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Intelligent Sensing and Decision-Making in Advanced Manufacturing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 22539

Special Issue Editors


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Guest Editor
Department of Radiology, Duke University, Durham, NC 27707, USA
Interests: cloud manufacturing; scheduling; modeling and simulation; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China
Interests: human–robot collaboration; smart product-service systems; engineering informatics; smart manufacturing systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
Interests: intelligent manufacturing; deep learning; machine learning; fault diagnosis; surface defect recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, with the rapid development of advanced network technologies (e.g., 5G) and artificial intelligence technologies (e.g., deep neural networks), advanced manufacturing (AM) systems are being applied more often, making AM systems increasingly digitalized, networked, and intelligent. Sensing and decision-making techniques, as the fundamental elements to achieve intelligent manufacturing, are highly significant and have been widely adopted in this process of change. This Special Issue therefore aims to formulate original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of intelligent sensing and decision-making for AM systems. Potential topics include but are not limited to:

  • Smart sensors for manufacturing devices;
  • Intrusive sensors and non-intrusive sensors
  • AI-based sensing technologies;
  • Intelligent sensors on industrial robots;
  • Dynamic decision-making methods for AM;
  • Uncertainty-oriented methods in AM;
  • Modeling and simulation for AM;
  • Planning and scheduling for AM;
  • Human-robot collaborative planning in AM;
  • Motion planning and control of industrial robotics;
  • Supply chain management and logistics in AM

Dr. Longfei Zhou
Dr. Pai Zheng
Prof. Dr. Xinyu Li
Guest Editors

Manuscript Submission Information

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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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • smart sensors for manufacturing devices
  • intrusive sensors and non-intrusive sensors
  • AI-based sensing technologies
  • intelligent sensors on industrial robots
  • dynamic decision-making methods for AM
  • uncertainty-oriented methods in AM
  • modeling and simulation for AM
  • planning and scheduling for AM
  • human-robot collaborative planning in AM
  • motion planning and control of industrial robotics
  • supply chain management and logistics in AM

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Published Papers (12 papers)

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Research

51 pages, 9945 KiB  
Article
Data-Driven AI Models within a User-Defined Optimization Objective Function in Cement Production
by Othonas Manis, Michalis Skoumperdis, Christos Kioroglou, Dimitrios Tzilopoulos, Miltos Ouzounis, Michalis Loufakis, Nikolaos Tsalikidis, Nikolaos Kolokas, Panagiotis Georgakis, Ilias Panagoulias, Alexandros Tsolkas, Dimosthenis Ioannidis, Dimitrios Tzovaras and Mile Stankovski
Sensors 2024, 24(4), 1225; https://doi.org/10.3390/s24041225 - 14 Feb 2024
Viewed by 743
Abstract
This paper explores the energy-intensive cement industry, focusing on a plant in Greece and its mill and kiln unit. The data utilized include manipulated, non-manipulated, and uncontrolled variables. The non-manipulated variables are computed based on the machine learning (ML) models and selected by [...] Read more.
This paper explores the energy-intensive cement industry, focusing on a plant in Greece and its mill and kiln unit. The data utilized include manipulated, non-manipulated, and uncontrolled variables. The non-manipulated variables are computed based on the machine learning (ML) models and selected by the minimum value of the normalized root mean square error (NRMSE) across nine (9) methods. In case the distribution of the data displayed in the user interface changes, the user should trigger the retrain of the AI models to ensure their accuracy and robustness. To form the objective function, the expert user should define the desired weight for each manipulated or non-manipulated variable through the user interface (UI), along with its corresponding constraints or target value. The user selects the variables involved in the objective function based on the optimization strategy, and the evaluation is based on the comparison of the optimized and the active value of the objective function. The differential evolution (DE) method optimizes the objective function that is formed by the linear combination of the selected variables. The results indicate that using DE improves the operation of both the cement mill and kiln, yielding a lower objective function value compared to the current values. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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26 pages, 9070 KiB  
Article
Novel Information-Driven Smoothing Spline Linearization Method for High-Precision Displacement Sensors Based on Information Criterions
by Wen-Hao Zhang, Lin Dai, Wang Chen, Anyu Sun, Wu-Le Zhu and Bing-Feng Ju
Sensors 2023, 23(22), 9268; https://doi.org/10.3390/s23229268 - 18 Nov 2023
Viewed by 886
Abstract
A noise-resistant linearization model that reveals the true nonlinearity of the sensor is essential for retrieving accurate physical displacement from the signals captured by sensing electronics. In this paper, we propose a novel information-driven smoothing spline linearization method, which innovatively integrates one new [...] Read more.
A noise-resistant linearization model that reveals the true nonlinearity of the sensor is essential for retrieving accurate physical displacement from the signals captured by sensing electronics. In this paper, we propose a novel information-driven smoothing spline linearization method, which innovatively integrates one new and three standard information criterions into a smoothing spline for the high-precision displacement sensors’ linearization. Using theoretical analysis and Monte Carlo simulation, the proposed linearization method is demonstrated to outperform traditional polynomial and spline linearization methods for high-precision displacement sensors with a low noise to range ratio in the 10−5 level. Validation experiments were carried out on two different types of displacement sensors to benchmark the performance of the proposed method compared to the polynomial models and the the non-smoothing cubic spline. The results show that the proposed method with the new modified Akaike Information Criterion stands out compared to the other linearization methods and can improve the residual nonlinearity by over 50% compared to the standard polynomial model. After being linearized via the proposed method, the residual nonlinearities reach as low as ±0.0311% F.S. (Full Scale of Range), for the 1.5 mm range chromatic confocal displacement sensor, and ±0.0047% F.S., for the 100 mm range laser triangulation displacement sensor. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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22 pages, 4420 KiB  
Article
A Sustainability-Based Expert System for Additive Manufacturing and CNC Machining
by Josage Chathura Perera, Bhaskaran Gopalakrishnan, Prakash Singh Bisht, Subodh Chaudhari and Senthil Sundaramoorthy
Sensors 2023, 23(18), 7770; https://doi.org/10.3390/s23187770 - 09 Sep 2023
Viewed by 962
Abstract
The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For [...] Read more.
The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid®V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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14 pages, 922 KiB  
Article
Can ChatGPT Help in Electronics Research and Development? A Case Study with Applied Sensors
by Zoltán Tafferner, Illés Balázs, Olivér Krammer and Attila Géczy
Sensors 2023, 23(10), 4879; https://doi.org/10.3390/s23104879 - 18 May 2023
Cited by 6 | Viewed by 5369
Abstract
In this paper, we investigated the applicability of ChatGPT AI in electronics research and development via a case study of applied sensors in embedded electronic systems, a topic that is rarely mentioned in the recent literature, thus providing new insight for professionals and [...] Read more.
In this paper, we investigated the applicability of ChatGPT AI in electronics research and development via a case study of applied sensors in embedded electronic systems, a topic that is rarely mentioned in the recent literature, thus providing new insight for professionals and academics. The initial electronics-development tasks of a smart home project were prompted to the ChatGPT system to find out its capabilities and limitations. We wanted to obtain detailed information on the central processing controller units and the actual sensors usable for the specific project, their specifications and recommendations on the hardware and software design flow additionally. Furthermore, an extensive literature survey was requested to see if the bot could offer scientific papers covering the given topic. It was found that the ChatGPT responded with proper recommendations on controllers. However, the suggested sensor units, the hardware and software design were only partially acceptable, with occasional errors in specifications and generated code. The results of the literature survey showed that non-acceptable, fabricated citations (fake authors list, title, journal details and DOI—Digital Object identifier) were presented by the bot. The paper provides a detailed qualitative analysis, a performance analysis and critical discussion of the aforementioned aspects while providing the query set, the generated answers and codes as supplied data with the goal to give added value to electronics researchers and developers if trying to reach out for the tools in their profession. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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15 pages, 4980 KiB  
Article
Accurate Cutting-Force Measurement with Smart Tool Holder in Lathe
by Wandong Song, Jingjie Zhang, Guangchun Xiao, Mingdong Yi, Zhaoqiang Chen, Li Wang, Jun Chen and Chonghai Xu
Sensors 2023, 23(9), 4419; https://doi.org/10.3390/s23094419 - 30 Apr 2023
Viewed by 1489
Abstract
Cutting force in lathe work is closely related to tool wear and affects the turning quality. Direct measurement of the cutting force by measuring the strain of the tool holder is challenging because the tool holder design aims to be highly rigid in [...] Read more.
Cutting force in lathe work is closely related to tool wear and affects the turning quality. Direct measurement of the cutting force by measuring the strain of the tool holder is challenging because the tool holder design aims to be highly rigid in order to undertake large cutting forces. Accordingly, the most popular dynamometer designs modify the standard tool holder by decreasing the structural rigidity of the holder, which reduces the machining precision and is not widely accepted. In order to solve the issue of the low stiffness of the dynamometer reducing the machining precision, in this paper, the ultra-low strain on the tool holder was successfully detected by the highly sensitive semiconductor strain gauges (SCSG) adjacent to the blade cutting insert. However, the cutting process would generate much heat, which increases the force measuring area temperature of the tool holder by about 30 °C. As a result, the readout drifted significantly with the temperature changes due to the high temperature coefficient of SCSG. To solve this problem, the temperature on the tool holder was monitored and a BP neural network was proposed to compensate for temperature drift errors. Our methods improved the sensitivity (1.14 × 10−2 mV/N) and the average relative error of the BP neural network prediction (≤1.48%) while maintaining the original stiffness of the tool holder. The smart tool holder developed possesses high natural frequency (≥6 kHz), it is very suitable for dynamic cutting-force measurement. The cutting experiment data in the lathe work show comparable performance with the traditional dynamometers and the resolution of the smart tool holder is 2 N (0.25% of total range). Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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21 pages, 759 KiB  
Article
Multicriteria Decision Making in Supply Chain Management Using FMEA and Hybrid AHP-PROMETHEE Algorithms
by Bandar Altubaishe and Salil Desai
Sensors 2023, 23(8), 4041; https://doi.org/10.3390/s23084041 - 17 Apr 2023
Cited by 5 | Viewed by 1588
Abstract
In today’s global environment, supplier selection is one of the critical strategic decisions made by supply chain management. The supplier selection process involves the evaluation of suppliers based on several criteria, including their core capabilities, price offerings, lead times, geographical proximity, data collection [...] Read more.
In today’s global environment, supplier selection is one of the critical strategic decisions made by supply chain management. The supplier selection process involves the evaluation of suppliers based on several criteria, including their core capabilities, price offerings, lead times, geographical proximity, data collection sensor networks, and associated risks. The ubiquitous presence of internet of things (IoT) sensors at different levels of supply chains can result in risks that cascade to the upstream end of the supply chain, making it imperative to implement a systematic supplier selection methodology. This research proposes a combinatorial approach for risk assessment in supplier selection using the failure mode effect analysis (FMEA) with hybrid analytic hierarchy process (AHP) and the preference ranking organization method for enrichment evaluation (PROMETHEE). The FMEA is used to identify the failure modes based on a set of supplier criteria. The AHP is implemented to determine the global weights for each criterion, and PROMETHEE is used to prioritize the optimal supplier based on the lowest supply chain risk. The integration of multicriteria decision making (MCDM) methods overcomes the shortcomings of the traditional FMEA and enhances the precision of prioritizing the risk priority numbers (RPN). A case study is presented to validate the combinatorial model. The outcomes indicate that suppliers were evaluated more effectively based on company chosen criteria to select a low-risk supplier over the traditional FMEA approach. This research establishes a foundation for the application of multicriteria decision-making methodology for unbiased prioritization of critical supplier selection criteria and evaluation of different supply chain suppliers. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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13 pages, 6438 KiB  
Article
Enabling Modular Robotics with Secure Transducer Identification Based on Extended IEEE 21450 Transducer Electronic Datasheets
by Tobias Mitterer, Christian Lederer and Hubert Zangl
Sensors 2023, 23(5), 2873; https://doi.org/10.3390/s23052873 - 06 Mar 2023
Cited by 1 | Viewed by 1580
Abstract
In robotics, there are many different sensors and actuators mounted onto a robot which may also, in the case of modular robotics, be interchanged during operation. During development of new sensors or actuators, prototypes may also be mounted onto a robot to test [...] Read more.
In robotics, there are many different sensors and actuators mounted onto a robot which may also, in the case of modular robotics, be interchanged during operation. During development of new sensors or actuators, prototypes may also be mounted onto a robot to test functionality, where the new prototypes often have to be integrated manually into the robot environment. Proper, fast and secure identification of new sensor or actuator modules for the robot thus becomes important. In this work, a workflow to add new sensors or actuators to an existing robot environment while establishing trust in an automated manner using electronic datasheets has been developed. The new sensors or actuators are identified via near field communication (NFC) to the system and exchange security information via the same channel. By using electronic datasheets stored on the sensor or actuator, the device can be easily identified and trust can be established by using additional security information contained in the datasheet. In addition, the NFC hardware can simultaneously be used for wireless charging (WLC), thus allowing for wireless sensor and actuator modules. The developed workflow has been tested with prototype tactile sensors mounted onto a robotic gripper. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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12 pages, 10397 KiB  
Article
Methodology for the Generation of High-Quality Ultrasonic Images of Complex Geometry Pieces Using Industrial Robots
by Sofía Aparicio Secanellas, Iñaki Gauna León, Montserrat Parrilla, Montserrat Acebes, Alberto Ibáñez, Héctor de Matías Jiménez, Óscar Martínez-Graullera, Alberto Álvarez de Pablos, Margarita González Hernández and José Javier Anaya Velayos
Sensors 2023, 23(5), 2684; https://doi.org/10.3390/s23052684 - 01 Mar 2023
Cited by 3 | Viewed by 1285
Abstract
Industrial robotic arms integrated with server computers, sensors and actuators have revolutionized the way automated non-destructive testing is performed in the aeronautical sector. Currently, there are commercial, industrial robots that have the precision, speed and repetitiveness in their movements that make them suitable [...] Read more.
Industrial robotic arms integrated with server computers, sensors and actuators have revolutionized the way automated non-destructive testing is performed in the aeronautical sector. Currently, there are commercial, industrial robots that have the precision, speed and repetitiveness in their movements that make them suitable for use in numerous non-destructive testing inspections. Automatic ultrasonic inspection of complex geometry parts remains one of the most difficult challenges in the market. The closed configuration, i.e., restricted access to internal motion parameters, of these robotic arms makes it difficult for an adequate synchronism between the movement of the robot and the acquisition of the data. This is a serious problem in the inspection of aerospace components, where high-quality images are necessary to assess the condition of the inspected component. In this paper, we applied a methodology recently patented for the generation of high-quality ultrasonic images of complex geometry pieces using industrial robots. The methodology is based on the calculation of a synchronism map after a calibration experiment and to introduce this corrected map in an autonomous, independent external system developed by the authors to obtain precise ultrasonic images. Therefore, it has been shown that it is possible to establish the synchronization of any industrial robot with any ultrasonic imaging generation system to generate high-quality ultrasonic images. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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24 pages, 1546 KiB  
Article
Research on Real-Time Robust Optimization of Perishable Supply-Chain Systems Based on Digital Twins
by Yingnian Wu, Jing Zhang, Qingkui Li and Hao Tan
Sensors 2023, 23(4), 1850; https://doi.org/10.3390/s23041850 - 07 Feb 2023
Cited by 3 | Viewed by 1900
Abstract
Aiming at the real-time robust optimization problem of perishable supply-chain systems in complex environments, a real-time robust optimization scheme based on supply-chain digital twins is proposed. Firstly, based on the quantitative logical relationship between production and sales of single-chain series supply-chain system products, [...] Read more.
Aiming at the real-time robust optimization problem of perishable supply-chain systems in complex environments, a real-time robust optimization scheme based on supply-chain digital twins is proposed. Firstly, based on the quantitative logical relationship between production and sales of single-chain series supply-chain system products, the state space equation of the supply-chain system with logical characteristics, structural characteristics, and quantitative characteristics was constructed, and twin data were introduced to construct the digital twins of supply chains based on the state-space equation. Secondly, the perishable supply-chain system in complex environments was regarded as an uncertain closed-loop system from the perspective of the state space equation, and then a robust H controller design strategy was proposed, and the supply-chain digital twins was used to update and correct the relevant parameters of the supply-chain system in real-time, to implement the real-time robust optimization based on the supply-chain digital twins. Finally, the simulation experiment was carried out with a cake supply-chain production as an example. The experimental results show that the real-time updating of relevant parameters through the digital twins can help enterprise managers to formulate reasonable management plans, effectively avoid the shortage problem of enterprises in the cake supply-chain system, and reduce the maximum inventory movement standard deviation of each link by 12.65%, 6.50%, and 14.87%, and the maximum production movement standard deviation by 70.21%, 56.84%, and 45.19%. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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25 pages, 3378 KiB  
Article
Development of an IEEE 1451 Plug-and-Play Module for Smart Transducers in Industrial Environments
by João Pinheiro, Diogo Oliveira, Luís Neto, Vítor H. Pinto and Gil Gonçalves
Sensors 2022, 22(20), 7880; https://doi.org/10.3390/s22207880 - 17 Oct 2022
Cited by 2 | Viewed by 1919
Abstract
The use of Sensors and Actuators is ubiquitous in an industrial environment. The advent of the Industrial Internet-of-Things (IIoT) and the 4th industrial revolution demands new, more intelligent and more efficient ways to be able to connect, read and control transducers at the [...] Read more.
The use of Sensors and Actuators is ubiquitous in an industrial environment. The advent of the Industrial Internet-of-Things (IIoT) and the 4th industrial revolution demands new, more intelligent and more efficient ways to be able to connect, read and control transducers at the plant floor level. Newer control and data science techniques also largely benefit from structured information endpoints available at the edge of the network. The IEEE 1451 standard presents architecture and methodology to solve these problems with the usage of smart transducers, introducing into edge devices concepts such as self-identification and standardization of data communication. In this work, a transducer interface module is developed using the IEEE 1451 standard focused on flexibility, ease of integration and plug-and-play features. Furthermore, a system architecture, based on IEEE 1451.0 is presented, and development and implementation features are explained. This system is then released as an open-source platform to help and motivate the usage of smart transducer systems. At last, the system is tested, results are collected, and a methodology and metrics are defined for comparison between different smart transducer systems. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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16 pages, 1181 KiB  
Article
A Plug-and-Play Solution for Smart Transducers in Industrial Applications Based on IEEE 1451 and IEC 61499 Standards
by Diogo Oliveira, João Pinheiro, Luís Neto, Vítor H. Pinto and Gil Gonçalves
Sensors 2022, 22(19), 7694; https://doi.org/10.3390/s22197694 - 10 Oct 2022
Cited by 4 | Viewed by 2162
Abstract
In a cyberphysical production system, the connectivity between the physical entities of a production system with the digital component that controls and monitors that system takes fundamental importance. This connectivity has been increasing from the transducers’ side, through gathering new functionalities and operating [...] Read more.
In a cyberphysical production system, the connectivity between the physical entities of a production system with the digital component that controls and monitors that system takes fundamental importance. This connectivity has been increasing from the transducers’ side, through gathering new functionalities and operating increasingly independently, taking the role of smart transducers, and from the applications’ side, by being developed in a distributed and decentralized paradigm. This work presents a plug-and-play solution capable of integrating smart transducers compliant with the IEEE 1451 standard in industrial applications based on the IEC 61499 standard. For this, we implemented the NCAP module of the smart transducer defined in IEEE 1451, which, when integrated with 4diac IDE and DINASORE (development and execution tools compliant with IEC 61499), enabled a solution that presented automatically the smart sensors and actuators in the IDE application and embedded their functionalities (access to data and processing functions) in the runtime environment. In this way, a complete plug-and-play solution was presented from the connection of the transducer to the network until its integration into the application. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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14 pages, 378 KiB  
Article
Model-Free Adaptive Iterative Learning Bipartite Containment Control for Multi-Agent Systems
by Shangyu Sang, Ruikun Zhang and Xue Lin
Sensors 2022, 22(19), 7115; https://doi.org/10.3390/s22197115 - 20 Sep 2022
Cited by 3 | Viewed by 1098
Abstract
This paper studies the bipartite containment tracking problem for a class of nonlinear multi-agent systems (MASs), where the interactions among agents can be both cooperative or antagonistic. Firstly, by the dynamic linearization method, we propose a novel model-free adaptive iterative learning control (MFAILC) [...] Read more.
This paper studies the bipartite containment tracking problem for a class of nonlinear multi-agent systems (MASs), where the interactions among agents can be both cooperative or antagonistic. Firstly, by the dynamic linearization method, we propose a novel model-free adaptive iterative learning control (MFAILC) to solve the bipartite containment problem of MASs. The designed controller only relies on the input and output data of the agent without requiring the model information of MASs. Secondly, we give the convergence condition that the containment error asymptotically converges to zero. The result shows that the output states of all followers will converge to the convex hull formed by the output states of leaders and the symmetric output states of leaders. Finally, the simulation verifies the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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