{"product_id":"discretetime-adaptive-iterative-learning-control-from-modelbased-to-datadriven-9789811904660","title":"Discrete-Time Adaptive Iterative Learning Control: From Model-Based to Data-Driven","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eDiscrete-time adaptive iterative learning control (DAILC) is a cutting-edge ILC method that addresses random initial states, iteration-varying targets, and non-repetitive uncertainties in practical applications. It discusses model-based and data-driven DAILC methods, including linear parametric data mapping, to facilitate broader applications. It is intended for academic scholars, engineers, and graduate students interested in control, adaptive control, nonlinear systems, and related fields. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 206 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 23 March 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer Verlag, Singapore\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThis comprehensive book delves into the realm of control and systems theory, focusing on the cutting-edge topic of discrete-time adaptive iterative learning control (DAILC). DAILC emerges as a powerful tool to address various challenges in practical applications, including dealing with random initial states, iteration-varying targets, and non-repetitive uncertainties.\u003cbr\u003e\u003cbr\u003eThe book begins by introducing the design and analysis of model-based DAILC methods, drawing upon the tools developed in discrete-time adaptive control theory. Recognizing the complexities inherent in modeling complex systems, the book then explores data-driven DAILC approaches, which establish a linear parametric data mapping between consecutive iterations. This innovative method facilitates the representation and manipulation of complex system dynamics, enabling more accurate and efficient control.\u003cbr\u003e\u003cbr\u003eFurthermore, the book delves into other significant improvements and extensions of model-based\/data-driven DAILC, broadening its applications across diverse fields. It discusses advanced control strategies, optimization techniques, and their integration with machine learning algorithms, enhancing the performance and adaptability of DAILC systems.\u003cbr\u003e\u003cbr\u003eThe book is meticulously crafted to cater to academic scholars, engineers, and graduate students interested in exploring control, adaptive control, nonlinear systems, and related disciplines. It provides a comprehensive and up-to-date overview of DAILC, covering its theoretical foundations, practical implementations, and real-world applications. With its clear explanations, illustrative examples, and extensive references, this book serves as a valuable resource for advancing knowledge and fostering innovation in the field of control and systems engineering.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 338g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9789811904660\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2022\u003c\/p\u003e","brand":"Ronghu Chi,Na Lin,Huimin Zhang,Ruikun Zhang","offers":[{"title":"Paperback \/ softback","offer_id":44307626524922,"sku":"9789811904660","price":91.62,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/noImage_1_bd5df34a-8978-41b5-943d-338ab35c6a6b.jpg?v=1688110611","url":"https:\/\/shulphink.com\/products\/discretetime-adaptive-iterative-learning-control-from-modelbased-to-datadriven-9789811904660","provider":"Shulph Ink","version":"1.0","type":"link"}