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Machines
Special Issues
Advanced Control Systems and Optimization Techniques
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Advanced Control Systems and Optimization Techniques

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

Deadline for manuscript submissions: closed (31 August 2018) | Viewed by 34780

Special Issue Editors

Prof. Dr. Radu-Emil Precup

E-Mail Website
Guest Editor
Department of Automation and Applied Informatics, Faculty of Automation and Computers, Politehnica University of Timişoara, Bulevardul Vasile Pârvan, Nr. 2, 300223 Timişoara, Romania
Interests: new control structures and algorithms; soft computing; computer-aided design of control systems; modelling; optimization; mechatronic systems; embedded systems; control of power plants; servo systems; electrical driving systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sašo Blažič

E-Mail Website
Guest Editor
Laboratory of Control Systems and Cybernetics, Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia
Interests: control of nonlinear systems; modeling of nonlinear systems; autonomous mobile systems; mobile robotics; motion control; trajectory tracking
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 monthly 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 2400 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 (6 papers)

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Research

14 pages, 5590 KiB  
Article
Robust Control of Small Turbojet Engines
by Rudolf Andoga, Ladislav Főző, Radovan Kovács, Károly Beneda, Tomáš Moravec and Michal Schreiner
Machines 2019, 7(1), 3; https://doi.org/10.3390/machines7010003 - 04 Jan 2019
Cited by 29 | Viewed by 5989
Abstract
Modern turbojet engines mainly use computerized digital engine control systems. This opens the way for application of advanced algorithms aimed at increasing their operational efficiency and safety. The theory of robust control is a set of methods known for good results in complex [...] Read more.
Modern turbojet engines mainly use computerized digital engine control systems. This opens the way for application of advanced algorithms aimed at increasing their operational efficiency and safety. The theory of robust control is a set of methods known for good results in complex control tasks, making them ideal candidates for application in the current turbojet engine control units. Different methodologies in the design of robust controllers, utilizing a small turbojet engine with variable exhaust nozzle designated as iSTC-21v, were therefore investigated in the article. The resulting controllers were evaluated for efficiency in laboratory conditions. The aim was to find a suitable approach and design method for robust controllers, taking into account the limitations and specifics of a real turbojet engine and its hardware, contrary to most studies which have used only simulated environments. The article shows the most effective approach in the design of robust controllers and the resulting speed controllers for a class of small turbojet engines, which can be applied in a discrete digital control environment. Full article
(This article belongs to the Special Issue Advanced Control Systems and Optimization Techniques)
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9 pages, 2787 KiB  
Article
Nonlinear Model Predictive Control Using Robust Fixed Point Transformation-Based Phenomena for Controlling Tumor Growth
by Bence Czakó and Levente Kovács
Machines 2018, 6(4), 49; https://doi.org/10.3390/machines6040049 - 25 Oct 2018
Cited by 7 | Viewed by 2913
Abstract
In this paper a novel control strategy is introduced in order to create optimal dosage profiles for individualized cancer treatment. This approach uses Nonlinear Model Predictive Control to construct optimal dosage protocols in conjunction with Robust Fixed Point Transformations which hinders the negative [...] Read more.
In this paper a novel control strategy is introduced in order to create optimal dosage profiles for individualized cancer treatment. This approach uses Nonlinear Model Predictive Control to construct optimal dosage protocols in conjunction with Robust Fixed Point Transformations which hinders the negative effect of inherent model uncertainties and measurement disturbances. The results are validated by extensive simulation on the proposed control algorithm from which conclusions were drawn. Full article
(This article belongs to the Special Issue Advanced Control Systems and Optimization Techniques)
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14 pages, 1799 KiB  
Article
MPC Control and LQ Optimal Control of A Two-Link Robot Arm: A Comparative Study
by El-Hadi Guechi, Samir Bouzoualegh, Youcef Zennir and Sašo Blažič
Machines 2018, 6(3), 37; https://doi.org/10.3390/machines6030037 - 17 Aug 2018
Cited by 22 | Viewed by 9829
Abstract
This study examined the control of a planar two-link robot arm. The control approach design was based on the dynamic model of the robot. The mathematical model of the system was nonlinear, and thus a feedback linearization control was first proposed to obtain [...] Read more.
This study examined the control of a planar two-link robot arm. The control approach design was based on the dynamic model of the robot. The mathematical model of the system was nonlinear, and thus a feedback linearization control was first proposed to obtain a linear system for which a model predictive control (MPC) was developed. The MPC control parameters were obtained analytically by minimizing a cost function. In addition, a simulation study was done comparing the proposed MPC control approach, the linear quadratic (LQ) control based on the same feedback linearization, and a control approach proposed in the literature for the same problem. The results showed the efficiency of the proposed method. Full article
(This article belongs to the Special Issue Advanced Control Systems and Optimization Techniques)
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9 pages, 2415 KiB  
Article
Research on PCBN Tool Dry Cutting GCr15
by Qinghua Li, Chen Pan, Yuxin Jiao and Kaixing Hu
Machines 2018, 6(3), 28; https://doi.org/10.3390/machines6030028 - 01 Jul 2018
Cited by 8 | Viewed by 3178
Abstract
This paper is based on the theoretical analysis designs of a dry cutting orthogonal test in order to study a phenomenon that the radial force is larger than the main cutting force when a PCBN (polycrystalline cubic boron nitride) tool hard turns GCr15. [...] Read more.
This paper is based on the theoretical analysis designs of a dry cutting orthogonal test in order to study a phenomenon that the radial force is larger than the main cutting force when a PCBN (polycrystalline cubic boron nitride) tool hard turns GCr15. Finite element modelling and cutting tests show the cutting depth and the spindle speed having an impact on the main cutting force, the radial force, and the axial force. In this study, due to the shear function of the cutting process, the squeezing effect between the tool and the workpiece, and the metal softening effect of the workpiece material, the different cutting depth and the spindle speed bring about different cutting force changes, and also different spindle speeds have different effects on the three components of the total cutting force. The research result provides a basis for further study on dry turning of hardened bearing steel. Full article
(This article belongs to the Special Issue Advanced Control Systems and Optimization Techniques)
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21 pages, 1236 KiB  
Article
Use of the Adjoint Method for Controlling the Mechanical Vibrations of Nonlinear Systems
by Carmine Maria Pappalardo and Domenico Guida
Machines 2018, 6(2), 19; https://doi.org/10.3390/machines6020019 - 04 May 2018
Cited by 34 | Viewed by 5025
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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20 pages, 870 KiB  
Article
System Identification Algorithm for Computing the Modal Parameters of Linear Mechanical Systems
by Carmine Maria Pappalardo and Domenico Guida
Machines 2018, 6(2), 12; https://doi.org/10.3390/machines6020012 - 26 Mar 2018
Cited by 35 | Viewed by 5213
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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