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Research Papers

Model-Based Probabilistic Robust Design With Data-Based Uncertainty Compensation for Partially Unknown System

[+] Author and Article Information
XinJiang Lu

 State Key Laboratory of High Performance Complex Manufacturing,Central South University, Hunan 410083, China

Han-Xiong Li1

Department of Systems Engineering and Engineering Management, City University of Hong Kong,Hong Kong;  State Key Laboratory of High Performance Complex Manufacturing,Central South University, Hunan 410083, China

C. L. Philip Chen

Faculty of Science and Technology,  University of Macau, Av. Padre Tomás Pereira, Taipa, Macau, China

1

Corresponding author.

J. Mech. Des 134(2), 021004 (Feb 03, 2012) (8 pages) doi:10.1115/1.4005589 History: Received June 26, 2010; Revised October 27, 2011; Published February 03, 2012

Model uncertainty often results from incomplete system knowledge or simplification made at the design stage. In this paper, a hybrid model/data-based probabilistic design approach is proposed to design a nonlinear system to be robust under the circumstances of parameter variation and model uncertainty. First, the system is formulated under a linear structure which will serve as a nominal model of the system. All model uncertainties and nonlinearities will be placed under a sensitivity matrix with its bound estimated from process data. On this basis, a model-based robust design method is developed to minimize the influence of parameter variation in relation to performance covariance. Since this proposed design approach possesses both merits from the model-based robust design as well as from the data-based uncertainty compensation, it can effectively achieve robustness for partially unknown nonlinear systems. Finally, two practical examples demonstrate and confirm the effectiveness of the proposed method.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Traditional probabilistic robust design for partially unknown nonlinear system

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Figure 2

Hybrid model/data-based robust design methodology

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Figure 3

Meaning of perturbation sensitivity matrix

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Figure 4

Diagram of perturbation sensitivity matrix

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Figure 5

Configuration of the proposed robust design method

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Figure 6

A pneumatic cylinder

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Figure 7

(a) Data-based estimation for ΔJ1 . (b) Data-based estimation for ΔJ2 .

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Figure 8

Bound modeling for the performance variation ΔY

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Figure 9

Comparison under the parameter variations and the model uncertainty for example 1

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Figure 10

A low-pass filter

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Figure 11

Bound modeling for the performance variations: (a) ΔY1 and (b) ΔY2

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Figure 12

Comparison under the parameter variations and the model uncertainty for example 2

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