Model Free Data Driven Optimal Tuning of Controller Parameters - 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 Data Driven Optimal Tuning of Controller Parameters write by David-Pieter Molenaar. This book was released on 1995. Model Free Data Driven Optimal Tuning of Controller Parameters available in PDF, EPUB and Kindle.
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 Data Driven Optimal Tuning of Controller Parameters
Model Free Data Driven Optimal Tuning of Controller Parameters - 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 Data Driven Optimal Tuning of Controller Parameters write by Håkan Hjalmarsson. This book was released on 1994. Model Free Data Driven Optimal Tuning of Controller Parameters available in PDF, EPUB and Kindle.
Data Driven Strategies
Data Driven Strategies - 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 Strategies write by Wang Jianhong. This book was released on 2023-03-31. Data Driven Strategies available in PDF, EPUB and Kindle. A key challenge in science and engineering is to provide a quantitative description of the systems under investigation, leveraging the noisy data collected. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in engineering. The book aims to develop model-free system analysis and control strategies, i.e., data-driven control from theoretical analysis and engineering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book reviews the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strategies to be considered data-driven control strategies, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strategies are applied to interested engineering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems.
Advanced Trajectory Optimization, Guidance and Control Strategies for Aerospace Vehicles
Advanced Trajectory Optimization, Guidance and Control Strategies for Aerospace Vehicles - 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 Advanced Trajectory Optimization, Guidance and Control Strategies for Aerospace Vehicles write by Runqi Chai. This book was released on 2023-10-29. Advanced Trajectory Optimization, Guidance and Control Strategies for Aerospace Vehicles available in PDF, EPUB and Kindle. This book focuses on the design and application of advanced trajectory optimization and guidance and control (G&C) techniques for aerospace vehicles. Part I of the book focuses on the introduction of constrained aerospace vehicle trajectory optimization problems, with particular emphasis on the design of high-fidelity trajectory optimization methods, heuristic optimization-based strategies, and fast convexification-based algorithms. In Part II, various optimization theory/artificial intelligence (AI)-based methods are constructed and presented, including dynamic programming-based methods, model predictive control-based methods, and deep neural network-based algorithms. Key aspects of the application of these approaches, such as their main advantages and inherent challenges, are detailed and discussed. Some practical implementation considerations are then summarized, together with a number of future research topics. The comprehensive and systematic treatment of practical issues in aerospace trajectory optimization and guidance and control problems is one of the main features of the book, which is particularly suitable for readers interested in learning practical solutions in aerospace trajectory optimization and guidance and control. The book is useful to researchers, engineers, and graduate students in the fields of G&C systems, engineering optimization, applied optimal control theory, etc.
