scholarly journals Robust iterative learning control: theory and experimental verification

2008 ◽  
Vol 18 (10) ◽  
pp. 999-1000 ◽  
Author(s):  
D. H. Owens ◽  
E. Rogers
Keyword(s):  
Control Theory ◽  
Experimental Verification ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning

Design of arbitrary-order robust iterative learning control based on robust control theory

Mechatronics ◽  
2017 ◽  
Vol 47 ◽  
pp. 67-76 ◽  
Author(s):  
Minghui Zheng ◽  
Cong Wang ◽  
Liting Sun ◽  
Masayoshi Tomizuka
Keyword(s):  
Robust Control ◽  
Control Theory ◽  
Iterative Learning Control ◽  
Arbitrary Order ◽  
Learning Control ◽  
Iterative Learning ◽  
Robust Control Theory

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.

Synthesis of Four-Bar Function Generators by an Iterative Learning Control Procedure

1996 ◽  
Author(s):  
Minh Q. Phan ◽  
Meng-Sang Chew
Keyword(s):  
Control Theory ◽  
Control Problem ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Procedure ◽  
Output Function ◽  
Mechanism Synthesis ◽  
Function Generator ◽  
Cost Functionals

Abstract This paper investigates the applicability of learning control theory to mechanism synthesis via the classical four-bar function generator problem. A function to be generated by a mechanism can be looked upon as a trajectory to be tracked. The parameters that define the mechanism can be thought of as the control inputs. In this sense, the problem of synthesizing a mechanism to generate a particular output function can be treated as a “control” problem. Moreover, it is a learning control problem if the mechanism is synthesized by an iterative process. At each trial or iteration, a learning scheme modifies the mechanism dimensions based on how well it generates the desired function in the previous trial so that the synthesized mechanism approximates the desired output function more and more closely. With this thinking, concepts and tools from learning control theory can be adapted to the mechanism synthesis problem. It will be shown that mechanisms with minimum residual error or minimum structural error can be synthesized by a procedure analogous to that derived for iterative learning control. The starting angles of the input and output links are learned together with the mechanism dimensions. By the use of weighted cost functionals, iterative learning schemes that handle the tradeoff between the emphasis on a certain portion of the output trajectory (e.g., local control) and the mechanism dimensions can be derived in a straight forward manner. Numerical examples are used to illustrate the utility and flexibility of the learning formulation.

Robust Iterative Learning Control for Spatially Interconnected Systems using 2D Control Theory*

2021 ◽  
Author(s):  
Bartlomiej Sulikowski ◽  
Krzysztof Galkowski ◽  
Daniel Trzcinski ◽  
Eric Rogers ◽  
Anton Kummert
Keyword(s):  
Control Theory ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Interconnected Systems ◽  
Spatially Interconnected Systems
Sign in / Sign up

Export Citation Format

Share Document