Submit to Mathematics Review for Mathematics Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Modeling, Simulation, Control and Optimization in Engineering with Applications

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Related Special Issue
Published Papers

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 12713

Share This Special Issue

Special Issue Editors

Prof. Dr. Montserrat Gil-Martinez

E-Mail Website
Guest Editor

Control Engineering Research Group, Electrical Engineering Department, University of La Rioja, Logroño, Spain
Interests: automatic control; control theory; robust control; quantitative feedback theory (QFT); unmanned aerial vehicles; autopilot; machine learning; wastewater control systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ramón Vilanova Arbós

E-Mail Website
Guest Editor

Department of Telecommunication and System Engineering, Universitat Autonoma de Barcelona, Barcelona, Spain
Interests: wastewater control systems; PID control systems; event-based control; systems with uncertainty; analysis of control systems with several degrees of freedom; application to environmental systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Marian Barbu

E-Mail Website
Guest Editor

Department of Automation and Electrical Engineering, Dunarea de Jos University of Galati, Galati, Romania
Interests: wastewater control systems; control of integrated water systems; data-driven control; application to environmental systems; application to energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The level of maturity achieved within the fields of information technologies, advanced programming and computer science has allowed a much-simplified application of modeling, simulation and, especially, optimization techniques. This fact has made modeling, simulation and optimization (MSO) a mandatory stage before any experimental application to solve engineering problems. Furthermore, MSO reveals a special impact on the design of control systems, where the final deploying step becomes even more confident and guarantees a high degree of complement with the expected control system performance. Optimization allows complete approaches to design, including realistic constraints, the use of detailed nonlinear models of the system under control, or considering multiobjective problems, among others. Optimization and modeling constitute also the core components of artificial intelligence and the popular machine learning algorithms. In this framework, this Special Issue on modeling, simulation, control and optimization (MSCO) aims to gather a collection of case studies, examples of application, and new optimization and simulation-based techniques specially oriented to facilitate the controller design task and its success behavior.

Topics include but are not limited to the following:

Mathematical modeling of physical systems.
Simulation and optimization software.
Computational processes in modeling, simulation and optimization.
Optimisation approaches for control systems design.
Optimisation and modeling in artificial intelligence.
Modeling, simulation and optimization of coupled problems.
Modeling and simulation-based decision support systems.
Defining synthetic environments for engineering problems.
Model predictive control.
Robust and adaptive control.
Modeling, simulation, control and optimization of industrial processes, electrical and energy systems, or transport systems.
Modelling, control, navigation and guidance of unmanned vehicles.

Prof. Dr. Montserrat Gil-Martinez
Prof. Dr. Ramón Vilanova Arbós
Prof. Dr. Marian Barbu
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. Mathematics 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

control systems
multiobjective optimization
modelling
simulation
optimal control
stochastic control
time-varying systems
robust-adaptive control
model-predictive control
fuzzy systems
numerical methods
fault detection
fault tolerance
data-driven control
evolutionary computation
machine learning
parameter estimation
uncertainty quantification

Related Special Issue

Modeling, Simulation, Control and Optimization in Engineering with Applications, 2nd Edition in Mathematics

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

22 pages, 5713 KiB

Open AccessArticle

L1 Adaptive Control for Marine Structures

by Jose Joaquin Sainz, Victor Becerra, Elías Revestido Herrero, Jose Ramon Llata and Francisco J. Velasco

Mathematics 2023, 11(16), 3554; https://doi.org/10.3390/math11163554 - 17 Aug 2023

Viewed by 631

Abstract

Nowadays, many maritime structures require precise dynamic positioning (DP) of the constructive elements that compose them. In addition, the use of preconstructed elements that are later moved to the final location has become widespread. These operations have not been automated with the risks involved in carrying out the complex operations required. To minimize these operational risks and to perform a correct DP of floating structures, a new approach based on the L1 adaptive control technique is proposed. As an example of application, a proposed L1 adaptive controller was implemented in the dynamic positioning of a floating caisson. Several simulations of the system with wave disturbances were carried out, and the results were compared with those obtained by applying other classical and advanced control techniques, such as linear quadratic Gaussian control (LQG) and model predictive control (MPC). It was concluded that the proposed L1 adaptive controller performs correct dynamic positioning and reduces the tension generated on the lines concerning the other advanced control techniques with which it was compared. This reduction in line tension leads to an important improvement due to the possibility of reducing the size of the actuators or reducing their number, with the important economic and safety repercussions that these actions entail. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications)

► Show Figures

Figure 1

22 pages, 7089 KiB

Open AccessArticle

Enhancing the Performance of a Simulated WWTP: Comparative Analysis of Control Strategies for the BSM2 Model

by Bogdan Roșu, George Dănuț Mocanu, Mihaela Munteanu Pila, Gabriel Murariu, Adrian Roșu and Maxim Arseni

Mathematics 2023, 11(16), 3471; https://doi.org/10.3390/math11163471 - 10 Aug 2023

Viewed by 669

Abstract

This study aimed to improve the performance of a wastewater treatment plant (WWTP) simulated with Benchmark Model No. 2 (BSM2). To achieve this objective, three control strategies were implemented and tested. The first control strategy aimed to maintain the concentration of nitrate and nitrite nitrogen (S_NO) by controlling the external carbon flowrate (strategy A₁), and the second control strategy aimed to maintain the ammonia and ammonium nitrogen (S_NH) at a desired level with the use of a cascade controller (strategy A₂). The third strategy was applied to control the total suspended solids (TSS) (strategy A₃). Combinations of these strategies were considered (B₁, B₂, and B₃ strategies), as well as the use of all three together (strategy C₁). The control strategies presented in this paper were compared to the default control strategy of BSM2 to validate and identify the one that provided the best performance. The results revealed that the B₁ strategy was the most environmentally friendly, while C₁ obtained the highest overall performance. Several Monte Carlo simulations were performed for the validated control strategies, to identify the optimal setpoint values. For the C₁ strategy, a second method of optimization regarding polynomial interpolation was considered. The applied optimization methods provided the optimal reference values for the PI (proportional integral) controllers. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications)

► Show Figures

Figure 1

25 pages, 21161 KiB

Open AccessArticle

Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent

by Claudiu-Ionel Nicola and Marcel Nicola

Mathematics 2023, 11(15), 3324; https://doi.org/10.3390/math11153324 - 28 Jul 2023

Viewed by 623

Abstract

Starting from the fact that in sensorless control systems of the Permanent Magnet Synchronous Motor (PMSM), the load torque can have short and significant variations, this paper presents the sensorless control of a PMSM based on a Linear Adaptive Disturbance Rejection Controller (LADRC) type controller. Essentially, the successful operation of the LADRC controller to achieve PMSM rotor speed control performance depends on a good estimation of the disturbances acting on the system. Traditionally, an Extended State Observer (ESO) is used to make such an estimate. In this paper, it is proposed to use a Disturbance Observer (DO) to estimate the external disturbances, and after their rejection, the LADRC controller ensures an equivalent global behavior of the control system with an ideal double integrator, thus increasing ease in achieving the desired control performance. Control structures and Matlab/Simulink implementation of the PMSM sensorless control system based on the LADRC controller with an ESO-/DO-type observer are presented, as is its use in tandem with a Reinforcement Learning Twin-Delayed Deep Deterministic Policy Gradient (RL-TD3) specially trained agent that provides correction signals for more accurate estimation of external disturbances and hence improved control performance. To optimize the gain value of the DO-type observer, a computational intelligence algorithm such as the Ant Colony Algorithm (ACO) is used. Qualitatively superior performance is achieved by using LADRC with the RL-TD3 agent control structure in terms of parametric robustness, response time, and steady-state error. In addition, by calculating the fractal dimension (DF) of the controlled signal and the PMSM rotor speed, it is found that the higher the DF, the better the performance of the control system. The validation of the superiority of the proposed control structures is carried out by means of numerical simulations in the Matlab/Simulink environment. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications)

► Show Figures

Figure 1

20 pages, 1098 KiB

Open AccessArticle

Robust Cascade Control inside a New Model-Matching Architecture

by Javier Rico-Azagra and Montserrat Gil-Martínez

Mathematics 2023, 11(11), 2523; https://doi.org/10.3390/math11112523 - 31 May 2023

Viewed by 823

Abstract

Whenever additional states of a plant can be measured, closing nested feedback loops can be exploited in a variety of ways. The goal here is to reduce the bandwidth of feedback controllers and thus reduce the amplification of sensor noise that can otherwise spoil the expected performance when the actuator saturates. This can be particularly relevant for demanding tracking specifications and large plant uncertainties. In this context, the current work proposes a novel model-matching control architecture with a feedforward controller and two feedback controllers, which is accompanied by a new robust design method in the frequency domain of Quantitative Feedback Theory (QFT). The use of a feedforward controller reduces the amount of feedback to the minimum necessary to constrain the spread of the tracking error responses as specified. Furthermore, this amount of feedback is quantitatively distributed along the frequency between the inner and outer loops to reduce the total sensor noise at the control input as much as possible. A theoretical example illustrates the method and highlights the advantages of the new architecture over two other previously feasible QFT solutions: one with double feedback and another with single feedback plus feedforward. The importance of choosing the correct switching frequency between loops is also demonstrated. Finally, the angle of rotation of a commercial servo motor is successfully controlled using the motor speed as an internal measure. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications)

► Show Figures

Figure 1

16 pages, 1731 KiB

Open AccessArticle

Data-Driven pH Model in Raceway Reactors for Freshwater and Wastewater Cultures

by Pablo Otálora, José Luis Guzmán, Manuel Berenguel and Francisco Gabriel Acién

Mathematics 2023, 11(7), 1614; https://doi.org/10.3390/math11071614 - 27 Mar 2023

Cited by 2 | Viewed by 1087

Abstract

The industrial production of microalgae is a process as sustainable as it is interesting in terms of its diverse applications, especially for wastewater treatment. Its optimization requires an exhaustive knowledge of the system, which is commonly achieved through models that describe its dynamics. Although not widely used in this field, artificial neural networks are presented as an appropriate technique to develop this type of model, having the ability to adapt to complex and nonlinear problems solely from the process data. In this work, neural network models have been developed to characterize the pH dynamics in two different raceway reactors, one with freshwater and the other with wastewater. The models are able to predict pH profiles with a prediction horizon of up to eleven hours and only using available measurable process data, such as medimum level, CO

_{2}

injection, and solar radiation. The results demonstrate the potential of artificial neural networks in the modeling of continuous dynamic systems in the field of industry, obtaining accurate, fast-running models that can adapt to different circumstances. Moreover, these models open the field to the design of data-driven model-based control algorithms to account for the nonlinear dynamics of this biological system. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications)

► Show Figures

Figure 1

31 pages, 7512 KiB

Open AccessArticle

Performance and Extreme Conditions Analysis Based on Iterative Modelling Algorithm for Multi-Trailer AGVs

by Roberto Sánchez-Martinez, J. Enrique Sierra-García and Matilde Santos

Mathematics 2022, 10(24), 4783; https://doi.org/10.3390/math10244783 - 15 Dec 2022

Cited by 2 | Viewed by 1529

Abstract

Automatic guidance vehicles (AGV) are industrial vehicles that play an important role in the development of smart manufacturing systems and Industry 4.0. To provide these autonomous systems with the flexibility that is required today in these industrial workspaces, AGV computational models are necessary in order to analyze their performance and design efficient planning and control strategies. To address these issues, in this work, the mathematical model and the algorithm that implement a computational control-oriented simulation model of a hybrid tricycle-differential AGV with multi-trailers have been developed. Physical factors, such as wheel-ground interaction and the effect of vertical and lateral loads on its dynamics, have been incorporated into the model. The model has been tested in simulation with two different controllers and three trajectories: a circumference, a square, and an s-shaped curve. Furthermore, it has been used to analyze extreme situations of slipping and capsizing and the influence of the number of trailers on the tracking error and the control effort. This way, the minimum lateral friction coefficient to avoid slipping and the minimum ratio between the lateral and height displacement of the center of gravity to avoid capsizing have been obtained. In addition, the effect of a change in the friction coefficient has also been simulated. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications)

► Show Figures

Figure 1

20 pages, 546 KiB

Open AccessArticle

A Branch-and-Bound Algorithm for the Bi-Objective Quay Crane Scheduling Problem Based on Efficiency and Energy

by Hongming Li and Xintao Li

Mathematics 2022, 10(24), 4705; https://doi.org/10.3390/math10244705 - 11 Dec 2022

Cited by 2 | Viewed by 1141

Abstract

Motivated by the call of the International Maritime Organization to meet the emission targets of 2030, this study considers two important practical aspects of quay crane scheduling: efficiency and energy consumption. More precisely, we introduce the bi-objective quay crane scheduling problem where the objective is to minimize the vessel’s completion time and the crane’s energy consumption. This is done by formulating a bi-objective mixed-integer programming model. A branch-and-bound algorithm was developed as the exact solution approach to find the full set of Pareto-optimal solutions. We consider (i) various lower bounds for both objectives, (ii) specific upper bounds, (iii) additional branching criteria, and (iv) fathoming criteria to detect Pareto-optimal solutions. Numerical experiments on benchmark instances show that the branch-and-bound algorithm can efficiently solve small- and medium-sized problems. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications)

► Show Figures

Figure 1

13 pages, 2673 KiB

Open AccessArticle

Analytic Solution for Nonlinear Impact-Angle Guidance Law with Time-Varying Thrust

by Sungjin Cho

Mathematics 2022, 10(21), 4034; https://doi.org/10.3390/math10214034 - 31 Oct 2022

Cited by 3 | Viewed by 1174

Abstract

This paper presents an impact-angle guidance law of unmanned aerial vehicles (UAVs) with time-varying thrust in a boosting phase. Most current research on impact-angle guidance law assumes that UAV speed is constant in terms of controlled thrust. However, the UAV speed and the acceleration in a boosting phase keep changing because of time-varying thrust. Environmental factors and manufacturing process error may prohibit accurately predicting vehicle-thrust profiles. We propose a nonlinear impact-angle guidance law by analytically solving second-order error dynamics with nonlinear time-varying coefficients. The proposed analytic solution enables one to update guidance gains according to initial and current states so that desired impact angle is met while the miss-distance error is reduced. We prove the finite-time error convergence of the proposed guidance law with the Lyapunov stability theory. Various simulation studies are performed to verify the proposed guidance law. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications)

► Show Figures

Figure 1

19 pages, 6149 KiB

Open AccessArticle

Coordination Control of Multi-Axis Steering and Active Suspension System for High-Mobility Emergency Rescue Vehicles

by Hao Chen, Ming-De Gong, Ding-Xuan Zhao, Wen-Bin Liu and Guan-Yong Jia

Mathematics 2022, 10(19), 3562; https://doi.org/10.3390/math10193562 - 29 Sep 2022

Cited by 4 | Viewed by 1514

Abstract

This study proposes a coordinated control strategy to solve the coupling problem between the multi-axle steering system and the active suspension system of emergency rescue vehicles. Firstly, an eleven-degree-of-freedom coupling model of an emergency rescue vehicle is established. Secondly, a dual sliding mode (DSM) controller is designed for the multi-axle steering system and a dual linear quadratic regulator (DLQR) controller is designed for the active suspension system. Finally, the coordinated control strategy is designed, and the weight values are selected using the fuzzy algorithm. Results show that compared with the individual control, the root mean square (RMS) value of the body roll angle, roll angle acceleration, and yaw angle acceleration with coordinated control are reduced by 16.89%, 29.08%, and 27.75%, respectively. The proposed coordinated control strategy effectively improves the handling stability and ride comfort of the vehicle. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications)

► Show Figures

Figure 1

27 pages, 1172 KiB

Open AccessArticle

Energy Management of Refrigeration Systems with Thermal Energy Storage Based on Non-Linear Model Predictive Control

by Guillermo Bejarano, João M. Lemos, Javier Rico-Azagra, Francisco R. Rubio and Manuel G. Ortega

Mathematics 2022, 10(17), 3167; https://doi.org/10.3390/math10173167 - 02 Sep 2022

Cited by 2 | Viewed by 1659

Abstract

This work addresses the energy management of a combined system consisting of a refrigeration cycle and a thermal energy storage tank based on phase change materials. The storage tank is used as a cold-energy buffer, thus decoupling cooling demand and production, which leads to cost reduction and satisfaction of peak demand that would be infeasible for the original cycle. A layered scheduling and control strategy is proposed, where a non-linear predictive scheduler computes the references of the main powers involved (storage tank charging/discharging powers and direct cooling production), while a low-level controller ensures that the requested powers are actually achieved. A simplified model retaining the dominant dynamics is proposed as the prediction model for the scheduler. Economic, efficiency, and feasibility criteria are considered, seeking operating cost reduction while ensuring demand satisfaction. The performance of the proposed strategy for the system with energy storage is compared in simulation with that of a cycle without energy storage, where the former is shown to satisfy challenging demands while reducing the operating cost by up to 28%. The proposed approach also shows suitable robustness when significant uncertainty in the prediction model is considered. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications)

► Show Figures

Journal Menu

Journal Browser

Modeling, Simulation, Control and Optimization in Engineering with Applications

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Related Special Issue

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI