Chemical engineers assemble the theoretical and practical research on the design, analysis, and application of nonlinear process control strategies, much easier of late because of model- based approaches and more inexpensive and powerful computers. They outline the issues driving the research and several classic techniques, and provide a detailed introduction to nonlinear process modeling. Then they describe the two leading approaches of input/output linearization and nonlinear predictive control, show how to design state observers that permit control even if on-line measurements of all state variables cannot be obtained, and discuss new techniques for developing empirical models using artificial neural networks. Annotation c. by Book News, Inc., Portland, Or.
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Process Control is the largest, most important segment of the chemical engineering industry. In the past decade, one of the most important topics within process control engineering has been the control of nonlinear systems. This work provides an introduction to nonlinear process control systems and covers four major topics: nonlinear dynamics; nonlinear identification; nonlinear controller design; and nonlinear state estimation. Case studies are presented to illustrate the controller design procedures. Feedback linearization techniques which yield exact linearization of the closed-loop system are discussed, as well as nonlinear model predictive control strategies.
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