Special Issue "New Trends in Intelligent Control and Filter Design"

A special issue of Designs (ISSN 2411-9660).

Deadline for manuscript submissions: 15 December 2018

Special Issue Editors

Guest Editor
Prof. Dr. Choon Ki Ahn

School of Electrical Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 136-701 Korea
Website | E-Mail
Interests: controller and filter design; intelligent system design; machine learning; drone design; autonomous vehicle design; multi-sensor fusion; wireless sensor network; networked control and estimation; information fusion; cyber-physical system
Guest Editor
Prof. Dr. Xiaojie Su

College of Automation, Chongqing University, Chongqing 400044, China
Website | E-Mail
Interests: Takagi-Sugeno Fuzzy systems; time-delay systems; signal processing; multi-objective control and optimization techniques; model reduction
Guest Editor
Prof. Dr. Fanbiao Li

School of Information Science and Engineering, Central South University, Changsha, 410083, China
Website | E-Mail
Interests: hybrid dynamical systems; sliding mode control; fault detection; filtering

Special Issue Information

Dear Colleagues,

Intelligent techniques, including neural networks, fuzzy systems, and genetic algorithms, have been successfully applied to controller and filter design methods for numerous complex systems with uncertainties and disturbances. Recent advances in intelligent controllers and filters have introduced new approaches to the fundamental problems in how they can deal with practical constraints, such as network-induced delay and quantization in industrial applications. Both hardware and software development environments for intelligent controllers and filters have also been considered extensively.

The main aim of this Special Issue is to provide a forum for researchers to share their recent results and to discuss several issues for the research directions and advanced controller and filter design methods based on intelligent techniques in the field. The papers to be published in this Special Issue are expected to provide recent results in advanced design methods especially for cross-fertilizations between the broad fields of intelligent controllers and filters.

Prof. Dr. Choon Ki Ahn
Prof. Dr. Xiaojie Su
Prof. Dr. Fanbiao Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Designs is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Intelligent modeling and identification for complex systems
  • Intelligent control and filtering methods for network-induced constraints
  • Intelligent optimization techniques and applications to control and filtering
  • Data-driven intelligent modeling, control, and filtering techniques
  • Intelligent fault detection, diagnosis, and monitoring system design
  • Deep learning approaches and big data solutions for controller and filter design
  • Novel fuzzy logic and neural network structures for controller and filter design
  • Simulation models and technologies for controller and filter design
  • Industrial applications and case studies

Published Papers (2 papers)

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Research

Open AccessArticle An Underactuated Bio-Inspired Helical Swimming Microrobot Using Fuzzy-PI Controller with Novel Error Detection Method for 5-DOF Micromanipulation
Received: 8 April 2018 / Revised: 6 June 2018 / Accepted: 8 June 2018 / Published: 14 June 2018
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Abstract
The potential of microrobots to bring about revolutionary changes over micro-operation demands is increasing day to day. This paper presents a controller to provide 5 degrees of freedom for an underactuated bio-inspired helical swimming microrobot. The considered system is a helical swimming microrobot
[...] Read more.
The potential of microrobots to bring about revolutionary changes over micro-operation demands is increasing day to day. This paper presents a controller to provide 5 degrees of freedom for an underactuated bio-inspired helical swimming microrobot. The considered system is a helical swimming microrobot with three flagella in a low Reynolds performance environment. Control of the considered system is performed to reach any desired location, roll angle and pitch angle. The proposed controlling error definition extracted from the system geometry is general for similar actuation configurations. An error detection method for multi-propulsion-unit systems is utilized for 5-DOF micromanipulation of an underactuated bio-inspired helical swimming microrobot by fuzzy-PI controller. A fuzzy-PI controller is proposed to use modified experimental data of PI controller debugging to maintain a suitable efficient control. The comparison of two other possible controllers and the proposed fuzzy-PI controller is discussed, and the performance of trajectory tracking is evaluated by simulations. Full article
(This article belongs to the Special Issue New Trends in Intelligent Control and Filter Design)
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Open AccessArticle Classification via an Embedded Approach
Received: 5 August 2017 / Revised: 6 September 2017 / Accepted: 7 September 2017 / Published: 15 September 2017
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Abstract
This paper presents the results of an automated volatile organic compound (VOC) classification process implemented by embedding a machine learning algorithm into an Arduino Uno board. An electronic nose prototype is constructed to detect VOCs from three different fruits. The electronic nose is
[...] Read more.
This paper presents the results of an automated volatile organic compound (VOC) classification process implemented by embedding a machine learning algorithm into an Arduino Uno board. An electronic nose prototype is constructed to detect VOCs from three different fruits. The electronic nose is constructed using an array of five tin dioxide (SnO2) gas sensors, an Arduino Uno board used as a data acquisition section, as well as an intelligent classification module by embedding an approach function which receives data signals from the electronic nose. For the intelligent classification module, a training algorithm is also implemented to create the base of a portable, automated, fast-response, and economical electronic nose device. This solution proposes a portable system to identify and classify VOCs without using a personal computer (PC). Results show an acceptable precision for the embedded approach in comparison with the performance of a toolbox used in a PC. This constitutes an embedded solution able to recognize VOCs in a reliable way to create application products for a wide variety of industries, which are able to classify data acquired by an electronic nose, as VOCs. With this proposed and implemented algorithm, a precision of 99% for classification was achieved into the embedded solution. Full article
(This article belongs to the Special Issue New Trends in Intelligent Control and Filter Design)
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