Real-Time Identification of Fuzzy PID-Controlled Maglev System using TLBO-Based Functional Link Artificial Neural Network

Sahoo, Amit Kumar; Mishra, Sudhansu Kumar; Majhi, Babita; Panda, Ganapati; Satapathy, Suresh Chandra

doi:10.1007/s13369-020-05292-x

Cited by 10 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results reveal that the sur-face defects around the slit are the least under the optimized working conditions of TLBO. In the work [20], a functional link artificial neural network based TLBO is proposed for real-time identification of fuzzy PID-control magnetic levitation system. In the research [21] ters [26].…”

Section: Related Workmentioning

confidence: 99%

Strengthened teaching–learning-based optimization algorithm for numerical optimization tasks

Chen

Zhang

et al. 2023

Evol. Intel.

View full text Add to dashboard Cite

The teaching-learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching-learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO's exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks. The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article.

show abstract

Section: Related Workmentioning

confidence: 99%

Strengthened teaching–learning-based optimization algorithm for numerical optimization tasks

Chen

Zhang

et al. 2023

Evol. Intel.

View full text Add to dashboard Cite

show abstract

“…A functional link artificial neural network (FLANN-TLBO) model which improves the detection and running of the Maglev system through teaching-learning-based optimization proposed by [40], the outcomes show that the actual system and the detected model have better response matching. Furthermore, the statistical experiments confirm the FLANN-TLBO network's superiority to other networks.…”

Section: Magnetic Levitationmentioning

confidence: 99%

A Non-Contact Manipulation for Robotic Applications: A Review on Acoustic Levitation

2022

View full text Add to dashboard Cite

Robots play an important role all over the world in the industrial field in terms of dealing with various materials, performing duties quickly and high accuracy as well as in the pharmaceutical/chemical field since robots can handle some issues better than human. Robotic manipulation is the term used to describe how robots deal with many objects. This article reviews the contact and non-contact manipulation with their most common robotic applications and focusing on the non-contact manipulation since it can do specific tasks which contact manipulation cannot do. Reviewing of the robotic non-contact manipulation which include the metal-based robotic manipulation such as magnetic and electrostatic levitation and the non-metal-based robotic manipulation method such as aerodynamic and acoustic levitation have been made in this article. The main core of this article is acoustic levitation which considered one of the most important levitation and manipulation method due to the recent advancements that made it proper to deal with any kind of materials. Here, we review the renowned three methods of acoustic levitation that already using in robotic non-contact manipulation which are standing wave, near-field and single-beam acoustic levitation. Then, we present a performance characteristic of the reviewed methods and the summary of controlling strategies, followed by conclusion and future recommendations for acoustic levitation methods as a non-contact manipulation.

show abstract

Section: Related Workmentioning

confidence: 99%

Strengthened Teaching–Learning-Based Optimization Algorithm for Numerical Optimization Tasks

Chen

Zhang

et al. 2021

Preprint

View full text Add to dashboard Cite

The teaching–learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching–learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO’s exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks. The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article.

show abstract

Real-Time Identification of Fuzzy PID-Controlled Maglev System using TLBO-Based Functional Link Artificial Neural Network

Cited by 10 publications

References 42 publications

Strengthened teaching–learning-based optimization algorithm for numerical optimization tasks

Strengthened teaching–learning-based optimization algorithm for numerical optimization tasks

A Non-Contact Manipulation for Robotic Applications: A Review on Acoustic Levitation

Strengthened Teaching–Learning-Based Optimization Algorithm for Numerical Optimization Tasks

Contact Info

Product

Resources

About