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Algorithms
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Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes
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Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (4 November 2024) | Viewed by 23587

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Li Jin

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: power system stability analysis and control; time-delay system; robust theory and application
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sheng Du

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: process control; intelligent control; intelligent optimization; computational intelligence; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Zixin Huang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
Interests: underdrive system control; intelligent control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiongbo Wan

E-Mail Website
Guest Editor
1. School of Automation, China University of Geosciences, Wuhan 430074, China
2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
3. Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China
Interests: artificial intelligence; robust control of time-delay systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the ever-evolving landscape of industrial process modeling and optimization, data-driven intelligent algorithms have emerged as a transformative force. This Special Issue aims to explore the intersection of data-driven approaches, intelligent modeling, and optimization algorithms in the context of industrial processes. With the relentless growth of Industry 4.0, the integration of advanced data analytics, machine learning, and artificial intelligence has become imperative to opening up new possibilities in production efficiency, sustainability, and quality assurance in industrial processes.

Scope and objectives:

This Special Issue aims to explore the multifaceted aspects of data-driven intelligent modelling and optimization algorithms for industrial processes. The main objectives are to harness the power of data to improve control, decision making and parameter optimization, and to drive industrial systems to unprecedented levels of efficiency, reliability, and adaptability.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Data-Driven Modeling:
    Intelligent data representation;
    Integration/hybrid modeling.
  2. Machine Learning and Optimization:
    Advanced machine learning algorithms;
    Hybrid models with optimization algorithms;
    Adaptive learning algorithms.
  3. Intelligent Process Monitoring:
    Real-time data monitoring and analysis;
    Soft sensing technologies;
    Operating mode perception and recognition.
  4. Decision Support Systems:
    Intelligent decision support systems;
    The integration of optimization algorithms;
    Human–machine collaboration for enhanced decision making.


Prof. Dr. Li Jin
Prof. Dr. Sheng Du
Dr. Zixin Huang
Prof. Dr. Xiongbo Wan
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. Algorithms 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 1600 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

  • data-driven modeling
  • industrial processes
  • machine learning algorithms
  • optimization algorithms
  • adaptive learning

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Related Special Issue

Published Papers (13 papers)

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Editorial

Jump to: Research, Review

6 pages, 179 KiB  
Editorial
Recent Progress in Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes
by Sheng Du, Zixin Huang, Li Jin and Xiongbo Wan
Algorithms 2024, 17(12), 569; https://doi.org/10.3390/a17120569 - 12 Dec 2024
Cited by 2 | Viewed by 1260
Abstract
This editorial discusses recent progress in data-driven intelligent modeling and optimization algorithms for industrial processes. With the advent of Industry 4.0, the amalgamation of sophisticated data analytics, machine learning, and artificial intelligence has become pivotal, unlocking new horizons in production efficiency, sustainability, and [...] Read more.
This editorial discusses recent progress in data-driven intelligent modeling and optimization algorithms for industrial processes. With the advent of Industry 4.0, the amalgamation of sophisticated data analytics, machine learning, and artificial intelligence has become pivotal, unlocking new horizons in production efficiency, sustainability, and quality assurance. Contributions to this Special Issue highlight innovative research in advancements in work-sampling data analysis, data-driven process choreography discovery, intelligent ship scheduling for maritime rescue, process variability monitoring, hybrid optimization algorithms for economic emission dispatches, and intelligent controlled oscillations in smart structures. These studies collectively contribute to the body of knowledge on data-driven intelligent modeling and optimization, offering practical solutions and theoretical frameworks to address complex industrial challenges. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)

Research

Jump to: Editorial, Review

20 pages, 3221 KiB  
Article
A VIKOR-Based Sequential Three-Way Classification Ranking Method
by Wentao Xu, Jin Qian, Yueyang Wu, Shaowei Yan, Yongting Ni and Guangjin Yang
Algorithms 2024, 17(11), 530; https://doi.org/10.3390/a17110530 - 19 Nov 2024
Cited by 1 | Viewed by 976
Abstract
VIKOR uses the idea of overall utility maximization and individual regret minimization to afford a compromise result for multi-attribute decision-making problems with conflicting attributes. Many researchers have proposed corresponding improvements and expansions to make it more suitable for sorting optimization in their respective [...] Read more.
VIKOR uses the idea of overall utility maximization and individual regret minimization to afford a compromise result for multi-attribute decision-making problems with conflicting attributes. Many researchers have proposed corresponding improvements and expansions to make it more suitable for sorting optimization in their respective research fields. However, these improvements and extensions only rank the alternatives without classifying them. For this purpose, this text introduces the three-way sequential decisions method and combines it with the VIKOR method to design a three-way VIKOR method that can deal with both ranking and classification. By using the final negative ideal solution (NIS) and the final positive ideal solution (PIS) for all alternatives, the individual regret value and group utility value of each alternative were calculated. Different three-way VIKOR models were obtained by four different combinations of individual regret value and group utility value. In the ranking process, the characteristics of VIKOR method are introduced, and the subjective preference of decision makers is considered by using individual regret, group utility, and decision index values. In the classification process, the corresponding alternatives are divided into the corresponding decision domains by sequential three-way decisions, and the risk of direct acceptance or rejection is avoided by putting the uncertain alternatives into the boundary region to delay the decision. The alternative is divided into decision domains through sequential three-way decisions, sorted according to the collation rules in the same decision domain, and the final sorting results are obtained according to the collation rules in different decision domains. Finally, the effectiveness and correctness of the proposed method are verified by a project investment example, and the results are compared and evaluated. The experimental results show that the proposed method has a significant correlation with the results of other methods, ad is effective and feasible, and is simpler and more effective in dealing with some problems. Errors caused by misclassification is reduced by sequential three-way decisions. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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23 pages, 7960 KiB  
Article
Novelty in Intelligent Controlled Oscillations in Smart Structures
by Amalia Moutsopoulou, Markos Petousis, Georgios E. Stavroulakis, Anastasios Pouliezos and Nectarios Vidakis
Algorithms 2024, 17(11), 505; https://doi.org/10.3390/a17110505 - 4 Nov 2024
Cited by 1 | Viewed by 798
Abstract
Structural control techniques can be used to protect engineering structures. By computing instantaneous control forces based on the input from the observed reactions and adhering to a strong control strategy, intelligent control in structural engineering can be achieved. In this study, we employed [...] Read more.
Structural control techniques can be used to protect engineering structures. By computing instantaneous control forces based on the input from the observed reactions and adhering to a strong control strategy, intelligent control in structural engineering can be achieved. In this study, we employed intelligent piezoelectric patches to reduce vibrations in structures. The actuators and sensors were implemented using piezoelectric patches. We reduced structural oscillations by employing sophisticated intelligent control methods. Examples of such control methods include H-infinity and H2. An advantage of this study is that the results are presented for both static and dynamic loading, as well as for the frequency domain. Oscillation suppression must be achieved over the entire frequency range. In this study, advanced programming was used to solve this problem and complete oscillation suppression was achieved. This study explored in detail the methods and control strategies that can be used to address the problem of oscillations. These techniques have been thoroughly described and analyzed, offering valuable insights into their effective applications. The ability to reduce oscillations has significant implications for applications that extend to various structures and systems such as airplanes, metal bridges, and large metallic structures. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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26 pages, 1156 KiB  
Article
Adaptive Sliding-Mode Controller for a Zeta Converter to Provide High-Frequency Transients in Battery Applications
by Andrés Tobón, Carlos Andrés Ramos-Paja, Martha Lucía Orozco-Gutíerrez, Andrés Julián Saavedra-Montes and Sergio Ignacio Serna-Garcés
Algorithms 2024, 17(7), 319; https://doi.org/10.3390/a17070319 - 21 Jul 2024
Cited by 2 | Viewed by 1783
Abstract
Hybrid energy storage systems significantly impact the renewable energy sector due to their role in enhancing grid stability and managing its variability. However, implementing these systems requires advanced control strategies to ensure correct operation. This paper presents an algorithm for designing the power [...] Read more.
Hybrid energy storage systems significantly impact the renewable energy sector due to their role in enhancing grid stability and managing its variability. However, implementing these systems requires advanced control strategies to ensure correct operation. This paper presents an algorithm for designing the power and control stages of a hybrid energy storage system formed by a battery, a supercapacitor, and a bidirectional Zeta converter. The control stage involves an adaptive sliding-mode controller co-designed with the power circuit parameters. The design algorithm ensures battery protection against high-frequency transients that reduce lifespan, and provides compatibility with low-cost microcontrollers. Moreover, the continuous output current of the Zeta converter does not introduce current harmonics to the battery, the microgrid, or the load. The proposed solution is validated through an application example using PSIM electrical simulation software (version 2024.0), demonstrating superior performance in comparison with a classical cascade PI structure. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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29 pages, 6158 KiB  
Article
A Novel Hybrid Crow Search Arithmetic Optimization Algorithm for Solving Weighted Combined Economic Emission Dispatch with Load-Shifting Practice
by Bishwajit Dey, Gulshan Sharma and Pitshou N. Bokoro
Algorithms 2024, 17(7), 313; https://doi.org/10.3390/a17070313 - 16 Jul 2024
Cited by 3 | Viewed by 1503
Abstract
The crow search arithmetic optimization algorithm (CSAOA) method is introduced in this article as a novel hybrid optimization technique. This proposed strategy is a population-based metaheuristic method inspired by crows’ food-hiding techniques and merged with a recently created simple yet robust arithmetic optimization [...] Read more.
The crow search arithmetic optimization algorithm (CSAOA) method is introduced in this article as a novel hybrid optimization technique. This proposed strategy is a population-based metaheuristic method inspired by crows’ food-hiding techniques and merged with a recently created simple yet robust arithmetic optimization algorithm (AOA). The proposed method’s performance and superiority over other existing methods is evaluated using six benchmark functions that are unimodal and multimodal in nature, and real-time optimization problems related to power systems, such as the weighted dynamic economic emission dispatch (DEED) problem. A load-shifting mechanism is also implemented, which reduces the system’s generation cost even further. An extensive technical study is carried out to compare the weighted DEED to the penalty factor-based DEED and arrive at a superior compromise option. The effects of CO2, SO2, and NOx are studied independently to determine their impact on system emissions. In addition, the weights are modified from 0.1 to 0.9, and the effects on generating cost and emission are investigated. Nonparametric statistical analysis asserts that the proposed CSAOA is superior and robust. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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16 pages, 4902 KiB  
Article
Data-Driven Load Frequency Control for Multi-Area Power System Based on Switching Method under Cyber Attacks
by Guangqiang Tian and Fuzhong Wang
Algorithms 2024, 17(6), 233; https://doi.org/10.3390/a17060233 - 27 May 2024
Cited by 2 | Viewed by 1119
Abstract
This paper introduces an innovative method for load frequency control (LFC) in multi-area interconnected power systems vulnerable to denial-of-service (DoS) attacks. The system is modeled as a switching system with two subsystems, and an adaptive control algorithm is developed. Initially, a dynamic linear [...] Read more.
This paper introduces an innovative method for load frequency control (LFC) in multi-area interconnected power systems vulnerable to denial-of-service (DoS) attacks. The system is modeled as a switching system with two subsystems, and an adaptive control algorithm is developed. Initially, a dynamic linear data model is used to model each subsystem. Next, a model-free adaptive control strategy is introduced to maintain frequency stability in the multi-area interconnected power system, even during DoS attacks. A rigorous stability analysis of the power system is performed, and the effectiveness of the proposed approach is demonstrated by applying it to a three-area interconnected power system. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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13 pages, 2252 KiB  
Article
An Interface to Monitor Process Variability Using the Binomial ATTRIVAR SS Control Chart
by João Pedro Costa Violante, Marcela A. G. Machado, Amanda dos Santos Mendes and Túlio S. Almeida
Algorithms 2024, 17(5), 216; https://doi.org/10.3390/a17050216 - 16 May 2024
Cited by 1 | Viewed by 1051
Abstract
Control charts are tools of paramount importance in statistical process control. They are broadly applied in monitoring processes and improving quality, as they allow the detection of special causes of variation with a significant level of accuracy. Furthermore, there are several strategies able [...] Read more.
Control charts are tools of paramount importance in statistical process control. They are broadly applied in monitoring processes and improving quality, as they allow the detection of special causes of variation with a significant level of accuracy. Furthermore, there are several strategies able to be employed in different contexts, all of which offer their own advantages. Therefore, this study focuses on monitoring the variability in univariate processes through variance using the Binomial version of the ATTRIVAR Same Sample S2 (B-ATTRIVAR SS S2) control chart, given that it allows coupling attribute and variable inspections (ATTRIVAR means attribute + variable), i.e., taking advantage of the cost-effectiveness of the former and the wealth of information and greater performance of the latter. Its Binomial version was used for such a purpose, since inspections are made using two attributes, and the Same Sample was used due to being submitted to both the attribute and variable stages of inspection. A computational application was developed in the R language using the Shiny package so as to create an interface to facilitate its application and use in the quality control of the production processes. Its application enables users to input process parameters and generate the B-ATTRIVAR SS control chart for monitoring the process variability with variance. By comparing the data obtained from its application with a simpler code, its performance was validated, given that its results exhibited striking similarity. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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14 pages, 3876 KiB  
Article
Three-Dimensional Finite Element Modeling of Ultrasonic Vibration-Assisted Milling of the Nomex Honeycomb Structure
by Tarik Zarrouk, Mohammed Nouari, Jamal-Eddine Salhi, Mohammed Abbadi and Ahmed Abbadi
Algorithms 2024, 17(5), 204; https://doi.org/10.3390/a17050204 - 10 May 2024
Cited by 2 | Viewed by 1567
Abstract
Machining of Nomex honeycomb composite (NHC) structures is of critical importance in manufacturing parts to the specifications required in the aerospace industry. However, the special characteristics of the Nomex honeycomb structure, including its composite nature and complex geometry, require a specific machining approach [...] Read more.
Machining of Nomex honeycomb composite (NHC) structures is of critical importance in manufacturing parts to the specifications required in the aerospace industry. However, the special characteristics of the Nomex honeycomb structure, including its composite nature and complex geometry, require a specific machining approach to avoid cutting defects and ensure optimal surface quality. To overcome this problem, this research suggests the adoption of RUM technology, which involves the application of ultrasonic vibrations following the axis of revolution of the UCK cutting tool. To achieve this objective, a three-dimensional finite element numerical model of Nomex honeycomb structure machining is developed with the Abaqus/Explicit software, 2017 version. Based on this model, this research examines the impact of vibration amplitude on the machinability of this kind of structure, including cutting force components, stress and strain distribution, and surface quality as well as the size of the chips. In conclusion, the results highlight that the use of ultrasonic vibrations results in an important reduction in the components of the cutting force by up to 42%, improves the quality of the surface, and decreases the size of the chips. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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10 pages, 1658 KiB  
Article
Intelligent Ship Scheduling and Path Planning Method for Maritime Emergency Rescue
by Wen Ying, Zhaohui Wang, Hui Li, Sheng Du and Man Zhao
Algorithms 2024, 17(5), 197; https://doi.org/10.3390/a17050197 - 8 May 2024
Cited by 2 | Viewed by 1553
Abstract
Intelligent ship navigation scheduling and planning is of great significance for ensuring the safety of maritime production and life and promoting the development of the marine economy. In this paper, an intelligent ship scheduling and path planning method is proposed for a practical [...] Read more.
Intelligent ship navigation scheduling and planning is of great significance for ensuring the safety of maritime production and life and promoting the development of the marine economy. In this paper, an intelligent ship scheduling and path planning method is proposed for a practical application scenario wherein the emergency rescue center receives rescue messages and dispatches emergency rescue ships to the incident area for rescue. Firstly, the large-scale sailing route of the task ship is pre-planned in the voyage planning stage by using the improved A* algorithm. Secondly, the full-coverage path planning algorithm is used to plan the ship’s search route in the regional search stage by updating the ship’s navigation route in real time. In order to verify the effectiveness of the proposed algorithm, comparative experiments were carried out with the conventional algorithm in the two operation stages of rushing to the incident sea area and regional search and rescue. The experimental results show that the proposed algorithm can adapt to emergency search and rescue tasks in the complex setting of the sea area and can effectively improve the efficiency of the operation, ensure the safety of the operation process, and provide a more intelligent and efficient solution for the planning of maritime emergency rescue tasks. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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22 pages, 799 KiB  
Article
A Data-Driven Approach to Discovering Process Choreography
by Jaciel David Hernandez-Resendiz, Edgar Tello-Leal and Marcos Sepúlveda
Algorithms 2024, 17(5), 188; https://doi.org/10.3390/a17050188 - 29 Apr 2024
Cited by 1 | Viewed by 1593
Abstract
Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes [...] Read more.
Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes a complete and modular data-driven approach that implements natural language processing techniques, text similarity, and process mining techniques (discovery and conformance checking) through a set of methods and formal rules that enable analysis of the data contained in the event logs and the intra-organizational process models of the participants in the collaboration, to identify patterns that allow the discovery of the process choreography. The approach enables merging the event logs of the inter-organizational collaboration participants from the identified message interactions, enabling the automatic construction of an IOBP model. The proposed approach was evaluated using four real-life and two artificial event logs. In discovering the choreography process, average values of 0.86, 0.89, and 0.86 were obtained for relationship precision, relation recall, and relationship F-score metrics. In evaluating the quality of the built IOBP models, values of 0.95 and 1.00 were achieved for the precision and recall metrics, respectively. The performance obtained in the different scenarios is encouraging, demonstrating the ability of the approach to discover the process choreography and the construction of business process models in inter-organizational environments. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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17 pages, 2232 KiB  
Article
Advancements in Data Analysis for the Work-Sampling Method
by Borut Buchmeister and Natasa Vujica Herzog
Algorithms 2024, 17(5), 183; https://doi.org/10.3390/a17050183 - 29 Apr 2024
Cited by 1 | Viewed by 4636
Abstract
The work-sampling method makes it possible to gain valuable insights into what is happening in production systems. Work sampling is a process used to estimate the proportion of shift time that workers (or machines) spend on different activities (within productive work or losses). [...] Read more.
The work-sampling method makes it possible to gain valuable insights into what is happening in production systems. Work sampling is a process used to estimate the proportion of shift time that workers (or machines) spend on different activities (within productive work or losses). It is estimated based on enough random observations of activities over a selected period. When workplace operations do not have short cycle times or high repetition rates, the use of such a statistical technique is necessary because the labor sampling data can provide information that can be used to set standards. The work-sampling procedure is well standardized, but additional contributions are possible when evaluating the observations. In this paper, we present our contribution to improving the decision-making process based on work-sampling data. We introduce a correlation comparison of the measured hourly shares of all activities in pairs to check whether there are mutual connections or to uncover hidden connections between activities. The results allow for easier decision-making (conclusions) regarding the influence of the selected activities on the triggering of the others. With the additional calculation method, we can uncover behavioral patterns that would have been overlooked with the basic method. This leads to improved efficiency and productivity of the production system. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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19 pages, 689 KiB  
Article
Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling
by Fengwei Jing, Fenghe Li, Yong Song, Jie Li, Zhanbiao Feng and Jin Guo 
Algorithms 2024, 17(3), 102; https://doi.org/10.3390/a17030102 - 26 Feb 2024
Cited by 1 | Viewed by 1815
Abstract
The concept of production stability in hot strip rolling encapsulates the ability of a production line to consistently maintain its output levels and uphold the quality of its products, thus embodying the steady and uninterrupted nature of the production yield. This scholarly paper [...] Read more.
The concept of production stability in hot strip rolling encapsulates the ability of a production line to consistently maintain its output levels and uphold the quality of its products, thus embodying the steady and uninterrupted nature of the production yield. This scholarly paper focuses on the paramount looper equipment in the finishing rolling area, utilizing it as a case study to investigate approaches for identifying the origins of instabilities, specifically when faced with inadequate looper performance. Initially, the paper establishes the equipment process accuracy evaluation (EPAE) model for the looper, grounded in the precision of the looper’s operational process, to accurately depict the looper’s functioning state. Subsequently, it delves into the interplay between the EPAE metrics and overall production stability, advocating for the use of EPAE scores as direct indicators of production stability. The study further introduces a novel algorithm designed to trace the root causes of issues, categorizing them into material, equipment, and control factors, thereby facilitating on-site fault rectification. Finally, the practicality and effectiveness of this methodology are substantiated through its application on the 2250 hot rolling equipment production line. This paper provides a new approach for fault tracing in the hot rolling process. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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Review

Jump to: Editorial, Research

29 pages, 1068 KiB  
Review
A Review on Data-Driven Model-Free Sliding Mode Control
by Duby Castellanos-Cárdenas, Norha L. Posada, Andrés Orozco-Duque, Lina M. Sepúlveda-Cano, Fabio Castrillón, Oscar E. Camacho and Rafael E. Vásquez
Algorithms 2024, 17(12), 543; https://doi.org/10.3390/a17120543 - 2 Dec 2024
Cited by 2 | Viewed by 1846
Abstract
Sliding mode control (SMC) has been widely used to control linear and nonlinear dynamics systems because of its robustness against parametric uncertainties and matched disturbances. Although SMC design has traditionally addressed process model-based approaches, the rapid advancements in instrumentation and control systems driven [...] Read more.
Sliding mode control (SMC) has been widely used to control linear and nonlinear dynamics systems because of its robustness against parametric uncertainties and matched disturbances. Although SMC design has traditionally addressed process model-based approaches, the rapid advancements in instrumentation and control systems driven by Industry 4.0, coupled with the increased complexity of the controlled processes, have led to the growing acceptance of controllers based on data-driven techniques. This review article aims to explore the landscape of SMC, focusing specifically on data-driven techniques through a comprehensive systematic literature review that includes a bibliometric analysis of relevant documents and a cumulative production model to estimate the deceleration point of the scientific production of this topic. The most used SMC schemes and their integration with data-driven techniques and intelligent algorithms, including identifying the leading applications, are presented. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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