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Processes
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Model Based, Data Driven Identification and Control for Developing Intelligent...
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Model Based, Data Driven Identification and Control for Developing Intelligent and Smart Processes and Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 10 October 2024 | Viewed by 7314

Special Issue Editors

Prof. Dr. Valentina E. Balas

E-Mail Website
Guest Editor
Department of Automatics and Applied Software, Faculty of Engineering, Aurel Vlaicu University of Arad, Ro-310025 Arad, Romania
Interests: intelligent systems; soft computing; fuzzy control; modeling and simulation; nonlinear systems
Special Issues, Collections and Topics in MDPI journals
Dr. Rajeeb Dey

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Institute of Technology, Silchar, Assam, India
Interests: stability of time-delay systems; control of time-delay system; robust control; time-delay control applications; intelligent control

Special Issue Information

Dear Colleagues,

There is a growing demand for effective and efficient automation in almost every industrial sector around the world due to competitive consumer market requirements and to fulfil the mandate of the United Nations’ Sustainable Development Goals (SDGs) for the people. To fulfil these needs, the automation of processes and systems play a key role to evolve sustainable technologies to meet the growing need of the society. Subsequently, processes and systems are becoming more and more complex owing to its interaction with the affordable high-speed digital communication infrastructure, cheap high-power computing devices (or processors) and the availability of big data or information. Thus, these steep requirements of automation have led researchers to innovate, investigate and further create intelligent and smart systems. The creation of such intelligent processes and systems cannot be achieved by restricting ourselves to conventional theories of control and identification; rather, there is a need to blend the conventional theories of control and identification with the evolving theories of information sciences, data sciences, artificial intelligence (AI), computational sciences and the sense of communication sciences to evolve a realisable technology for automation of the complex systems and processes. In this Special Issue, the complex systems arising from biomedical sciences, robotic systems and energy systems are the main focus as they address some of the main SDGs out of the 17 goals. Further, the emphasis will be placed to the development of new identification and control techniques or methods that can eventually make the process or system under study intelligent and (or) smart. This Special Issue will publish very high-quality, novel multi-disciplinary research results on identification and control on the identified application areas. The conventional identification and control methods without the motivation and application for the present need of automation in industries and society are not encouraged for submission, as we consider that no methods of control, communication, and computation can work in silo in the present-day scenario. The papers submitted to this Special Issue must include sufficient mathematical and scientific justification to validate the results and a mere explanation of the proof of concept of the technology will not be accepted for review. The word control and identification included in the scope signifies the development of new methods for the complexities included in the processes and systems. The following potential topics are welcomes for this Special Issue, but are not limited to:

Power and Energy Systems:

  • Power System Stability and Reliability;
  • Modelling and control of prosumer resources;
  • Decentralized control and management;
  • Intelligent control in smart grids;
  • Wide-area monitoring and control;
  • Design of energy markets for smart grids;
  • Cyber-physical and cybersecurity power grid applications;
  • Data analytics and big data applications to microgrids and ADNs;
  • Application of telecommunication technologies to power systems;
  • Smart sensors, meters, and Advance Metering Infrastructure (AMI);
  • Fuzzy modelling and control techniques for intelligent power system;
  • Adaptive control schemes using AI based techniques for Power and Energy Systems.

Bio-Processes and Systems:

  • Modelling and Control of biomedical systems with emphasis to complex and realistic application;
  • New data-driven techniques for the identification and control of biomedical systems;
  • AI-based biomedical imaging techniques;
  • Bio-medical instrumentation for industrial automation;
  • Smart and intelligent assistive technology for elderly people;
  • Application of control methods for the automation of agricultural industries;
  • Automation results from clinical trials;
  • Development of data-driven or mathematical models for in silico applications as per medical or health standards.

Robotics and Manufacturing Processes and Systems:

  • Robotics for industrial automation;
  • Social and service robotics;
  • Multi-agent robotic systems;
  • Machine intelligence in robotics;
  • Medical robotics;
  • Unmanned systems;
  • Reinforced learning;
  • Networked control systems;
  • Mechatronic systems;
  • Consensus control algorithms for cooperative control;
  • Bilateral teleoperation with communication constraints.

Prof. Dr. Valentina E. Balas
Dr. Rajeeb Dey
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. 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.

Published Papers (4 papers)

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Research

13 pages, 10709 KiB  
Article
A Convolutional Fuzzy Neural Network Active Noise Cancellation Approach without Error Sensors for Autonomous Rail Rapid Transit
by Tao Li, Yuyao He, Minqi Wang, Kaihui Zhao, Ning Wang, Weihua Gui, Jianghua Feng and Jun Yang
Processes 2023, 11(9), 2576; https://doi.org/10.3390/pr11092576 - 28 Aug 2023
Viewed by 953
Abstract
Autonomous rail rapid transit (ART) is a new type of multiunit, articulated, rubber-wheeled urban transport system. The noise sources of ART have significant time-varying characteristics. It is unsuitable to track the error signal by installing too many error sensors, which poses a significant [...] Read more.
Autonomous rail rapid transit (ART) is a new type of multiunit, articulated, rubber-wheeled urban transport system. The noise sources of ART have significant time-varying characteristics. It is unsuitable to track the error signal by installing too many error sensors, which poses a significant challenge in the active noise control of ART. Thus, this paper proposes a convolutional fuzzy neural network-based active noise cancellation approach without error sensors to solve this problem. The proposed approach utilizes convolutional neural network (CNN) to extract the noise signal characteristics of ART and trains multiple noise source signals using a CNN to estimate the virtual error signal in the target area. In addition, the proposed approach adopts fuzzy neural network (FNN) for adaptive adjustment of filter weight coefficients to achieve real-time noise tracking control with fast convergence and small error in the convergence process. The experimental results demonstrate that the proposed approach can effectively reduce ART low-frequency noise without error sensors, and the average sound pressure level in the target area is reduced more compared with conventional approaches. Full article
(This article belongs to the Special Issue Model Based, Data Driven Identification and Control for Developing Intelligent and Smart Processes and Systems)
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22 pages, 9243 KiB  
Article
Nonlinear Adaptive Generalized Predictive Control for PH Model of Nutrient Solution in Plant Factory Based on ANFIS
by Yonggang Wang, Ning Zhang, Chunling Chen, Yingchun Jiang and Tan Liu
Processes 2023, 11(8), 2317; https://doi.org/10.3390/pr11082317 - 2 Aug 2023
Viewed by 794
Abstract
A plant factory is typically considered to be an exceedingly advanced product management system characterized by higher crop yields and better quality control. The pH value of the nutrient solution is crucial for determining the health and productivity of crops. However, the nutrient [...] Read more.
A plant factory is typically considered to be an exceedingly advanced product management system characterized by higher crop yields and better quality control. The pH value of the nutrient solution is crucial for determining the health and productivity of crops. However, the nutrient solution process exhibits inherent complexity, such as parameters uncertainty, multi-disturbances, and strong nonlinearity. Therefore, the traditional control method cannot meet the necessary requirements. The main objective of this paper is to address the issues of parameter uncertainty, strong nonlinearity, and multiple disturbances in the regulation process of the nutrient solution while achieving accurate control of the nutrient solution pH in a plant factory. This is performed so that a dynamic model of a nutrient solution for pH is developed and a nonlinear adaptive controller is presented, which comprises a linear adaptive generalized predictive controller, a nonlinear adaptive generalized predictive controller, and a switching mechanism. The parameters of the controller are adjusted by generalized predictive control (GPC) laws. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is used to estimate the unmodeled dynamics to depress the influence of nonlinearity on the system. The experiments show that the mean errors and standard errors for gain-scheduling the proportional-integral-derivative (PID) control strategy are 0.1388 and 0.4784, respectively. The mean errors and standard errors for the nonlinear adaptive controller are 0.1046 and 0.3009, respectively. Simulation results indicate that the presented method can acquire a better control effect in the case of various complex situations. Therefore, by achieving precise control of the pH value, it is possible to provide a suitable growth environment for crops, promoting healthy crop growth and increasing crop yield. Full article
(This article belongs to the Special Issue Model Based, Data Driven Identification and Control for Developing Intelligent and Smart Processes and Systems)
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19 pages, 7218 KiB  
Article
Using Ant Colony Optimization as a Method for Selecting Features to Improve the Accuracy of Measuring the Thickness of Scale in an Intelligent Control System
by Abdulilah Mohammad Mayet, V. P. Thafasal Ijyas, Javed Khan Bhutto, John William Grimaldo Guerrero, Neeraj Kumar Shukla, Ehsan Eftekhari-Zadeh and Hala H. Alhashim
Processes 2023, 11(6), 1621; https://doi.org/10.3390/pr11061621 - 26 May 2023
Cited by 1 | Viewed by 1788
Abstract
The scaling of oil pipelines over time leads to issues including diminished flow rates, wasted energy, and decreased efficiency. To take appropriate action promptly and avoid the aforementioned issues, it is crucial to determine the precise value of the scale within the pipe. [...] Read more.
The scaling of oil pipelines over time leads to issues including diminished flow rates, wasted energy, and decreased efficiency. To take appropriate action promptly and avoid the aforementioned issues, it is crucial to determine the precise value of the scale within the pipe. Non-invasive gamma attenuation systems are one of the most accurate detection methods. To accomplish this goal, the Monte Carlo N Particle (MCNP) algorithm was used to simulate a scale thickness measurement system, which included two sodium iodide detectors, a dual-energy gamma source (241 Am and 133 Ba radioisotopes), and a test pipe. Water, gas, and oil were all used to mimic a three-phase flow in the test pipe, with the volume percentages ranging from 10% to 80%. Moreover, a scale ranging in thickness from 0 to 3 cm was inserted into the pipe, gamma rays were shone on the pipe, and on the opposite side of the pipe, photon intensity was measured by detectors. There were 252 simulations run. Fifteen time and frequency characteristics were derived from the signals collected by the detectors. The ant colony optimisation (ACO)-based approach is used to pick the ideal inputs from among the extracted characteristics for determining the thickness of the scale within the pipe. This technique led to the introduction of thirteen features that represented the ideal combination. The features introduced by ACO were introduced as inputs to a multi-layer perceptron (MLP) neural network to predict the scale thickness inside the oil pipe in centimetres. The maximum error found in calculating scale thickness was 0.017 as RMSE, which is a minor error compared to earlier studies. The accuracy of the present study in detecting scale thickness has been greatly improved by using the ACO to choose the optimal features. Full article
(This article belongs to the Special Issue Model Based, Data Driven Identification and Control for Developing Intelligent and Smart Processes and Systems)
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12 pages, 3757 KiB  
Article
Research on the Optimization of A/B Testing System Based on Dynamic Strategy Distribution
by Jinfang Sheng, Huadan Liu and Bin Wang
Processes 2023, 11(3), 912; https://doi.org/10.3390/pr11030912 - 17 Mar 2023
Cited by 1 | Viewed by 3148
Abstract
With the development of society, users have increasing requirements for the high-quality experience of products. The pursuit of a high profit conversion rate also gradually puts forward higher requirements for product details in the competition. Product providers need to iterate products fast and [...] Read more.
With the development of society, users have increasing requirements for the high-quality experience of products. The pursuit of a high profit conversion rate also gradually puts forward higher requirements for product details in the competition. Product providers need to iterate products fast and with a high quality to enhance user viscosity and activity to improve the profit conversion rate efficiently. A/B testing is a technical method to conduct experiments on target users who use different iterative strategies, and observe which strategy is better through log embedding and statistical analysis. Usually, different businesses of the same company are supported by different business systems, and the A/B tests of different business systems need to be operated in a unified manner. At present, most A/B testing systems cannot provide services for more than one business system at the same time, and there are problems such as high concurrency, scalability, reusability, and flexibility. In this regard, this paper proposes an idea of dynamic strategy distribution, based on which a configuration-driven traffic-multiplexing A/B testing model is constructed and implemented systematically. The model solves the high-concurrency problem when requesting experimental strategies by setting message middleware and strategy cache modules, making the system more lightweight, flexible, and efficient to meet the A/B testing requirements for multiple business systems. Full article
(This article belongs to the Special Issue Model Based, Data Driven Identification and Control for Developing Intelligent and Smart Processes and Systems)
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