Design Methods Of Control Systems
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Design of Modern Control Systems
The book reviews developments in the following fields: state-space theory; complex variable methods in feedback system analysis and design; robustness in variable control system design; design study using the characteristic locus method; inverse Nyquist array design method; nuclear boiler control scheme analysis and design; optimal control; control system design via mathematical programming; multivariable design optimisation; pole assignment; nonlinear systems; DDC system design; robust controller design; distributed parameter system control; and decentralised control.
Control Systems Design
Author: Vladimir Zakian
language: en
Publisher: Springer Science & Business Media
Release Date: 2005-12-27
In recent decades, a comprehensive new framework for the theory and design of control systems has emerged. It treats a range of significant and ubiquitous design problems more effectively than the conventional framework. Control Systems Design brings together contributions from the originators of the new framework in which they explain, expand and revise their research work. It is divided into four parts: - basic principles, including those of matching and inequalities with adjustments for robust matching and matching based on H-infinity methods and linear matrix inequalities; - computational methods, including matching conditions for transient inputs and design of a sampled-data control system; - search methods including search with simulated annealing, genetic algorithms and evaluation of the node array method; - case studies, including applications in distillation, benchmarking critical control of magnetic levitation systems and the use of the principle of matching in cruise control.
Applied Control Systems Design
Author: Magdi S. Mahmoud
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-04-13
Applied Control System Design examines several methods for building up systems models based on real experimental data from typical industrial processes and incorporating system identification techniques. The text takes a comparative approach to the models derived in this way judging their suitability for use in different systems and under different operational circumstances. A broad spectrum of control methods including various forms of filtering, feedback and feedforward control is applied to the models and the guidelines derived from the closed-loop responses are then composed into a concrete self-tested recipe to serve as a check-list for industrial engineers or control designers. System identification and control design are given equal weight in model derivation and testing to reflect their equality of importance in the proper design and optimization of high-performance control systems. Readers’ assimilation of the material discussed is assisted by the provision of problems and examples. Most of these exercises use MATLAB® to make computation and visualization more straightforward. Applied Control System Design will be of interest to academic researchers for its comparison of different systems models and their response to different control methods and will assist graduate students in learning the practical necessities of advanced control system design. The consistent reference to real systems coupled with self-learning tools will assist control practitioners who wish to keep up to date with the latest control design ideas.