Advances in the Control of Complex Dynamic Systems
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".
Deadline for manuscript submissions: 5 January 2025 | Viewed by 12479
Special Issue Editor
Interests: modeling; simulation; hybrid systems; nonlinear systems; fuzzy systems; model predictive control; robust control; optimization algorithms; intelligent methods; depth of anesthesia
Special Issue Information
Dear Colleagues,
This Special Issue addresses ongoing research and development in the field of control systems engineering, focusing on the modeling, identification, and control of systems with complex dynamics, distinct nonlinearities, and interacting components. Techniques used in this area include model-based control, adaptive control, optimal control, and robust control. The goal is to develop control systems that can effectively manage the complexity and uncertainty inherent in these systems, resulting in improved performance and stability.
A very important aspect is the modeling and identification of the complex processes involved. Significant nonlinearities can be observed in many real-world processes. For example, a well-established approach for dealing with nonlinearities is fuzzy logic. Fuzzy models represent efficient universal approximators of nonlinear dynamics since they can be used to approximate any continuous nonlinear function with arbitrary accuracy. Many processes exhibit both continuous and discrete dynamical properties. Such hybrid systems are dynamic systems that can contain both continuous and discrete states or inputs, and often, the continuous and discrete dynamics are inextricably intertwined. For the most complex processes, modern evolving approaches seem to give good results.
Model predictive control is a family of control methods in which a model of the system is used to predict the future behavior of the system given certain inputs. The optimal inputs that are finally applied to the real system are usually determined by various optimization techniques. Various intelligent methods and algorithms can be implemented to improve the stability and performance of the closed loop system.
Topics of interest include but are not limited to:
- Complex process modeling;
- Identification;
- Fuzzy systems;
- Hybrid systems;
- Evolving systems;
- Interval systems;
- Model predictive control;
- Robust control;
- Optimization algorithms;
- Intelligent methods.
Dr. Gorazd Karer
Guest Editor
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. Processes 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
- complex process modeling
- identification
- fuzzy systems
- hybrid systems
- evolving systems
- interval systems
- model predictive control
- robust control
- optimization algorithms
- intelligent methods
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