Data Driven Iterative Learning Control For Discrete Time Systems

Author : Ronghu Chi
Publisher : Springer Nature
ISBN 13 : 9811959501
Total Pages : 239 pages
Book Rating : 4.09/5 ( download)

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Download or read book Data-Driven Iterative Learning Control for Discrete-Time Systems written by Ronghu Chi and published by Springer Nature. This book was released on 2022-11-15 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
ISBN 13 : 9811904642
Total Pages : 211 pages
Book Rating : 4.46/5 ( download)

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Download or read book Discrete-Time Adaptive Iterative Learning Control written by Ronghu Chi and published by Springer Nature. This book was released on 2022-03-21 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Bing Song
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.91/5 ( download)

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Download or read book From Model-Based to Data-Driven Discrete-Time Iterative Learning Control written by Bing Song and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The improvement here makes use of Carleman bilinearized models to capture more nonlinear dynamics, with the potential for faster convergence when compared to existing methods based on linearized models. The last work presented here finally uses model-free reinforcement learning (RL) to eliminate the need for an a priori model. It is analogous to direct adaptive control using data to directly produce the gains in the ILC law without use of a model. An off-policy RL method is first developed by extending a model-free model predictive control method and then applied in the trial domain for ILC. Adjustments of the ILC learning law and the RL recursion equation for state-value function updates allow the collection of enough data while improving the tracking accuracy without much safety concerns. This algorithm can be seen as the first step to bridge ILC and RL aiming to address nonlinear systems.

Author : Zhongsheng Hou
Publisher : CRC Press
ISBN 13 : 1466594187
Total Pages : 400 pages
Book Rating : 4.80/5 ( download)

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Download or read book Model Free Adaptive Control written by Zhongsheng Hou and published by CRC Press. This book was released on 2013-09-24 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Radu-Emil Precup
Publisher : CRC Press
ISBN 13 : 1000519635
Total Pages : 408 pages
Book Rating : 4.31/5 ( download)

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Download or read book Data-Driven Model-Free Controllers written by Radu-Emil Precup and published by CRC Press. This book was released on 2021-12-27 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Zeungnam Bien
Publisher : Springer Science & Business Media
ISBN 13 : 1461556295
Total Pages : 384 pages
Book Rating : 4.99/5 ( download)

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Download or read book Iterative Learning Control written by Zeungnam Bien and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Iterative Learning Control (ILC) differs from most existing control methods in the sense that, it exploits every possibility to incorporate past control informa tion, such as tracking errors and control input signals, into the construction of the present control action. There are two phases in Iterative Learning Control: first the long term memory components are used to store past control infor mation, then the stored control information is fused in a certain manner so as to ensure that the system meets control specifications such as convergence, robustness, etc. It is worth pointing out that, those control specifications may not be easily satisfied by other control methods as they require more prior knowledge of the process in the stage of the controller design. ILC requires much less information of the system variations to yield the desired dynamic be haviors. Due to its simplicity and effectiveness, ILC has received considerable attention and applications in many areas for the past one and half decades. Most contributions have been focused on developing new ILC algorithms with property analysis. Since 1992, the research in ILC has progressed by leaps and bounds. On one hand, substantial work has been conducted and reported in the core area of developing and analyzing new ILC algorithms. On the other hand, researchers have realized that integration of ILC with other control techniques may give rise to better controllers that exhibit desired performance which is impossible by any individual approach.

Author : Wenjun Xiong
Publisher : Springer Nature
ISBN 13 : 9819709261
Total Pages : 229 pages
Book Rating : 4.67/5 ( download)

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Download or read book Iterative Learning Control for Network Systems Under Constrained Information Communication written by Wenjun Xiong and published by Springer Nature. This book was released on with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Dong Shen
Publisher : Springer
ISBN 13 : 9811361363
Total Pages : 256 pages
Book Rating : 4.64/5 ( download)

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Download or read book Iterative Learning Control for Systems with Iteration-Varying Trial Lengths written by Dong Shen and published by Springer. This book was released on 2019-01-29 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : David H. Owens
Publisher : Springer
ISBN 13 : 1447167724
Total Pages : 473 pages
Book Rating : 4.23/5 ( download)

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Download or read book Iterative Learning Control written by David H. Owens and published by Springer. This book was released on 2015-10-31 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 presents them separately as their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates the underlying robustness of the paradigm and also includes new control laws that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference and auxiliary signals and also to support new properties such as spectral annihilation. Iterative Learning Control will interest academics and graduate students working in control who will find it a useful reference to the current status of a powerful and increasingly popular method of control. The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes.

Author : Ding Wang
Publisher : Springer Nature
ISBN 13 : 9811972915
Total Pages : 283 pages
Book Rating : 4.11/5 ( download)

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Download or read book Advanced Optimal Control and Applications Involving Critic Intelligence written by Ding Wang and published by Springer Nature. This book was released on 2023-01-21 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.