Advances on Automatic Control and Soft Computing from 15th APCA International Conference CONTROLO’2022

Topic Information

Dear Colleagues,

Nowadays there are several challenges related to automatic control systems, both from a theoretical point of view and from the point of view of real applications.

In the current context of industry 4.0, it is mandatory to develop advanced control methodologies, using optimization techniques, in order to improve the performance of processes and systems, trying to reduce operating costs.

This Topic is intended to share the state of the art of emerging approaches and novel techniques to find optimal solutions for open problems in the area of automatic control systems, namely: modeling, identification and simulation of dynamic systems, nonlinear systems, controllers design, fault detection and diagnosis approaches, fault tolerant control methodologies, among other related themes. 

This Topic has a wide field of application areas, such as: process control, vehicle control, industrial automation, robotics, among others. Original contributions, including experimental validation, are expected. The topics of interest include but are not limited to:

• Cyber-Physical Systems
• Internet-of-Things
• Modeling and simulation
• Advanced control algorithms
• Nonlinear systems
• Optimization
• Fault detection and diagnosis
• Fault tolerant control

Dr. Luis Gomes
Dr. Luís Brito Palma
Dr. Bruno J. N. Guerreiro
Dr. Anikó Costa
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Actuators actuators	2.2	3.9	2012	17.7 Days	CHF 2400
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Automation automation	-	2.9	2020	24.1 Days	CHF 1000
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600

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Published Papers (5 papers)

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All Actuators Applied Sciences Automation Electronics Sensors

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27 pages, 6517 KiB  

Open AccessArticle

An Approach for Modeling and Simulation of Virtual Sensors in Automatic Control Systems Using Game Engines and Machine Learning

by João Rosas, Luís Brito Palma and Rui Azevedo Antunes

Sensors 2024, 24(23), 7610; https://doi.org/10.3390/s24237610 - 28 Nov 2024

Cited by 1 | Viewed by 1309

Abstract

We live in an era characterized by Society 4.0 and Industry 4.0 where successive innovations that are more or less disruptive are occurring. Within this context, the modeling and simulation of dynamic supervisory and control systems require dealing with more sophistication and complexity, [...] Read more.

We live in an era characterized by Society 4.0 and Industry 4.0 where successive innovations that are more or less disruptive are occurring. Within this context, the modeling and simulation of dynamic supervisory and control systems require dealing with more sophistication and complexity, with effects in terms of development errors and higher costs. One of the most difficult aspects of simulating these systems is the handling of vision sensors. The current tools provide these sensors but in a specific and limited way. This paper describes a six-step approach to sensor virtualization. For testing the approach, a simulation platform based on game engines was developed. As contributions, the platform can simulate dynamic systems, including industrial processes with vision sensors. Furthermore, the proposed virtualization approach allows for the modeling of sensors in a systematic way, reducing the complexity and effort required to simulate this type of system. Full article

(This article belongs to the Topic Advances on Automatic Control and Soft Computing from 15th APCA International Conference CONTROLO’2022)

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13 pages, 3093 KiB  

Open AccessArticle

Cost Function Approach for Dynamical Component Analysis: Full Recovery of Mixing and State Matrix

by Knut Hüper, Markus Schlarb and Christian Uhl

Automation 2024, 5(3), 360-372; https://doi.org/10.3390/automation5030022 - 1 Aug 2024

Viewed by 1261

Abstract

A reformulation of the dynamical component analysis (DyCA) via an optimization-free approach is presented. The original cost function approach is converted into a numerical linear algebra problem, i.e., the computation of coupled singular-value decompositions. A simple algorithm is presented together with numerical experiments [...] Read more.

A reformulation of the dynamical component analysis (DyCA) via an optimization-free approach is presented. The original cost function approach is converted into a numerical linear algebra problem, i.e., the computation of coupled singular-value decompositions. A simple algorithm is presented together with numerical experiments to document the feasability of the approach. This methodology is able to recover the mixing and state matrices of multivariate signals from high-dimensional measured data fully. Full article

(This article belongs to the Topic Advances on Automatic Control and Soft Computing from 15th APCA International Conference CONTROLO’2022)

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17 pages, 4924 KiB  

Open AccessArticle

Integrating Machine Learning with Intelligent Control Systems for Flow Rate Forecasting in Oil Well Operations

by Bibars Amangeldy, Nurdaulet Tasmurzayev, Shona Shinassylov, Aksultan Mukhanbet and Yedil Nurakhov

Automation 2024, 5(3), 343-359; https://doi.org/10.3390/automation5030021 - 1 Aug 2024

Viewed by 2122

Abstract

This study addresses the integration of machine learning (ML) with supervisory control and data acquisition (SCADA) systems to enhance predictive maintenance and operational efficiency in oil well monitoring. We investigated the applicability of advanced ML models, including Long Short-Term Memory (LSTM), Bidirectional LSTM [...] Read more.

This study addresses the integration of machine learning (ML) with supervisory control and data acquisition (SCADA) systems to enhance predictive maintenance and operational efficiency in oil well monitoring. We investigated the applicability of advanced ML models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Momentum LSTM (MLSTM), on a dataset of 21,644 operational records. These models were trained to predict a critical operational parameter, FlowRate, which is essential for operational integrity and efficiency. Our results demonstrate substantial improvements in predictive accuracy: the LSTM model achieved an R² score of 0.9720, the BiLSTM model reached 0.9725, and the MLSTM model topped at 0.9726, all with exceptionally low Mean Absolute Errors (MAEs) around 0.0090 for LSTM and 0.0089 for BiLSTM and MLSTM. These high R² values indicate that our models can explain over 97% of the variance in the dataset, reflecting significant predictive accuracy. Such performance underscores the potential of integrating ML with SCADA systems for real-time applications in the oil and gas industry. This study quantifies ML’s integration benefits and sets the stage for further advancements in autonomous well-monitoring systems. Full article

(This article belongs to the Topic Advances on Automatic Control and Soft Computing from 15th APCA International Conference CONTROLO’2022)

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14 pages, 923 KiB  

Open AccessArticle

Event-Based Modeling of Input Signal Behaviors for Discrete-Event Controllers

by Luis Gomes, Diogo Natário, Anikó Costa, João-Paulo Barros and Rogério Campos-Rebelo

Appl. Sci. 2024, 14(12), 5289; https://doi.org/10.3390/app14125289 - 19 Jun 2024

Cited by 4 | Viewed by 753

Abstract

Controllers for discrete-event systems are commonly designed using state-based formalisms, like state diagrams and Petri nets. These formalisms are strongly supported by the concept of events, which, from an automation system perspective, can be associated with a simple change in the value of [...] Read more.

Controllers for discrete-event systems are commonly designed using state-based formalisms, like state diagrams and Petri nets. These formalisms are strongly supported by the concept of events, which, from an automation system perspective, can be associated with a simple change in the value of a signal or more complex behavioral evolutions of the signals. In this paper, the characterization of several types of events is proposed, associated with different types of signals, such as Boolean and multivalued signals. The major goal of this characterization is to improve the compactness of the model, benefiting the editing and visual interpretation of the graphical model but keeping precise execution semantics, which in turn allows for the use of computational tools covering the different stages of system development. The behavioral model of the controller is produced using a non-autonomous class of Petri nets, the IOPT nets, and the associated IOPT-Tools, which supports the specification, simulation, property verification, and automatic code generation ready to be deployed into implementation platforms. All the types of proposed events have a behavioral sub-model executed concurrently with the main model of the controller. An application example is provided to illustrate some of the advantages of the adoption of the proposed approach, encapsulating the behavioral dependencies on the evolution of input signals into events. Full article

(This article belongs to the Topic Advances on Automatic Control and Soft Computing from 15th APCA International Conference CONTROLO’2022)

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20 pages, 1068 KiB  

Open AccessArticle

Safe Robust Adaptive Motion Control for Underactuated Marine Robots

by G. Reza Nazmara and A. Pedro Aguiar

Sensors 2024, 24(12), 3974; https://doi.org/10.3390/s24123974 - 19 Jun 2024

Viewed by 936

Abstract

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources [...] Read more.

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources of uncertainties, ensuring the system’s safe and robust behavior holds paramount importance in the control literature. To address this concern, this paper introduces a control strategy designed to ensure robustness at both the kinematic and dynamic levels. By emphasizing the compensation for the system uncertainties, the design integrates a straightforward fuzzy system structure. To further ensure the system’s safety, a funnel surface is defined, followed by the design of a suitable nonlinear sliding surface as a function of the funnel and tracking error. Using Lyapunov theory, the study formally establishes the Semi-globally Practically Finite-time Stability of the closed-loop system, validated through simulations conducted on underactuated marine robots. Full article

(This article belongs to the Topic Advances on Automatic Control and Soft Computing from 15th APCA International Conference CONTROLO’2022)

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