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Processes
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Recent Developments in Automatic Control and Systems Engineering
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Recent Developments in Automatic Control and Systems Engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 13638

Special Issue Editors

Dr. Anthony Rossiter

E-Mail Website
Guest Editor
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK
Interests: predictive control; LPV systems; interpolation; computational simplicity feasibility
Prof. Dr. Maurício Bezerra De Souza Jr.

E-Mail Website
Guest Editor
Chemical Engineering Department, Universidade Federal do Rio de Janeiro, (UFRJ), Rio de Janeiro, RJ, Brazil
Interests: process control; data-based methods for monitoring and control; fault detection and diagnosis; process system engineering; artificial intelligence; neural networks; autonomous systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Processes (ISSN 2227-9717) provides an advanced forum for process/system-related research, and in particular provides numerous Special Issues to give focus. This Special Issue gives focus to automatic control and, in particular, an opportunity to capture recent thinking in the academic and industrial communities.

The intention is that the Special Issue will contain a number of survey papers, alongside those which are narrower in focus and perhaps are future-looking with some novel elements, but still contain an element of survey contribution in their writing. Surveys could cover a broad range of issues, including those focusing on: i) application; ii) control methodology; iii) education; iv) industrial practice; and v) more.

Please speak to the Editor if you are not sure about whether your proposal fits the scope of the Issue.

Dr. Anthony Rossiter
Prof. Dr. Maurício Bezerra De Souza Jr.
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.

Keywords

  • automatic control
  • control design
  • survey
  • industrial control practice

Published Papers (6 papers)

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Research

Jump to: Review

33 pages, 4621 KiB  
Article
Virtual Test Beds for Image-Based Control Simulations Using Blender
by Akkarakaran Francis Leonard, Govanni Gjonaj, Minhazur Rahman and Helen E. Durand
Processes 2024, 12(2), 279; https://doi.org/10.3390/pr12020279 - 27 Jan 2024
Viewed by 652
Abstract
Process systems engineering research often utilizes virtual testbeds consisting of physicsbased process models. As machine learning and image processing become more relevant sensing frameworks for control, it becomes important to address how process systems engineers can research the development of control and analysis [...] Read more.
Process systems engineering research often utilizes virtual testbeds consisting of physicsbased process models. As machine learning and image processing become more relevant sensing frameworks for control, it becomes important to address how process systems engineers can research the development of control and analysis frameworks that utilize images of physical processes. One method for achieving this is to develop experimental systems; another is to use software that integrates the visualization of systems, as well as modeling of the physics, such as three-dimensional graphics software. The prior work in our group analyzed image-based control for the small-scale example of level in a tank and hinted at some of its potential extensions, using Blender as the graphics software and programming the physics of the tank level via the Python programming interface. The present work focuses on exploring more practical applications of image-based control. Specifically, in this work, we first utilize Blender to demonstrate how a process like zinc flotation, where images of the froth can play a key role in assessing the quality of the process, can be modeled in graphics software through the integration of visualization and programming of the process physics. Then, we demonstrate the use of Blender for testing image-based controllers applied to two other processes: (1) control of the stochastic motion of a nanorod as a precursor simulation toward image-based control of colloidal self-assembly using a virtual testbed; and (2) controller updates based on environment recognition to modify the controller behavior in the presence of different levels of sunlight to reduce the impacts of environmental disturbances on the controller performance. Throughout, we discuss both the setup used in Blender for these systems, as well as some of the features when utilizing Blender for such simulations, including highlighting cases where non-physical parameters of the graphics software would need to be assumed or tuned to the needs of a given process for the testbed simulation. These studies highlight benefits and limitations of this framework as a testbed for image-based controllers and discuss how it can be used to derive insights on image-based control functionality without the development of an experimental testbed. Full article
(This article belongs to the Special Issue Recent Developments in Automatic Control and Systems Engineering)
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22 pages, 2029 KiB  
Article
Embedded MPC Strategies for ESP-Lifted Oil Wells: Hardware-in-the-Loop Performance Analysis of Nonlinear and Robust Techniques
by Bruno A. Santana, Victor S. Matos, Daniel D. Santana and Márcio A. F. Martins
Processes 2023, 11(5), 1354; https://doi.org/10.3390/pr11051354 - 28 Apr 2023
Viewed by 1127
Abstract
This paper proposes embedded model predictive control strategies for oil-production processes equipped with electric submersible pump (ESP) installations. The novelty of this paper is the robustness and computational performance analysis of the Robust Infinite-Horizon Model Predictive Controller (RIHMPC) and Nonlinear Model Predictive Controller [...] Read more.
This paper proposes embedded model predictive control strategies for oil-production processes equipped with electric submersible pump (ESP) installations. The novelty of this paper is the robustness and computational performance analysis of the Robust Infinite-Horizon Model Predictive Controller (RIHMPC) and Nonlinear Model Predictive Controller (NMPC) strategies, which have not yet been documented by the oil and gas exploration and production literature. The proposed method to embed the control laws is flexible with different hardware and is based on automatic code generation, which facilitates the project workflow. Hardware-in-the-loop simulation cases were used to compare the performance of both control strategies embedded in the Teensy 4.1 microcontroller, using key indices for real applications. The results showed that the RIHMPC strategy is a very promising alternative for real-time operation in ESP-lifted oil wells, with overall performance similar to the NMPC controller, even in noisy and plant–model mismatch scenarios, and using only linear models in its formulation. Full article
(This article belongs to the Special Issue Recent Developments in Automatic Control and Systems Engineering)
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18 pages, 1068 KiB  
Article
Unrestricted Horizon Predictive Controller Applied in a Biphasic Oil Separator under Periodic Slug Disturbances
by Rodrigo Trentini, Alexandre Campos, Marcos Antonio Salvador, Yuri Matheus Scheuer and Carlos Henrique Farias dos Santos
Processes 2023, 11(3), 928; https://doi.org/10.3390/pr11030928 - 18 Mar 2023
Viewed by 1116
Abstract
Multi-phase flow, characterised by the presence of both liquid and gas phases, often occurs in riser pipes during oil extraction. These flows can be problematic because they can cause oscillations due to the formation of bubbles within the pipes, which can negatively impact [...] Read more.
Multi-phase flow, characterised by the presence of both liquid and gas phases, often occurs in riser pipes during oil extraction. These flows can be problematic because they can cause oscillations due to the formation of bubbles within the pipes, which can negatively impact the safety and efficiency of offshore production operations. One solution to this problem is to use a gravitational oil separator, which is designed to dampen these oscillations. The separator is equipped with a control system that uses liquid level and gas pressure sensors to stabilise the flow by adjusting the positions of its valves. This paper presents the use of a specific type of model-based predictive controller to control the level and pressure of a biphasic oil separator, particularly in the presence of slug disturbances. The designs of the separator model and controller are discussed in detail, with a focus on the advantages of using an unrestricted horizon predictive controller, such as its ability to make predictions over a long horizon and its relatively low computational requirements. For the sake of comparison, a linear quadratic regulator is also evaluated. The simulation results demonstrate that the proposed control system is able to effectively regulate the separator’s liquid level and gas pressure within a magnitude range of 10−4 m for the liquid level and 10−3 bar for the internal pressure. Aside from that, the dynamics of the closed-loop system is six times faster than the plant’s for the liquid behaviour and 30 times faster for the pressure, while also presenting sharp attenuation characteristics for the input disturbances of nearly 50 dB for the pressure output and 68 dB for the liquid level. Full article
(This article belongs to the Special Issue Recent Developments in Automatic Control and Systems Engineering)
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15 pages, 2976 KiB  
Article
Experimental Study on Constant Speed Control Technology of Hydraulic Drive Pavers
by Xiujie Yin, Shujun Yin, Hong Zhu and Zhihao Zhang
Processes 2023, 11(2), 477; https://doi.org/10.3390/pr11020477 - 04 Feb 2023
Cited by 1 | Viewed by 1230
Abstract
The paver needs superior constant speed performance when paving the pavement. In order to effectively reduce the paver speed fluctuation of the paver, and the wandering deviation from the predetermined track during the paving operation, a control scheme of paver travelling system based [...] Read more.
The paver needs superior constant speed performance when paving the pavement. In order to effectively reduce the paver speed fluctuation of the paver, and the wandering deviation from the predetermined track during the paving operation, a control scheme of paver travelling system based on GNSS, Global Navigation Satellite System, is proposed; the scheme can realize open-loop control, closed-loop control, and deviation correction control according to the driver’s choice. During closed-loop control, the setting value and the PID controller output of the left wheel are combined to control the speed of the left wheel, as is the closed-loop control of the right wheel. During the deviation correction control, the coordinate provided by the RTK GNSS receiver and the predetermined trajectory line are used to calculate the lateral deviation of the paver. The lateral deviation is input to the right wheel navigation correction PID algorithm. After the calculation, the correction value of the right wheel speed is obtained, which is input to the right wheel PID controller for the deviation correction control. In this paper, the low constant speed performance of the paver, such as during straight driving, turning driving, and driving when resistance changing, was studied by means of experiments. The test results show that when the test paver was running at a speed of more than 2 m/min, the average speed was almost the same. The higher the average speed was, the more stable the speed was. When the paver was less than 1 m/min, its speed fluctuation tended to increase, and its constant speed performance could not be guaranteed. When the test paver hit a movable obstacle at a speed of 5 m/min, which changed the driving resistance, the average speed of the left and right wheels decreased significantly, with a change of about 2.8%, and there was no significant change in the speed fluctuation of the left and right wheels. At the same time, the wandering deviation test proves that the strait-line travelling wandering deviation was basically controlled within 2.5 cm. Without driver intervention, the wandering deviation of the test paver travelling 50 m decreased by about 97.4%, and the constant speed control fluctuation was within 0.2% when the paver travelled at the speed of 5 m/min. Full article
(This article belongs to the Special Issue Recent Developments in Automatic Control and Systems Engineering)
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16 pages, 2712 KiB  
Article
Deep Transfer Learning for Approximate Model Predictive Control
by Samuel Arce Munoz, Junho Park, Cristina M. Stewart, Adam M. Martin and John D. Hedengren
Processes 2023, 11(1), 197; https://doi.org/10.3390/pr11010197 - 07 Jan 2023
Cited by 4 | Viewed by 2951
Abstract
Transfer learning is a machine learning technique that takes a pre-trained model that has already been trained on a related task, and adapts it for use on a new, related task. This is particularly useful in the context of model predictive control (MPC), [...] Read more.
Transfer learning is a machine learning technique that takes a pre-trained model that has already been trained on a related task, and adapts it for use on a new, related task. This is particularly useful in the context of model predictive control (MPC), where deep transfer learning is used to improve the training of the MPC by leveraging the knowledge gained from related controllers. One way in which transfer learning is applied in the context of MPC is by using a pre-trained deep learning model of the MPC, and then fine-tuning the controller training for a new process automation task. This is similar to how an equipment operator quickly learns to manually control a new processing unit because of related skills learned from controlling the prior unit. This reduces the amount of data required to train the approximate MPC controller, and also improves the performance on the target system. Additionally, learning the MPC actions alleviates the computational burden of online optimization calculations, although this approach is limited to learning from systems where an MPC has already been developed. The paper reviews approximate MPC formulations with a case study that illustrates the use of neural networks and transfer learning to create a multiple-input multiple-output (MIMO) approximate MPC. The performance of the resulting controller is similar to that of a controller trained on an existing MPC, but it requires less than a quarter of the target system data for training. The main contributions of this paper are a summary survey of approximate MPC formulations and a motivating case study that includes a discussion of future development work in this area. The case study presents an example of using neural networks and transfer learning to create a MIMO approximate MPC and discusses the potential for further research and development in this area. Overall, the goal of this paper is to provide an overview of the current state of research in approximate MPC, as well as to inspire and guide future work in transfer learning. Full article
(This article belongs to the Special Issue Recent Developments in Automatic Control and Systems Engineering)
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Review

Jump to: Research

31 pages, 1247 KiB  
Review
Where Reinforcement Learning Meets Process Control: Review and Guidelines
by Ruan de Rezende Faria, Bruno Didier Olivier Capron, Argimiro Resende Secchi and Maurício B. de Souza, Jr.
Processes 2022, 10(11), 2311; https://doi.org/10.3390/pr10112311 - 06 Nov 2022
Cited by 11 | Viewed by 5367
Abstract
This paper presents a literature review of reinforcement learning (RL) and its applications to process control and optimization. These applications were evaluated from a new perspective on simulation-based offline training and process demonstrations, policy deployment with transfer learning (TL) and the challenges of [...] Read more.
This paper presents a literature review of reinforcement learning (RL) and its applications to process control and optimization. These applications were evaluated from a new perspective on simulation-based offline training and process demonstrations, policy deployment with transfer learning (TL) and the challenges of integrating it by proposing a feasible approach to online process control. The study elucidates how learning from demonstrations can be accomplished through imitation learning (IL) and reinforcement learning, and presents a hyperparameter-optimization framework to obtain a feasible algorithm and deep neural network (DNN). The study details a batch process control experiment using the deep-deterministic-policy-gradient (DDPG) algorithm modified with adversarial imitation learning. Full article
(This article belongs to the Special Issue Recent Developments in Automatic Control and Systems Engineering)
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Displaying articles 1-6

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A Review of Control Applied for Materials and Materials Manufacturing
Authors: Dr. Helen Durand
Affiliation: Department of Electrical and Computer Engineering, Wayne State University, USA

Title: A Modular Industrial Cyber-Physical Systems for Smart Factories
Authors: Dr. Jerome Mendes
Affiliation: University of Coimbra, Centre for Mechanical Engineering, Materials and ProcessesThis link is disabled., Coimbra, Portugal

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