Fine tuning of a fuzzy controller for vibration suppression of smart plates using genetic algorithms

Tairidis, Georgios K.; Foutsitzi, G.; Koutsianitis, Panagiotis; Stavroulakis, Georgios Ε.

doi:10.1016/j.advengsoft.2016.01.019

Cited by 34 publications

(24 citation statements)

References 14 publications

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“…Equations (6) are the final equations of motion and can be used in smart structures applications such as vibration control.…”

Section: Finite Element Formulationmentioning

confidence: 99%

“…An introductory survey of fuzzy control has been conducted in [5]. Indeed, to maximize the efficiency of the proposed controllers, their characteristics can be subjected to a fine-tuning process either using global optimization algorithms [6,7] or artificial neural networks [2,3]. Artificial neural networks provide better solutions to some problems due to their capability of parallelism and learning.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Neuro Fuzzy Control for Vibration Suppression of a Smart Built-in Plate With Enbeded Piezoelectric Sensors and Actuators

Tairidis¹,

Stavroulakis²

2017

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…Equations (6) are the final equations of motion and can be used in smart structures applications such as vibration control.…”

Section: Finite Element Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro Fuzzy Control for Vibration Suppression of a Smart Built-in Plate With Enbeded Piezoelectric Sensors and Actuators

Tairidis¹,

Stavroulakis²

2017

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…Some of these approaches to the parameter tuning of fuzzy control systems are presented here. Tairidis et al utilized the genetic algorithm (GA) for optimization of the FLC in the vibration suppression of smart structures. Qian et al developed a fuzzy controller for transport control of double‐pendulum‐type systems and used GA for tuning of the FLC parameters.…”

Section: Introductionmentioning

confidence: 99%

Optimal tuning of fuzzy parameters for structural motion control using multiverse optimizer

Azizi

Ghasemi

Ejlali

et al. 2019

Structural Design Tall Build

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Summary In recent years, there is an increasing interest in optimization of structural control algorithms. Fuzzy logic controller is one of the most common and versatile control algorithms that is generally formulated based on the human knowledge and expert. Human knowledge and experience do not yield optimal control responses for a given structure, and tuning of the fuzzy parameters is necessary. This paper focuses on the optimization of a fuzzy controller applied to a seismically excited nonlinear building. In the majority of cases, this problem is formulated based on the linear behavior of the structure; however, in this paper, objective functions and the evaluation criteria are considered with respect to the nonlinear responses of the structures. Multiverse optimizer is a novel nature‐inspired optimization algorithm that is based on the three concepts of cosmology as white hole, black hole, and wormhole. This algorithm has fast convergence rate and can be utilized in continuous and discrete optimization problems. In this paper, the multiverse optimizer is considered as the optimization algorithm for optimization of the fuzzy controller. The performance of the selected algorithm is compared with eight different optimization algorithms. The results prove that the selected algorithm is able to provide very competitive results.

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“…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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Fine tuning of a fuzzy controller for vibration suppression of smart plates using genetic algorithms

Cited by 34 publications

References 14 publications

Adaptive Neuro Fuzzy Control for Vibration Suppression of a Smart Built-in Plate With Enbeded Piezoelectric Sensors and Actuators

Adaptive Neuro Fuzzy Control for Vibration Suppression of a Smart Built-in Plate With Enbeded Piezoelectric Sensors and Actuators

Optimal tuning of fuzzy parameters for structural motion control using multiverse optimizer

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

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