Real-Time Iterative Learning Control

Real-Time Iterative Learning Control
-0 %
Design and Applications
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Artikel-Nr:
9781848821743
Veröffentl:
2008
Erscheinungsdatum:
23.12.2008
Seiten:
194
Autor:
Jian-Xin Xu
Gewicht:
459 g
Format:
243x164x22 mm
Sprache:
Englisch
Beschreibung:

Real-time Iterative Learning Control demonstrates how the latest advances in iterative learning control (ILC) can be applied to a number of plants widely encountered in practice. The book gives a systematic introduction to real-time ILC design and source of illustrative case studies for ILC problem solving; the fundamental concepts, schematics, configurations and generic guidelines for ILC design and implementation are enhanced by a well-selected group of representative, simple and easy-to-learn example applications. Key issues in ILC design and implementation in linear and nonlinear plants pervading mechatronics and batch processes are addressed, in particular: ILC design in the continuous- and discrete-time domains; design in the frequency and time domains; design with problem-specific performance objectives including robustness and optimality; design in a modular approach by integration with other control techniques; and design by means of classical tools based on Bode plots and state space.

Provides the industry-based control engineer with a practical reference for designing and implementing iterative learning control systems in their own plants based on many practical examples
to ILC: Concepts, Schematics, and Implementation.- Robust Optimal ILC Design for Precision Servo: Application to an XY Table.- ILC for Precision Servo with Input Non-linearities: Application to a Piezo Actuator.- ILC for Process Temperature Control: Application to a Water-heating Plant.- ILC with Robust Smith Compensator: Application to a Furnace Reactor.- Plug-in ILC Design for Electrical Drives: Application to a PM Synchronous Motor.- ILC for Electrical Drives: Application to a Switched Reluctance Motor.- Optimal Tuning of PID Controllers Using Iterative Learning Approach.- Calibration of Micro-robot Inverse Kinematics Using Iterative Learning Approach.- Conclusion.

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