Special Issue "Advanced Control Systems and Optimization Techniques"

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: 31 August 2018

Special Issue Editors

Guest Editor
Prof. Dr. Ing. Dipl. Math. Radu-Emil Precup

Department of Automation and Applied Informatics, Faculty of Automation and Computers, Politehnica University of Timisoara, Bd. V. Parvan 2, 300223 Timisoara, Romania
Website | E-Mail
Interests: control structures and algorithms (conventional control; fuzzy control; data-driven control; model-free control; sliding mode control; neuro-fuzzy control), theory and applications of soft computing; systems modelling; identification and optimization (including nature-inspired optimization)
Guest Editor
Prof. Dr. Sašo Blažič

Laboratory of Autonomous Mobile Systems, Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia
Website | E-Mail
Interests: adaptive control; fuzzy control; predictive control, modeling of nonlinear systems; autonomous mobile systems; mobile robotics; control of satellite systems

Special Issue Information

Dear Colleagues,

The last decade has seen a serious step forward regarding the complexity of various technical and non-technical processes and in high demand dynamic and steady-state performance, including the robustness of control systems. Advanced control systems that include intelligent control, adaptive control, data-driven and learning control, have been successfully applied to cope with the uncertainties and disturbances of many processes. The optimization algorithms play an important role in this context, as they give, in the case of correct formulations, solutions to rather complicated problems in order to systematically meet the performance specifications of control systems.

The dynamic environments are usually changing and control systems should adapt themselves accordingly. Therefore, by employing intelligent approaches (dealing, for example, with fuzzy systems, neural networks and nature-inspired optimization), advanced control systems have been developed. With this regard, more efforts should be focused on the methodology of the learning systems. However, the advantage of advanced analysis tools should be embedded to improve the control system performance.

Nowadays, process control applications are developed under the conditions of optimal performance requirements. However, there is generally no dynamical model available of the process, or the process model is too complex to be used in controller design. Since modeling and system identification tools can be expensive and time-consuming, and models may be time-varying, or nonlinear, or contain delays, data-driven control has been proposed, with the aim to avoid the use of process models in controller tuning and to efficiently use the information in large amounts of process input–output data to design predictors, controllers, and monitoring systems that guarantee the required control system performance.

The main objective of this Special Issue is to create a platform for scientists, engineers and practitioners, to share their latest theoretical and technological results and to discuss several issues for the research directions in the field of advanced control systems and optimization. The papers to be published in this Special Issue are expected to provide recent results in advanced modeling and controller design and tuning techniques especially for cross-fertilizations between the fields of advanced control systems and optimization. Papers containing experimental results regarding advanced control systems and optimization are especially welcome.

Prof. Dr. Radu-Emil Precup
Prof. Dr. Sašo Blažič
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. Machines 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) for publication in this open access journal is 350 CHF (Swiss Francs). 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

  • Advanced intelligent control
  • Data-driven control
  • Learning-based control
  • Systems modeling, parameter estimation and optimization
  • Fuzzy logic and neural network structures for controller design
  • Metaheuristics for process modeling and controller tuning
  • Machine learning for control and optimization
  • Adaptive and predictive control
  • Simulation and optimization of intelligent systems
  • Nonlinear observers of dynamical systems

Published Papers (2 papers)

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Research

Open AccessArticle Use of the Adjoint Method for Controlling the Mechanical Vibrations of Nonlinear Systems
Received: 28 March 2018 / Revised: 25 April 2018 / Accepted: 2 May 2018 / Published: 4 May 2018
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Abstract
In this work, the analytical derivation and the computer implementation of the adjoint method are described. The adjoint method can be effectively used for solving the optimal control problem associated with a large class of nonlinear mechanical systems. As discussed in this investigation,
[...] Read more.
In this work, the analytical derivation and the computer implementation of the adjoint method are described. The adjoint method can be effectively used for solving the optimal control problem associated with a large class of nonlinear mechanical systems. As discussed in this investigation, the adjoint method represents a broad computational framework, rather than a single numerical algorithm, in which the control problem for nonlinear dynamical systems can be effectively formulated and implemented employing a set of advanced analytical methods as well as an array of well-established numerical procedures. A detailed theoretical derivation and a comprehensive description of the numerical algorithm suitable for the computer implementation of the methodology used for performing the adjoint analysis are provided in the paper. For this purpose, two important cases are analyzed in this work, namely the design of a feedforward control scheme and the development of a feedback control architecture. In this investigation, the control problem relative to the mechanical vibrations of a nonlinear oscillator characterized by a generalized Van der Pol damping model is considered in order to illustrate the effectiveness of the computational algorithm based on the adjoint method by means of numerical experiments. Full article
(This article belongs to the Special Issue Advanced Control Systems and Optimization Techniques)
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Open AccessArticle System Identification Algorithm for Computing the Modal Parameters of Linear Mechanical Systems
Received: 1 March 2018 / Revised: 22 March 2018 / Accepted: 23 March 2018 / Published: 26 March 2018
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Abstract
The goal of this investigation is to construct a computational procedure for identifying the modal parameters of linear mechanical systems. The methodology employed in the paper is based on the Eigensystem Realization Algorithm implemented in conjunction with the Observer/Kalman Filter Identification method (ERA/OKID).
[...] Read more.
The goal of this investigation is to construct a computational procedure for identifying the modal parameters of linear mechanical systems. The methodology employed in the paper is based on the Eigensystem Realization Algorithm implemented in conjunction with the Observer/Kalman Filter Identification method (ERA/OKID). This method represents an effective and efficient system identification numerical procedure based on the time domain. The algorithm developed in this work is tested by means of numerical experiments on a full-car vehicle model. To this end, the modal parameters necessary for the design of active and semi-active suspension systems are obtained for the vehicle system considered as an illustrative example. In order to analyze the performance of the methodology developed in this investigation, the system identification numerical procedure was tested considering two case studies, namely a full state measurement and an incomplete state measurement. As expected, the numerical results found for the identified dynamical model showed a good agreement with the modal parameters of the mechanical system model. Furthermore, numerical results demonstrated that the proposed method has good performance considering a scenario in which the signal-to-noise ratio of the input and output measurements is relatively high. The method developed in this paper can be effectively used for solving important engineering problems such as the design of control systems for road vehicles. Full article
(This article belongs to the Special Issue Advanced Control Systems and Optimization Techniques)
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