as for researchers starting to work in the field, particularly suitable for engineers wishing to enter the field quickly and efficiently

Neural Computing for Advanced Applications

Haijun Zhang,Yuehui Chen,Xianghua Chu,Zhao Zhang,Tianyong Hao,Zhou Wu,Yimin Yang
Neural Computing for Advanced Applications Book PDF

Author : Haijun Zhang,Yuehui Chen,Xianghua Chu,Zhao Zhang,Tianyong Hao,Zhou Wu,Yimin Yang
Publisher : Springer Nature
Page : 566 pages
File Size : 40,7 Mb
Release : 2022-10-20
Category : Computers
ISBN : 9789811961427

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Neural Computing for Advanced Applications by Haijun Zhang,Yuehui Chen,Xianghua Chu,Zhao Zhang,Tianyong Hao,Zhou Wu,Yimin Yang Pdf

The two-volume Proceedings set CCIS 1637 and 1638 constitutes the refereed proceedings of the Third International Conference on Neural Computing for Advanced Applications, NCAA 2022, held in Jinan, China, during July 8–10, 2022. The 77 papers included in these proceedings were carefully reviewed and selected from 205 submissions. These papers were categorized into 10 technical tracks, i.e., neural network theory, and cognitive sciences, machine learning, data mining, data security & privacy protection, and data-driven applications, computational intelligence, nature-inspired optimizers, and their engineering applications, cloud/edge/fog computing, the Internet of Things/Vehicles (IoT/IoV), and their system optimization, control systems, network synchronization, system integration, and industrial artificial intelligence, fuzzy logic, neuro-fuzzy systems, decision making, and their applications in management sciences, computer vision, image processing, and their industrial applications, natural language processing, machine translation, knowledge graphs, and their applications, Neural computing-based fault diagnosis, fault forecasting, prognostic management, and system modeling, and Spreading dynamics, forecasting, and other intelligent techniques against coronavirus disease (COVID-19).

Non-linear Predictive Control

Basil Kouvaritakis,Mark Cannon
Non linear Predictive Control Book PDF

Author : Basil Kouvaritakis,Mark Cannon
Publisher : IET
Page : 277 pages
File Size : 42,6 Mb
Release : 2001-10-26
Category : Mathematics
ISBN : 9780852969847

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Non-linear Predictive Control by Basil Kouvaritakis,Mark Cannon Pdf

The advantage of model predictive control is that it can take systematic account of constraints, thereby allowing processes to operate at the limits of achievable performance. Engineers in academia, industry, and government from the US and Europe explain how the linear version can be adapted and applied to the nonlinear conditions that characterize the dynamics of most real manufacturing plants. They survey theoretical and practical trends, describe some specific theories and demonstrate their practical application, derive strategies that provide appropriate assurance of closed-loop stability, and discuss practical implementation. Annotation copyrighted by Book News, Inc., Portland, OR

Intelligent Optimal Adaptive Control for Mechatronic Systems

Marcin Szuster,Zenon Hendzel
Intelligent Optimal Adaptive Control for Mechatronic Systems Book PDF

Author : Marcin Szuster,Zenon Hendzel
Publisher : Springer
Page : 382 pages
File Size : 40,6 Mb
Release : 2017-12-28
Category : Technology & Engineering
ISBN : 9783319688268

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Intelligent Optimal Adaptive Control for Mechatronic Systems by Marcin Szuster,Zenon Hendzel Pdf

The book deals with intelligent control of mobile robots, presenting the state-of-the-art in the field, and introducing new control algorithms developed and tested by the authors. It also discusses the use of artificial intelligent methods like neural networks and neuraldynamic programming, including globalised dual-heuristic dynamic programming, for controlling wheeled robots and robotic manipulators,and compares them to classical control methods.

Artificial Intelligence and Soft Computing

Leszek Rutkowski,Marcin Korytkowski,Rafał Scherer,Ryszard Tadeusiewicz,Lotfi A. Zadeh,Jacek M. Zurada
Artificial Intelligence and Soft Computing Book PDF

Author : Leszek Rutkowski,Marcin Korytkowski,Rafał Scherer,Ryszard Tadeusiewicz,Lotfi A. Zadeh,Jacek M. Zurada
Publisher : Springer
Page : 834 pages
File Size : 44,6 Mb
Release : 2014-05-22
Category : Computers
ISBN : 9783319071763

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Artificial Intelligence and Soft Computing by Leszek Rutkowski,Marcin Korytkowski,Rafał Scherer,Ryszard Tadeusiewicz,Lotfi A. Zadeh,Jacek M. Zurada Pdf

The two-volume set LNAI 8467 and LNAI 8468 constitutes the refereed proceedings of the 13th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2014, held in Zakopane, Poland in June 2014. The 139 revised full papers presented in the volumes, were carefully reviewed and selected from 331 submissions. The 69 papers included in the first volume are focused on the following topical sections: Neural Networks and Their Applications, Fuzzy Systems and Their Applications, Evolutionary Algorithms and Their Applications, Classification and Estimation, Computer Vision, Image and Speech Analysis and Special Session 3: Intelligent Methods in Databases. The 71 papers in the second volume are organized in the following subjects: Data Mining, Bioinformatics, Biometrics and Medical Applications, Agent Systems, Robotics and Control, Artificial Intelligence in Modeling and Simulation, Various Problems of Artificial Intelligence, Special Session 2: Machine Learning for Visual Information Analysis and Security, Special Session 1: Applications and Properties of Fuzzy Reasoning and Calculus and Clustering.

Iterative Learning Control for Nonlinear Time-Delay System

Jianming Wei,Hong Wang,Fang Liu
Iterative Learning Control for Nonlinear Time Delay System Book PDF

Author : Jianming Wei,Hong Wang,Fang Liu
Publisher : Springer Nature
Page : 185 pages
File Size : 44,6 Mb
Release : 2023-01-01
Category : Technology & Engineering
ISBN : 9789811963179

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Iterative Learning Control for Nonlinear Time-Delay System by Jianming Wei,Hong Wang,Fang Liu Pdf

This book focuses on adaptive iterative learning control problem for nonlinear time-delay systems.A universal adaptive learning control scheme is provided for a wide classes of nonlinear systems with time-varying delay and input nonlinearity. Proceeding from easy to difficult, this book deals with the adaptive iterative learning control problems for parameterized nonlinear time-delay systems, non-parameterized nonlinear time-delay systems, nonlinear time-delay systems with unknown control direction and nonlinear time-delay systems with un-measurable states. The proposed control schemes can be extended to the adaptive learning control problem for wider classes of nonlinear systems revelent to abovementioned nonlinear systems.The topics presented in this book are research hot spots of iterative learning control. This book will be a valuable reference for researchers and students working or studying in this area.

Discrete-Time Adaptive Iterative Learning Control

Ronghu Chi,Na Lin,Huimin Zhang,Ruikun Zhang
Discrete Time Adaptive Iterative Learning Control Book PDF

Author : Ronghu Chi,Na Lin,Huimin Zhang,Ruikun Zhang
Publisher : Springer Nature
Page : 211 pages
File Size : 46,9 Mb
Release : 2022-03-21
Category : Technology & Engineering
ISBN : 9789811904646

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Discrete-Time Adaptive Iterative Learning Control by Ronghu Chi,Na Lin,Huimin Zhang,Ruikun Zhang Pdf

This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various applications. This book is intended for academic scholars, engineers and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.

Computational Intelligence

Nazmul Siddique,Hojjat Adeli
Computational Intelligence Book PDF

Author : Nazmul Siddique,Hojjat Adeli
Publisher : John Wiley & Sons
Page : 524 pages
File Size : 52,8 Mb
Release : 2013-05-06
Category : Technology & Engineering
ISBN : 9781118534816

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Computational Intelligence by Nazmul Siddique,Hojjat Adeli Pdf

Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB® exercises and applications in each chapter Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems Considers real world problems in the domain of systems modelling, control and optimization Contains a foreword written by Lotfi Zadeh Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.

Learning-based Model Predictive Control with closed-loop guarantees

Raffaele Soloperto
Learning based Model Predictive Control with closed loop guarantees Book PDF

Author : Raffaele Soloperto
Publisher : Logos Verlag Berlin GmbH
Page : 172 pages
File Size : 53,6 Mb
Release : 2023-11-13
Category : Electronic
ISBN : 9783832557447

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Learning-based Model Predictive Control with closed-loop guarantees by Raffaele Soloperto Pdf

The performance of model predictive control (MPC) largely depends on the accuracy of the prediction model and of the constraints the system is subject to. However, obtaining an accurate knowledge of these elements might be expensive in terms of money and resources, if at all possible. In this thesis, we develop novel learning-based MPC frameworks that actively incentivize learning of the underlying system dynamics and of the constraints, while ensuring recursive feasibility, constraint satisfaction, and performance bounds for the closed-loop. In the first part, we focus on the case of inaccurate models, and analyze learning-based MPC schemes that include, in addition to the primary cost, a learning cost that aims at generating informative data by inducing excitation in the system. In particular, we first propose a nonlinear MPC framework that ensures desired performance bounds for the resulting closed-loop, and then we focus on linear systems subject to uncertain parameters and noisy output measurements. In order to ensure that the desired learning phase occurs in closed-loop operations, we then propose an MPC framework that is able to guarantee closed-loop learning of the controlled system. In the last part of the thesis, we investigate the scenario where the system is known but evolves in a partially unknown environment. In such a setup, we focus on a learning-based MPC scheme that incentivizes safe exploration if and only if this might yield to a performance improvement.