| 书目名称 | Nonlinear Predictive Control Using Wiener Models |
| 副标题 | Computationally Effi |
| 编辑 | Maciej Ławryńczuk |
| 视频video | |
| 概述 | Presents computationally efficient MPC algorithms for processes described by Wiener models.Provides computational efficiency of MPC as a key issue in this book.Shows approaches using on-line models or |
| 丛书名称 | Studies in Systems, Decision and Control |
| 图书封面 |  |
| 描述 | This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlinearity. Unfortunately, the model‘s structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant..A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages ofneural Wiener models are demonstrated.. |
| 出版日期 | Book 2022 |
| 关键词 | Process Control; Model Predictive Control; Wiener Models; Laguerre Parameterisation; Linearization; Optim |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-83815-7 |
| isbn_softcover | 978-3-030-83817-1 |
| isbn_ebook | 978-3-030-83815-7Series ISSN 2198-4182 Series E-ISSN 2198-4190 |
| issn_series | 2198-4182 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 20:04:20
