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Advances in Artificial Intelligence Methods Applications in Industrial Control Systems
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Advances in Artificial Intelligence Methods Applications in Industrial Control Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 29859

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

Special Issue Editor

Prof. Dr. Emanuele Carpanzano

E-Mail Website
Guest Editor
Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, 6928 Manno, Switzerland
Interests: control systems; industrial automation; manufacturing; mass customization; artificial intelligence; human aspects
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) applications are considered today to be of increasing relevance for the future of industrial control systems. AI methods are applied more and more at different industrial control systems levels from single automation devices up to the real time control of complex machines, production processes and overall factories supervision and optimization. AI solutions are exploited with reference to different industrial control applications from sensor fusion methods to novel model predictive control techniques, from self-optimizing machines to collaborative robots, from factory adaptive automation systems to production supervisory control systems.

The aim of the present special issue is to provide an overview of novel applications of AI methods to industrial control systems, so as to improve the production systems self-learning capacities, their overall performance, the related process and product quality, the optimal use of resources and the industrial systems safety and resilience to varying boundary conditions and production requests.

Prof. Dr. Emanuele Carpanzano
Guest Editor

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Keywords

  • Control Systems
  • Industrial Automation
  • Artificial Intelligence
  • Machine Learning
  • Self Learning Machine Tools
  • Intelligent Collaborative Robots
  • Adaptive Production Systems

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Published Papers (8 papers)

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Editorial

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4 pages, 199 KiB  
Editorial
Editorial of the Special Issue “Advances in Artificial Intelligence Methods Applications in Industrial Control Systems”
by Emanuele Carpanzano
Appl. Sci. 2023, 13(1), 16; https://doi.org/10.3390/app13010016 - 20 Dec 2022
Viewed by 1020
Abstract
Today, Artificial Intelligence (AI) applications are considered to be of increasing relevance for the future of industrial control systems [...] Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methods Applications in Industrial Control Systems)

Research

Jump to: Editorial, Other

21 pages, 6816 KiB  
Article
Design of a NARX-ANN-Based SP Controller for Control of an Irrigation Main Canal Pool
by Ybrain Hernandez-Lopez, Raul Rivas-Perez and Vicente Feliu-Batlle
Appl. Sci. 2022, 12(18), 9180; https://doi.org/10.3390/app12189180 - 13 Sep 2022
Cited by 4 | Viewed by 1514
Abstract
The management of irrigation main canals are studied in this research. One way of improving this is designing an efficient automatic control system of the water that flows through the canal pools, which is usually carried out by PI controllers. However, since canal [...] Read more.
The management of irrigation main canals are studied in this research. One way of improving this is designing an efficient automatic control system of the water that flows through the canal pools, which is usually carried out by PI controllers. However, since canal pools are systems with large time delays and nonlinear hydrodynamics, these PIs are tuned in a very conservative way so that the closed-loop instability that may appear depending on the chosen operation regime is avoided. These controllers are inefficient because they have slow time responses. In order to obtain faster responses that remain stable independently of the operation regime, a control system that combines a Smith predictor, which is appropriate to control linear systems with large time delays, with a NARX artificial neural network (ANN), that models the nonlinear dynamics of the pools, is proposed. By applying system identification procedures, two nonlinear NARX-ANN-based models and a linear mathematical model of a real canal pool were obtained. These models were applied to implement a modified NARX-ANN-based SP controller and a conventional linear SP controller. Experimental results on our real canal pool showed that our modified NARX-ANN-based SP controller overcomes conventional linear SP controllers in both setpoint tracking and load disturbance rejection. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methods Applications in Industrial Control Systems)
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14 pages, 2076 KiB  
Article
Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network
by Yuzhan Wu, Chenlong Li, Changshun Yuan, Meng Li and Hao Li
Appl. Sci. 2022, 12(2), 682; https://doi.org/10.3390/app12020682 - 11 Jan 2022
Cited by 6 | Viewed by 1641
Abstract
Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as [...] Read more.
Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as a predictive model to construct a SUGV model and back propagation (BP) is taken as its learning algorithm. Second, the predictive control law is designed and the traditional objective function is improved to obtain a predictive objective function with a differential term. The optimal control quantity is given in real time through iterative optimization. Meanwhile, the stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, a tracking control experiment on the SUGV model is used to verify the effectiveness of the proposed scheme. For comparison, traditional MTN and Radial Basis Function (RBF) predictive control schemes are introduced. Moreover, a noise disturbance is considered. Experimental results show that the proposed scheme is effective, which ensures that the vehicle can quickly and accurately track the desired yaw velocity signal with good real-time, robustness, and convergence performance, and is superior to other comparison schemes. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methods Applications in Industrial Control Systems)
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18 pages, 2490 KiB  
Article
Decentralized Multi-Agent Control of a Manipulator in Continuous Task Learning
by Asad Ali Shahid, Jorge Said Vidal Sesin, Damjan Pecioski, Francesco Braghin, Dario Piga and Loris Roveda
Appl. Sci. 2021, 11(21), 10227; https://doi.org/10.3390/app112110227 - 1 Nov 2021
Cited by 9 | Viewed by 3253
Abstract
Many real-world tasks require multiple agents to work together. When talking about multiple agents in robotics, it is usually referenced to multiple manipulators in collaboration to solve a given task, where each one is controlled by a single agent. However, due to the [...] Read more.
Many real-world tasks require multiple agents to work together. When talking about multiple agents in robotics, it is usually referenced to multiple manipulators in collaboration to solve a given task, where each one is controlled by a single agent. However, due to the increasing development of modular and re-configurable robots, it is also important to investigate the possibility of implementing multi-agent controllers that learn how to manage the manipulator’s degrees of freedom (DoF) in separated clusters for the execution of a given application (e.g., being able to face faults or, partially, new kinematics configurations). Within this context, this paper focuses on the decentralization of the robot control action learning and (re)execution considering a generic multi-DoF manipulator. Indeed, the proposed framework employs a multi-agent paradigm and investigates how such a framework impacts the control action learning process. Multiple variations of the multi-agent framework have been proposed and tested in this research, comparing the achieved performance w.r.t. a centralized (i.e., single-agent) control action learning framework, previously proposed by some of the authors. As a case study, a manipulation task (i.e., grasping and lifting) of an unknown object (to the robot controller) has been considered for validation, employing a Franka EMIKA panda robot. The MuJoCo environment has been employed to implement and test the proposed multi-agent framework. The achieved results show that the proposed decentralized approach is capable of accelerating the learning process at the beginning with respect to the single-agent framework while also reducing the computational effort. In fact, when decentralizing the controller, it is shown that the number of variables involved in the action space can be efficiently separated into several groups and several agents. This simplifies the original complex problem into multiple ones, efficiently improving the task learning process. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methods Applications in Industrial Control Systems)
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24 pages, 1885 KiB  
Article
Manufacturing Execution System Integration through the Standardization of a Common Service Model for Cyber-Physical Production Systems
by Richárd Beregi, Gianfranco Pedone, Borbála Háy and József Váncza
Appl. Sci. 2021, 11(16), 7581; https://doi.org/10.3390/app11167581 - 18 Aug 2021
Cited by 28 | Viewed by 4688
Abstract
Digital transformation and artificial intelligence are creating an opportunity for innovation across all levels of industry and are transforming the world of work by enabling factories to embrace cutting edge Information Technologies (ITs) into their manufacturing processes. Manufacturing Execution Systems (MESs) are abandoning [...] Read more.
Digital transformation and artificial intelligence are creating an opportunity for innovation across all levels of industry and are transforming the world of work by enabling factories to embrace cutting edge Information Technologies (ITs) into their manufacturing processes. Manufacturing Execution Systems (MESs) are abandoning their traditional role of legacy executing middle-ware for embracing the much wider vision of functional interoperability enablers among autonomous, distributed, and collaborative Cyber-Physical Production System (CPPS). In this paper, we propose a basic methodology for universally modeling, digitalizing, and integrating services offered by a variety of isolated workcells into a single, standardized, and augmented production system. The result is a reliable, reconfigurable, and interoperable manufacturing architecture, which privileges Open Platform Communications Unified Architecture (OPC UA) and its rich possibilities for information modeling at a higher level of the common service interoperability, along with Message Queuing Telemetry Transport (MQTT) lightweight protocols at lower levels of data exchange. The proposed MES architecture has been demonstrated and validated in several use-cases at a research manufacturing laboratory of excellence for industrial testbeds. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methods Applications in Industrial Control Systems)
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14 pages, 6158 KiB  
Article
Digital Twin for Designing and Reconfiguring Human–Robot Collaborative Assembly Lines
by Niki Kousi, Christos Gkournelos, Sotiris Aivaliotis, Konstantinos Lotsaris, Angelos Christos Bavelos, Panagiotis Baris, George Michalos and Sotiris Makris
Appl. Sci. 2021, 11(10), 4620; https://doi.org/10.3390/app11104620 - 18 May 2021
Cited by 75 | Viewed by 7576
Abstract
This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital [...] Read more.
This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital twin in runtime, synthesizing data from multiple 2D–3D sensors in order to have up-to-date information about the actual production process. The model integrates both geometrical information and semantics. The model is used in combination with an artificial intelligence logic in order to derive alternative configurations of the production system. The overall approach is discussed with the help of a case study coming from the automotive industry. The case study introduces a production system integrating humans and autonomous mobile dual arm workers. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methods Applications in Industrial Control Systems)
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22 pages, 421 KiB  
Article
Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration
by Dengguo Xu, Qinglin Wang and Yuan Li
Appl. Sci. 2021, 11(5), 2312; https://doi.org/10.3390/app11052312 - 5 Mar 2021
Cited by 7 | Viewed by 2177
Abstract
In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal [...] Read more.
In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methods Applications in Industrial Control Systems)
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Other

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19 pages, 2928 KiB  
Perspective
Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems
by Emanuele Carpanzano and Daniel Knüttel
Appl. Sci. 2022, 12(21), 10962; https://doi.org/10.3390/app122110962 - 29 Oct 2022
Cited by 13 | Viewed by 6097
Abstract
Industrial control systems play a central role in today’s manufacturing systems. Ongoing trends towards more flexibility and sustainability, while maintaining and improving production capacities and productivity, increase the complexity of production systems drastically. To cope with these challenges, advanced control algorithms and further [...] Read more.
Industrial control systems play a central role in today’s manufacturing systems. Ongoing trends towards more flexibility and sustainability, while maintaining and improving production capacities and productivity, increase the complexity of production systems drastically. To cope with these challenges, advanced control algorithms and further developments are required. In recent years, developments in Artificial Intelligence (AI)-based methods have gained significantly attention and relevance in research and the industry for future industrial control systems. AI-based approaches are increasingly explored at various industrial control systems levels ranging from single automation devices to the real-time control of complex machines, production processes and overall factories supervision and optimization. Thereby, AI solutions are exploited with reference to different industrial control applications from sensor fusion methods to novel model predictive control techniques, from self-optimizing machines to collaborative robots, from factory adaptive automation systems to production supervisory control systems. The aim of the present perspective paper is to provide an overview of novel applications of AI methods to industrial control systems on different levels, so as to improve the production systems’ self-learning capacities, their overall performance, the related process and product quality, the optimal use of resources and the industrial systems safety, and resilience to varying boundary conditions and production requests. Finally, major open challenges and future perspectives are addressed. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methods Applications in Industrial Control Systems)
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