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Processes
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Advances in the Control of Complex Dynamic Systems
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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 July 2024 | Viewed by 2142

Special Issue Editor

Dr. Gorazd Karer

E-Mail Website
Guest Editor
Laboratory of Control Systems and Cybernetics, Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
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

Published Papers (2 papers)

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Research

24 pages, 1304 KiB  
Article
Distributed Control of an Ill-Conditioned Non-Linear Process Using Control Relevant Excitation Signals
by Yusuf Abubakar Sha’aban
Processes 2023, 11(12), 3320; https://doi.org/10.3390/pr11123320 - 29 Nov 2023
Cited by 1 | Viewed by 635
Abstract
Efficient control schemes for ill-conditioned systems, such as the high-purity distillation column, can be challenging and costly to design and implement. In this paper, we propose a distributed control scheme that utilizes well-designed excitation signals to identify the system. Unlike traditional systems, we [...] Read more.
Efficient control schemes for ill-conditioned systems, such as the high-purity distillation column, can be challenging and costly to design and implement. In this paper, we propose a distributed control scheme that utilizes well-designed excitation signals to identify the system. Unlike traditional systems, we found that a summation of correlated and uncorrelated signals can yield better excitation of the plant. Our proposed distributed model predictive control (MPC) scheme uses a shifted input sequence to address loop interactions and reduce the computational load. This approach deviates from traditional schemes that use iteration, which can increase complexity and computational load. We initially tested the proposed method on the linear model of a highly coupled 2 × 2 process and compared its performance with decentralized proportional-integral-derivative (PID) controllers and centralized MPC. Our results show improved performance over PID controllers and similar results to centralized MPC. Furthermore, we compared the performance of the proposed approach with a centralized MPC on a nonlinear model of a distillation column. The results for the second study also demonstrated comparable performance between the two controllers with the decentralised control slightly outperforming the centralised MPC in some cases. These findings are promising and may be of interest to practitioners that are more comfortable with tuning decentralised loops. Full article
(This article belongs to the Special Issue Advances in the Control of Complex Dynamic Systems)
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30 pages, 16476 KiB  
Article
Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking
by John Kern, Dailin Marrero and Claudio Urrea
Processes 2023, 11(12), 3267; https://doi.org/10.3390/pr11123267 - 22 Nov 2023
Cited by 3 | Viewed by 1085
Abstract
This research delves into the development and evaluation of two distinct controllers for a 3-DoF robotic arm in the context of Industry 4.0. Two primary control strategies are presented in the study. The first is a Fuzzy Logic Controller that utilizes joint position [...] Read more.
This research delves into the development and evaluation of two distinct controllers for a 3-DoF robotic arm in the context of Industry 4.0. Two primary control strategies are presented in the study. The first is a Fuzzy Logic Controller that utilizes joint position error and its derivative as inputs, employing a set of 9 control knowledge rules. The second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller, trained to learn the inverse dynamic model of the robot through a structured dataset. The research emphasizes the importance of accurate parameter tuning and data acquisition to achieve optimal control system performance. Extensive experimentation was conducted to evaluate the controllers’ performance in trajectory tracking and their response against external disturbances, such as load variations. The controllers exhibited remarkable precision and proficiency in tracking reference trajectories, with minimal deviations, overshoots, or oscillations. A quantitative analysis using performance indices such as root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE) further confirmed the controllers’ effectiveness. Notably, the ANFIS Controller consistently outperformed the Fuzzy Logic Controller, demonstrating superior precision in trajectory tracking. The study underscored the importance of selecting the right control method and obtaining high-quality training data. Challenges in parameter tuning for Fuzzy Logic Controllers and potential time constraints in training ANFIS were discussed. The findings have significant implications for advancing robotic control systems, particularly in the era of Industry 4.0. Full article
(This article belongs to the Special Issue Advances in the Control of Complex Dynamic Systems)
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