| 书目名称 | Identification and Control Using Volterra Models |
| 编辑 | F. J. Doyle,R. K. Pearson,B. A. Ogunnaike |
| 视频video | |
| 概述 | Identification results are presented from both deterministic (least squares) and stochastic perspectives.The identification of Volterra series is addressed from a new perspective, bringing to light ma |
| 丛书名称 | Communications and Control Engineering |
| 图书封面 |  |
| 描述 | Much has been written about the general difficulty of developing the models required for model-based control of processes whose dynamics exhibit signif icant nonlinearity (for further discussion and references, see Chapter 1). In fact, the development ofthese models stands as a significant practical imped iment to widespread industrial application oftechniques like nonlinear model predictive control (NMPC), whoselinear counterpart has profoundly changed industrial practice. One ofthe reasons for this difficulty lies in the enormous variety of "nonlinear models," different classes of which can be less similar to each other than they are to the class of linear models. Consequently, it is a practical necessity to restrict consideration to one or a few specific nonlinear model classes if we are to succeed in developing, understanding, and using nonlinear models as a basis for practical control schemes. Because they repre sent a highly structured extension ofthe class oflinear finite impulse response (FIR) models on which industrially popular linear MPC implementations are based, this book is devoted to the class of discrete-time Volterra models and a fewother, closelyrelated, nonlin |
| 出版日期 | Book 2002 |
| 关键词 | Volterra models; electrical engineering; identification; model; modeling; nonlinear; nonlinear control |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4471-0107-9 |
| isbn_softcover | 978-1-4471-1063-7 |
| isbn_ebook | 978-1-4471-0107-9Series ISSN 0178-5354 Series E-ISSN 2197-7119 |
| issn_series | 0178-5354 |
| copyright | Springer-Verlag London 2002 |
发表于 2025-3-21 18:22:30
