Adaptive Neuro-Fuzzy vibration control of a smart plate

Muradova, Aliki D.; Tairidis, Georgios K.; Stavroulakis, Georgios Ε.

doi:10.3934/naco.2017017

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…However, in this paper, we are concerned with the case that p is a vector-valued interval number. There are some numerical methods for interval and fuzzy computing and optimization problems in the open literature such as those in [17,3,14,43,1,29,31,20]. However, most of these methods have been developed for general interval/fuzzy optimization problems and do not take into consideration of special properties the coefficient of (3)-(4) possess such as that the system matrix A may be positive-definite, sparse M -matrix.…”

Section: Song Wangmentioning

confidence: 99%

Numerical solution of an obstacle problem with interval coefficients

Wang¹

2020

Numerical Algebra, Control &Amp; Optimization

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In this work we propose a novel numerical method for a finitedimensional optimization problem arising from the discretization of an infinitedimensional constrained optimization problem, called an obstacle problem, with interval coefficients. In this method, the two different ways of characterizing the optimal solutions, i.e., minimizing the mid-point and one end-point (the worst-case scenario) or the mid-point and the width of the objective interval, are formulated as a single constrained multi-objective minimization problem and the KKT conditions of the optimization problem defining the Pareto optimal solution to the multi-objective problem are of the form of a Linear Complementarity Problem (LCP) which is shown to have a unique solution. The LCP is the approximated by a non-linear equation using an interior penalty approach. We prove that the penalty equation is uniquely solvable and its solution converges to that of LCP as the penalty constant approaches to zero. Numerical results are presented to demonstrate the usefulness of the numerical method proposed.

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Section: Song Wangmentioning

confidence: 99%

Numerical solution of an obstacle problem with interval coefficients

Wang¹

2020

Numerical Algebra, Control &Amp; Optimization

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“…Neuro-fuzzy systems are widely used in several fields, such as structural control, robotics, mechatronics, etc. In previous investigations, several schemes for vibration reduction of smart structures were developed and tested based on the principles of active control (Stavroulakis et al., 2011; Muradova et al., 2017). In the present investigation, a neuro-fuzzy controller is developed for the activation of suitably defined, that is, preset resonant shunt circuits, exploiting in this way the capabilities of semi-active control.…”

Section: Neuro-fuzzy Controlmentioning

confidence: 99%

Vibration control of smart composite structures using shunted piezoelectric systems and neuro-fuzzy techniques

Tairidis

2019

Journal of Vibration and Control

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Shunt piezoelectric circuits can be used in several combinations for passive control of smart structures. Resonant shunt circuits with resistors and inductors can control resonant frequencies, by consuming the energy produced from vibrations by passing it to electric components. Such systems are very efficient for single-mode problems; however, when it comes to multi-mode control, their performance drastically deteriorates. The purpose of the present study is the development of optimized resonant shunt piezoelectric circuits, along with an intelligent control system based on adaptive neuro-fuzzy techniques, for vibration suppression of smart composite structures. Shunt circuits are pre-tuned to the first four eigenfrequencies and a neuro-fuzzy control system is developed and used for the activation of the suitable shunt circuit, each time, providing the necessary adaptivity to the whole system.

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“…Finally, the system optimizes, i.e., corrects/adapts, the rules and all the characteristics of the Sugeno controller. The details of the procedure can be found in the article of Muradova et al (2017).…”

Section: Neuro-fuzzy Control Schemementioning

confidence: 99%

“…The computational schemes using fuzzy inference systems of Mamdani-type have been proposed before for smart beams by Tairidis et al (2009) and for plates by Stavroulakis (2013, 2015), respectively. Adaptive neuro-fuzzy control has been used for suppression of vibrations of smart structures like beams and plates by Stavroulakis et al (2011) and Muradova et al (2017). Some other optimization techniques for the enhancement of the characteristics of fuzzy control, include, among others, genetic algorithms and particle swarm optimization as have been described in the works of Tairidis et al (2016) and Marinaki et al (2010), respectively.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Morphing of Smart Trusses and Mechanisms Using Fuzzy and Neuro-Fuzzy Techniques

Tairidis

Muradova

Stavroulakis

2019

Front. Built Environ.

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In the present investigation, the principles of dynamic morphing of smart truss structures and mechanisms are discussed. A possible way in order to find the optimal geometry of the structure for the enhancement of structural performance in terms of vibration control is sought. The vibrations of the host dynamic structures are monitored by controllers which are based on the principles of Mamdani-type fuzzy inference and Sugeno-type adaptive neuro-fuzzy inference. More specifically, the objective of the present study is a design, tuning, and an application of robust intelligent control mechanisms by means of the suppression of structural vibrations for several types of excitation forces. The proposed models are discretized by using a finite element method. For the time integration of the equations of motion, the Newmark-β method is used. The calculations and the analysis are conducted within the Matlab environment by using the Adaptive Neuro-Fuzzy Inference System (ANFIS) tool, which is included in the fuzzy toolbox. The controllers are tested with different excitation forces applied on a truss-shaped structure. The control outputs are applied on each time of the simulation in order to achieve the lowest possible deformation and to prevent potential damage or corruption of the structure. The same principles are used for the dynamic morphing of structures and mechanisms. The proposed formulation can be applied, among many others, on smart irrigation systems such as spray booms, on radio-telescope bases, on the spars of smart wings, on aircraft wings etc.

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Adaptive Neuro-Fuzzy vibration control of a smart plate

Cited by 7 publications

References 19 publications

Numerical solution of an obstacle problem with interval coefficients

Numerical solution of an obstacle problem with interval coefficients

Vibration control of smart composite structures using shunted piezoelectric systems and neuro-fuzzy techniques

Dynamic Morphing of Smart Trusses and Mechanisms Using Fuzzy and Neuro-Fuzzy Techniques

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