Experimental verification of constrained iterative learning control using successive projection

2012 ◽  
Author(s):  
Bing Chu ◽  
Zhonglun Cai ◽  
David H Owens ◽  
Eric Rogers ◽  
Chris T Freeman ◽  
...  
Keyword(s):  
Experimental Verification ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Successive Projection

Optimal Design of a Hybrid-Driven Servo Press and Experimental Verification

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
Pei-Lum Tso
Keyword(s):  
Control System ◽  
Optimal Design ◽  
Energy Saving ◽  
Experimental Verification ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Servo Press ◽  
Ac Motor ◽  
Control Theories

This paper focuses on a hybrid-driven servo press, which uses not only a servomotor but also a regular ac motor with a flywheel. A PC-based control system is developed based on both feedback and iterative learning control theories on a prototype. The stamping performance, improved forming ability, and energy saving merits have been verified by the experiment. The results show that the advantages of the hybrid-driven servo press have been validated, such as its energy saving features, flexible punch speeds, and adjustable strokes, for any kind of stamping operations.

Discrete Fourier Transform Based Iterative Learning Control Design for Linear Plants With Experimental Verification

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
C. T. Freeman ◽  
P. L. Lewin ◽  
E. Rogers ◽  
D. H. Owens ◽  
J. J. Hatonen
Keyword(s):  
Fourier Transform ◽  
Discrete Fourier Transform ◽  
Experimental Verification ◽  
Iterative Learning Control ◽  
Design Method ◽  
Control Design ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Algorithms ◽  
Straightforward Manner

This paper considers the design of linear iterative learning control algorithms using the discrete Fourier transform of the measured impulse response of the system or plant under consideration. It is shown that this approach leads to a transparent design method whose performance is then experimentally benchmarked on an electromechanical system. The extension of this approach to the case when there is uncertainty associated with the systems under consideration is also addressed in both algorithm development and experimental benchmarking terms. The robustness results here have the applications oriented benefit of allowing the designer to manipulate the convergence and robustness properties of the algorithm in a straightforward manner.

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