Optimization of Multibody Dynamic Systems using Meta-Model Based Robust Design Optimization
This research introduced meta-models based Design For Six Sigma (DFSS) and robust design optimization (RDO) for dynamic performance optimization problems, which could eliminate the design sensitivity analysis (DSA) and deal with numerical noises. The optimization methodologies could be executed effortlessly based on the gradient information of meta-models, robust/DFSS optimization combined with meta-models was therefore proposed to optimize shape of mechanical structures and gain coefficients of proportional-integral-derivative (PID) controller for multi-body systems (MBS). Meta-model based on simultaneous Kriging method was first presented to construct approximate models for optimization design objectives. Then, robust/DFSS design optimization formulation was also described in this paper. Two numerical examples on shape optimization with a flexible cantilever beam model and the gain value optimization of PID control algorithm with an inverted-pendulum model were performed to demonstrate the proposed optimization method. The former example described structural optimization of a flexible body under dynamic loading conditions within an MBS approach. In the latter example, the optimal values of P, I, D gains were obtained by using co-simulation between multibody dynamic (MBD) analysis and control environment. Therefore, this study achieved structural and control design improvement in different systems with the optimization theories.
KeywordsKriging Method Meta-model Numerical Optimization
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- 1.Wu, C. F. J., Hamada, M. S.: Experiments: Planning, analysis, and optimization. 2nd edn, Wiley, United States (2009).Google Scholar
- 2.Montgomery, D. C: Design and analysis of experiments. 8th edn. John Wiley & Sons, Asia (2012).Google Scholar
- 6.Jin, R., Chen, W., Simpson, T. W.: Comparative studies of metamodeling techniques under multiple modeling criteria. Structure Multidisciplinary Optimization 23(1), 1-13 (2001).Google Scholar
- 7.Vanderplaats, G. N.: Numerical optimization techniques for engineering design. 1st edn, McGraw-Hill, New York (1984).Google Scholar
- 8.Fletcher, R.: Practical method of optimization. 2nd edn. John Wiley & Sons, Chichester (1987).Google Scholar
- 9.RecurDyn/ Solver Theoretical Manual, Functionbay, Korea, (2010).Google Scholar
- 11.Muthukumaran, V., Rajmurugan, R., Ramkumar, V. K.: Cantilever beam and torsion rod design optimization using genetic algorithm. International Journal of Innovative Research in Science, Engineering and Technology 3(3), 2682-2689 (2014).Google Scholar