Data-Driven Iterative Learning Control for Discrete-Time Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data-Driven Iterative Learning Control for Discrete-Time Systems write by Ronghu Chi. This book was released on 2022-11-15. Data-Driven Iterative Learning Control for Discrete-Time Systems available 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.
Discrete-Time Adaptive Iterative Learning Control
Discrete-Time Adaptive Iterative Learning Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Discrete-Time Adaptive Iterative Learning Control write by Ronghu Chi. This book was released on 2022-03-21. Discrete-Time Adaptive Iterative Learning Control available 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.
From Model-Based to Data-Driven Discrete-Time Iterative Learning Control
From Model-Based to Data-Driven Discrete-Time Iterative Learning Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook From Model-Based to Data-Driven Discrete-Time Iterative Learning Control write by Bing Song. This book was released on 2019. From Model-Based to Data-Driven Discrete-Time Iterative Learning Control available in PDF, EPUB and Kindle. 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.
Data-Driven Model-Free Controllers
Data-Driven Model-Free Controllers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data-Driven Model-Free Controllers write by Radu-Emil Precup. This book was released on 2021-12-27. Data-Driven Model-Free Controllers available 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.
Model Free Adaptive Control
Model Free Adaptive Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Model Free Adaptive Control write by Zhongsheng Hou. This book was released on 2013-09-24. Model Free Adaptive Control available 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.
