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內容簡介 Many common industrial process control problems exhibit nonlinear behavior, and the limitations of traditional linear controllers in managing these processes have become increasingly apparent. Meanwhile, new model-based controller design strategies—coupled with more inexpensive and powerful computer—have made nonlinear process control much more practical. As a result, understanding nonlinear process control has become a high priority in both industry and academia. Nonlinear Process Control assembles the latest theoretical and practical research on design, analysis and application of nonlinear process control strategies. It presents detailed coverage of all three major elements of nonlinear process control: identification, controller design, and state estimation. An introductory chapter outlines the issues driving nonlinear process control research, as well as several classic techniques. The book then offers a detailed introduction to nonlinear process modeling, which is central to every nonlinear control strategy. Once the fundamentals are understood, Nonlinear Process Control covers two leading approaches: input/output linearization and nonlinear predictive control. The book also shows how to design nonlinear state observers that permit control even if on-line measurements of all state variables cannot be obtained. Finally, the book discusses new techniques for obtaining nonlinear empirical models using artificial neural networks, which are becoming increasingly prevalent in industry. Nonlinear Process Control reflects the contributions of eleven leading researchers in the field. It is an ideal textbook for graduate courses in process control, as well as a concise, up-to-date reference for control engineers.
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