Membrane structure active flatness control using genetic algorithm with online objective reweighting

Orszulik, Ryan R.; Shan, Jinjun; Stachowsky, Michael

doi:10.1016/j.actaastro.2010.11.009

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…In the both studies it is also possible that best set of parameters exist because the simplex algorithm can converge toward a local minimum of the RMSE function. To avoid this matter, the use of a genetic algorithm (such as proposed in [25] to study a similar problem) may improve the procedure.…”

Section: Inflatable Reflector Optimizationmentioning

confidence: 99%

A numerical method to optimize the design of a space inflatable membrane reflector

Bouzidi

Lecieux

2012

Acta Astronautica

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International audienceA numerical method is proposed to optimize the design of a space inflatable membrane reflector. The initial geometry is expressed by polynomial series weighted by a set of shape parameters. The problem is formulated as a minimization of a cost function representing the difference between the effective shape of the reflector and a perfect parabolic surface. The minimization is performed using the Nelder-Mead method or downhill simplex method. The cost function is computed at each vertex of a simplex defined in the space of optimization parameters by solving direct problem thanks to a finite element method. The finite element model handles geometrical non-linearities and takes into account phenomena like membrane wrinkling and torus buckling which may affect the reflector shape when inflated

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Section: Inflatable Reflector Optimizationmentioning

confidence: 99%

A numerical method to optimize the design of a space inflatable membrane reflector

Bouzidi

Lecieux

2012

Acta Astronautica

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“…The adaptive control method was also used to constitute a flatness adaptive control system and was able to adapt to the needs of different flatness control . Zhang et al combined the neural network, fuzzy control with the cloud inference model to construct a new type of flatness control system, which improved the control precision, and later added the genetic algorithm (GA) into this system, overcoming the defect of the traditional method, which is easy to fall into local minimum . However, the neural network is difficult to determine the number of hidden layers and poor global optimization.…”

Section: Introductionmentioning

confidence: 99%

Optimization design of RBF‐ARX model and application research on flatness control system

Zhang

Cheng

Hao

et al. 2016

Optim Control Appl Methods

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The radial basis function (RBF) network and autoregressive exogenous (ARX) model are combined to form the structure of the RBF-ARX model. The RBF-ARX model can describe the global nonlinear dynamic process of the object, and its function coefficients are approximated by data-driven method. The structured nonlinear parameters optimization method (SNPOM) is generally used to optimize model parameters, but this method is very complicated and hard to be mastered by engineers. However, genetic algorithm (GA) is simple and widely used. So the thought of GA optimizing RBF-ARX is generated, called GA-ARX-RBF, and applied to nonlinear dynamic flatness control system. In this article, the recursive least squares method to optimize linear weights of RBF is also used to improve the SNPOM, which reduces the complexity and storage capacity of data processing. Meanwhile, GA to optimize all the parameters of the RBF-ARX model replaces SNPOM completely. A GA-RBF-ARX modeling and optimizing method is proposed. In order to prove the efficiency of GA-RBF-ARX, it is applied into flatness control system, which has the characters of nonlinear, multivariable, and multi-disturbance. The flatness recognition model and flatness predictive model are established. A predictive controller based on GA-RBF-ARX is designed for 900HC reversible cold rolling mill. The simulation results demonstrate that the flatness control system based on GA-RBF-ARX is effective and has a better precision. The method is easily mastered by engineers and helps to promote the practical value of RBF-ARX.

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“…Recently, the various non contact measurement systems capable of measuring the characteristics of membrane space structures have been reviewed 7) . And, the photogrammetry measurements have also been applied to study the effectiveness of the active flatness control of the membranes structure 8) .…”

Section: Introductionmentioning

confidence: 99%

Visualization on Formation Process of Wrinkling Phenomena on Thin Membranes Using Grating Projection Method and Investigation on Their Nonlinear Mechanical Properties

Kawamura

Iwasa

Unemitsu³

et al. 2012

AEROSPACE TECHNOLOGY JAPAN

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A non contact whole field shape measurement using the grating projection method was performed to visualize formation and dissipation processes of the wrinkling phenomena on thin membranes, and their nonlinear mechanical properties were discussed. The wrinkling phenomena in a rectangular polyimide film, which held fixed at the bottom side and subjected to loadings at the upper side, was treated. The measurement results showed that the nonlinear wrinkling behavior such as the snap through phenomena appeared in the formation and dissipation processes of wrinkles was successfully captured. Through the discussion on the dissipation processes of wrinkling phenomena, the conditions of the applied tensions to reduce the distortion of the membrane surface feature are presented.

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Membrane structure active flatness control using genetic algorithm with online objective reweighting

Cited by 14 publications

References 22 publications

A numerical method to optimize the design of a space inflatable membrane reflector

A numerical method to optimize the design of a space inflatable membrane reflector

Optimization design of RBF‐ARX model and application research on flatness control system

Visualization on Formation Process of Wrinkling Phenomena on Thin Membranes Using Grating Projection Method and Investigation on Their Nonlinear Mechanical Properties

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