{"product_id":"nonlinear-predictive-control-using-wiener-models-computationally-efficient-approaches-for-polynomial-and-neural-structures-9783030838171","title":"Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eThis book presents an efficient MPC solution for controlling dynamical systems with Wiener models, addressing limitations in traditional MPC and offering improved control quality with online nonlinear optimization and parameterization using Laguerre functions. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 343 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 23 September 2022\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer Nature Switzerland AG\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThis book delves into the realm of computationally efficient model predictive control (MPC) solutions, offering a novel approach to controlling dynamical systems modeled by the Wiener model. While the classical MPC method employs an inverse static block to counteract the effects of process nonlinearity, it possesses certain limitations. Specifically, its structure is constrained, leading to suboptimal control performance when dealing with imperfect models and external disturbances. To address these challenges, an alternative solution is proposed: the computationally demanding MPC scheme, which involves online nonlinear optimization repeated at each sampling instant.\u003cbr\u003e\u003cbr\u003eWithin this framework, a linear approximation of the Wiener model or the predicted trajectory is derived online. This approximation results in quadratic optimization tasks, which can be efficiently solved using specialized algorithms. Furthermore, parameterization using Laguerre functions is explored, enabling the reduction of the number of decision variables, thus enhancing the computational efficiency of the MPC solution.\u003cbr\u003e\u003cbr\u003eTo substantiate the effectiveness of the proposed MPC algorithms, comprehensive simulation results are presented for ten benchmark processes. These results demonstrate that the discussed MPC algorithms yield exceptional control quality, surpassing traditional control strategies in terms of accuracy and stability. Moreover, the book highlights the essential advantages of neural Wiener models, particularly in the context of neutralization reactors and fuel cells.\u003cbr\u003e\u003cbr\u003eIn summary, this book presents a groundbreaking approach to MPC solutions, leveraging online optimization and parameterization techniques to achieve superior control performance in a wide range of applications. By overcoming the limitations of classical MPC methods, it opens up new possibilities for efficient and reliable control of complex dynamical systems.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 563g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9783030838171\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2022\u003c\/p\u003e","brand":"Maciej Lawrynczuk","offers":[{"title":"Paperback \/ softback","offer_id":44270970863866,"sku":"9783030838171","price":108.28,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/noImage_1_bbc7ab31-7527-4eb6-8b95-6ab4e68f5de3.jpg?v=1686155220","url":"https:\/\/shulphink.com\/products\/nonlinear-predictive-control-using-wiener-models-computationally-efficient-approaches-for-polynomial-and-neural-structures-9783030838171","provider":"Shulph Ink","version":"1.0","type":"link"}