Data Driven Iterative Learning Control For Discrete Time Systems PDF Download
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| Author | : Ronghu Chi |
| Publisher | : Springer Nature |
| Total Pages | : 239 |
| Release | : 2022-11-15 |
| Genre | : Technology & Engineering |
| ISBN | : 9811959501 |
Download Data-Driven Iterative Learning Control for Discrete-Time Systems Book in PDF, ePub and Kindle
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.
| Author | : Ronghu Chi |
| Publisher | : Springer Nature |
| Total Pages | : 211 |
| Release | : 2022-03-21 |
| Genre | : Technology & Engineering |
| ISBN | : 9811904642 |
Download Discrete-Time Adaptive Iterative Learning Control Book in PDF, ePub and Kindle
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.
| Author | : Wenjun Xiong |
| Publisher | : Springer Nature |
| Total Pages | : 229 |
| Release | : |
| Genre | : |
| ISBN | : 9819709261 |
Download Iterative Learning Control for Network Systems Under Constrained Information Communication Book in PDF, ePub and Kindle
| Author | : Zhongsheng Hou |
| Publisher | : CRC Press |
| Total Pages | : 400 |
| Release | : 2013-09-24 |
| Genre | : Technology & Engineering |
| ISBN | : 1466594187 |
Download Model Free Adaptive Control Book in PDF, ePub and Kindle
Model Free Adaptive Control: Theory and Applications summarizes theory and applications of model-free adaptive control (MFAC). MFAC is a novel adaptive control method for the unknown discrete-time nonlinear systems with time-varying parameters and time-varying structure, and the design and analysis of MFAC merely depend on the measured input and output data of the controlled plant, which makes it more applicable for many practical plants. This book covers new concepts, including pseudo partial derivative, pseudo gradient, pseudo Jacobian matrix, and generalized Lipschitz conditions, etc.; dynamic linearization approaches for nonlinear systems, such as compact-form dynamic linearization, partial-form dynamic linearization, and full-form dynamic linearization; a series of control system design methods, including MFAC prototype, model-free adaptive predictive control, model-free adaptive iterative learning control, and the corresponding stability analysis and typical applications in practice. In addition, some other important issues related to MFAC are also discussed. They are the MFAC for complex connected systems, the modularized controller designs between MFAC and other control methods, the robustness of MFAC, and the symmetric similarity for adaptive control system design. The book is written for researchers who are interested in control theory and control engineering, senior undergraduates and graduated students in engineering and applied sciences, as well as professional engineers in process control.
| Author | : Dong Shen |
| Publisher | : Springer |
| Total Pages | : 256 |
| Release | : 2019-01-29 |
| Genre | : Technology & Engineering |
| ISBN | : 9811361363 |
Download Iterative Learning Control for Systems with Iteration-Varying Trial Lengths Book in PDF, ePub and Kindle
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 problem. The book focuses on the synthesis and analysis of ILC for both linear and nonlinear systems with iteration-varying trial lengths, and proposes various novel techniques to deal with the precise tracking problem under non-repeatable trial lengths, such as moving window, switching system, and searching-based moving average operator. It not only discusses recent advances in ILC for systems with iteration-varying trial lengths, but also includes numerous intuitive figures to allow readers to develop an in-depth understanding of the intrinsic relationship between the incomplete information environment and the essential tracking performance. This book is intended for academic scholars and engineers who are interested in learning about control, data-driven control, networked control systems, and related fields. It is also a useful resource for graduate students in the above field.
| Author | : Ding Wang |
| Publisher | : Springer Nature |
| Total Pages | : 283 |
| Release | : 2023-01-21 |
| Genre | : Technology & Engineering |
| ISBN | : 9811972915 |
Download Advanced Optimal Control and Applications Involving Critic Intelligence Book in PDF, ePub and Kindle
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.
| Author | : Qiongxia Yu |
| Publisher | : Springer Nature |
| Total Pages | : 219 |
| Release | : 2023-02-17 |
| Genre | : Technology & Engineering |
| ISBN | : 9811988579 |
Download Predictive Learning Control for Unknown Nonaffine Nonlinear Systems Book in PDF, ePub and Kindle
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.
| Author | : Radu-Emil Precup |
| Publisher | : CRC Press |
| Total Pages | : 408 |
| Release | : 2021-12-27 |
| Genre | : Technology & Engineering |
| ISBN | : 1000519635 |
Download Data-Driven Model-Free Controllers Book in PDF, ePub and Kindle
This book categorizes the wide area of data-driven model-free controllers, reveals the exact benefits of such controllers, gives the in-depth theory and mathematical proofs behind them, and finally discusses their applications. Each chapter includes a section for presenting the theory and mathematical definitions of one of the above mentioned algorithms. The second section of each chapter is dedicated to the examples and applications of the corresponding control algorithms in practical engineering problems. This book proposes to avoid complex mathematical equations, being generic as it includes several types of data-driven model-free controllers, such as Iterative Feedback Tuning controllers, Model-Free Controllers (intelligent PID controllers), Model-Free Adaptive Controllers, model-free sliding mode controllers, hybrid model‐free and model‐free adaptive‐Virtual Reference Feedback Tuning controllers, hybrid model-free and model-free adaptive fuzzy controllers and cooperative model-free controllers. The book includes the topic of optimal model-free controllers, as well. The optimal tuning of model-free controllers is treated in the chapters that deal with Iterative Feedback Tuning and Virtual Reference Feedback Tuning. Moreover, the extension of some model-free control algorithms to the consensus and formation-tracking problem of multi-agent dynamic systems is provided. This book can be considered as a textbook for undergraduate and postgraduate students, as well as a professional reference for industrial and academic researchers, attracting the readers from both industry and academia.
| Author | : Hassan Haes Alhelou |
| Publisher | : Springer Nature |
| Total Pages | : 463 |
| Release | : 2023-03-12 |
| Genre | : Technology & Engineering |
| ISBN | : 3031203607 |
Download Power Systems Cybersecurity Book in PDF, ePub and Kindle
This book covers power systems cybersecurity. In order to enhance overall stability and security in wide-area cyber-physical power systems and defend against cyberattacks, new resilient operation, control, and protection methods are required. The cyberattack-resilient control methods improve overall cybersecurity and stability in normal and abnormal operating conditions. By contrast, cyberattack-resilient protection schemes are important to keep the secure operation of a system under the most severe contingencies and cyberattacks. The main subjects covered in the book are: 1) proposing new tolerant and cyberattack-resilient control and protection methods against cyberattacks for future power systems, 2) suggesting new methods for cyberattack detection and cybersecurity assessment, and 3) focusing on practical issues in modern power systems.
| Author | : Jian-Xin Xu |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 204 |
| Release | : 2008-12-12 |
| Genre | : Technology & Engineering |
| ISBN | : 1848821751 |
Download Real-time Iterative Learning Control Book in PDF, ePub and Kindle
Real-time Iterative Learning Control demonstrates how the latest advances in iterative learning control (ILC) can be applied to a number of plants widely encountered in practice. The book gives a systematic introduction to real-time ILC design and source of illustrative case studies for ILC problem solving; the fundamental concepts, schematics, configurations and generic guidelines for ILC design and implementation are enhanced by a well-selected group of representative, simple and easy-to-learn example applications. Key issues in ILC design and implementation in linear and nonlinear plants pervading mechatronics and batch processes are addressed, in particular: ILC design in the continuous- and discrete-time domains; design in the frequency and time domains; design with problem-specific performance objectives including robustness and optimality; design in a modular approach by integration with other control techniques; and design by means of classical tools based on Bode plots and state space.
