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Industrial Process Identification and Control Design

by Liu, Tao.
Authors: Gao, Furong.%author. | SpringerLink (Online service) Series: Advances in Industrial Control, 1430-9491 Physical details: XXVI, 474 p. online resource. ISBN: 0857299778 Subject(s): Engineering. | Chemical engineering. | Industrial engineering. | Engineering. | Control. | Industrial Chemistry/Chemical Engineering. | Industrial and Production Engineering.
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E-Book E-Book AUM Main Library 629.8 (Browse Shelf) Not for loan

Part I: Process Identification -- Introduction -- Step Response Identification of Stable Processes -- Step Response Identification of Integrating Processes -- Relay Feedback Identification of Stable Processes -- Relay Feedback Identification of Integrating Processes -- Relay Feedback Identification of Unstable Processes -- Part II: Control System Design -- Control of Single-input-single-output (SISO) Processes -- Two-degree-of-freedom (2DOF) Control of SISO Processes -- Cascade Control System -- Decoupling Control of Multiple-input-multiple-output (MIMO) Processes -- Multiloop/Decentralized Control Systems -- Batch Process Control -- Conclusions and Future Developments.

  Industrial Process Identification and Control Design is devoted to advanced identification and control methods for the operation of continuous-time processes both with and without time delay, in industrial and chemical engineering practice.   The simple and practical step- or relay-feedback test is employed when applying the proposed identification techniques, which are classified in terms of common industrial process type: open-loop stable; integrating; and unstable, respectively. Correspondingly, control system design and tuning models that follow are presented for single-input-single-output processes.   Furthermore, new two-degree-of-freedom control strategies and cascade control system design methods are explored with reference to independently-improving, set-point tracking and load disturbance rejection. Decoupling, multi-loop, and decentralized control techniques for the operation of multiple-input-multiple-output processes are also detailed. Perfect tracking of a desire output trajectory is realized using iterative learning control in uncertain industrial batch processes.   All the proposed methods are presented in an easy-to-follow style, illustrated by examples and practical applications. This book will be valuable for researchers in system identification and control theory, and will also be of interest to graduate control students from process, chemical, and electrical engineering backgrounds and to practising control engineers in the process industry.  

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