Nonlinear Process Control
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Nonlinear Process Control
This Book Is Intended For Researchers In Process Control And Applied Mathematics. It Can Also Serve As A Textbook For Graduate Students Interested In Nonlinear Control Theory.After Discussing The Basic Design Method Of Model Reference Nonlinear Controller (Mrnc), The Book Deals With The Incorporation Of Explicit Integral And Derivative Actions In The Control Law. Extension Of The Method To Systems With Relative Order Two And Higher Is Provided. The Design Of Series Cascade Mrnc Systems And Parallel Cascade Mrnc Systems Are Given.Extensions Of Mrnc For Systems With Significant Measurement Dynamics Or Actuator Dynamics Are Made. The Design Method Of Mrnc For Systems With Delay In Measurement Or In Actuator Is Provided.Simulation Studies On Several Nonlinear Processes Prove The Effectiveness Of The Mrnc.
Nonlinear Process Control
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. 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.
Nonlinear Model Based Process Control
Author: Rıdvan Berber
language: en
Publisher: Springer Science & Business Media
Release Date: 1998
The increasingly competitive environment within which modern industry has to work means that processes have to be operated over a wider range of conditions in order to meet constantly changing performance targets. Add to this the fact that many industrial operations are nonlinear, and the need for on-line control algorithms for nonlinear processes becomes clear. Major progress has been booked in constrained model-based control and important issues of nonlinear process control have been solved. This text surveys the state-of-the-art in nonlinear model-based control technology, by writers who have actually created the scientific profile. A broad range of issues are covered in depth, from traditional nonlinear approaches to nonlinear model predictive control, from nonlinear process identification and state estimation to control-integrated design. Advances in the control of inverse response and unstable processes are presented. Comparisons with linear control are given, and case studies are used for illustration.