Special Issue "Advanced Control Systems and Optimization Techniques"
A special issue of Machines (ISSN 2075-1702).
Deadline for manuscript submissions: closed (31 August 2018)
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
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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)
Prof. Dr. Sašo Blažič
Laboratory of Autonomous Mobile Systems, Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia
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Interests: adaptive control; fuzzy control; predictive control, modeling of nonlinear systems; autonomous mobile systems; mobile robotics; control of satellite systems
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
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.
- 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