Dynamic Modeling and Simulation for Control Systems, 2nd Edition

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 9644

Special Issue Editors


E-Mail Website
Guest Editor
Department of Robotics and Production Systems, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: robotics; dynamic behavior; neural networks; mobile robots; neurorehabilitation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Manufacturing Science and Engineering Department, “Dunarea de Jos” University of Galati, 800201 Galati, Romania
Interests: numerical modeling of machining systems; manufacturing process control; dynamics of cutting processes; chaos theory; computer-assisted design

E-Mail Website
Guest Editor
Department of Product Design, Mechatronics and Environment, Transilvania University of Brasov, 500036 Brasov, Romania
Interests: mechanical systems; renewable energy systems; virtual prototyping; modeling and simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue titled, “Dynamic Modeling and Simulation for Control Systems II”, will address topics including the mathematical modeling of dynamic behavior; optimization algorithms; assisted theoretical and experimental research; the control of physical engineering systems; mechanical, electrical and fluid interaction components; system response analysis; feedback control systems; numerical software and software for dynamic simulation and optimization; system stability; and dynamic behavior in the frequency field. This Special Issue aims to cover important aspects about how to optimize the dynamic behavior of physical systems using special algorithms and artificial intelligence in the modeling, simulation and optimization of the components and systems from important fields such as astronautics, aerospace, avionics, robotics, manufacturing systems, mechanical engineering, power energy, materials technology and neurorehabilitation. Fuzzy and neural network control applied in complex systems will be studied. Control and simulation isotope separation processes will be developed and analyzed. This Special Issue of Mathematics will be a useful guide on techniques for the modeling, simulation and optimization of control systems in order to obtain acceptable dynamic behaviors.

Topics for this Special Issue:

  • Design of physical engineering systems;
  • Control of physical engineering systems;
  • Mechanical, electrical and fluid interaction between system components;
  • Mathematical modeling of control systems;
  • Fuzzy logic and control systems;
  • Dynamic behavior analysis;
  • System response analysis;
  • Feedback control systems;
  • Numerical simulation of integrated systems;
  • Fault detection and diagnosis;
  • Networked control and time-delay systems;
  • Frequency response;
  • Stability;
  • Control and simulation of the isotope separation process;
  • Software for dynamic simulation and optimization.

Prof. Dr. Adrian Olaru
Prof. Dr. Gabriel Frumusanu
Prof. Dr. Catalin Alexandru
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

  • mathematical modeling
  • numerical simulation
  • software simulation
  • assisted research
  • data acquisition
  • mechanical and electrical interaction
  • physical engineering design
  • control systems
  • response analysis
  • feedback control
  • frequency response
  • stability
  • fuzzy logic
  • neural networks
  • artificial intelligence

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 439 KiB  
Article
The Optimal Consumption, Investment and Life Insurance for Wage Earners under Inside Information and Inflation
by Rui Jiao, Wei Liu and Yijun Hu
Mathematics 2023, 11(15), 3415; https://doi.org/10.3390/math11153415 - 05 Aug 2023
Viewed by 826
Abstract
This paper studies the dynamically optimal consumption, investment and life-insurance strategies for a wage earners under inside information and inflation. Assume that the wage earner can invest in a risk-free asset, a risky asset and an inflation-indexed bond and that the wage earner [...] Read more.
This paper studies the dynamically optimal consumption, investment and life-insurance strategies for a wage earners under inside information and inflation. Assume that the wage earner can invest in a risk-free asset, a risky asset and an inflation-indexed bond and that the wage earner can obtain some additional information on the risky asset from the financial market. By maximizing the expected utility of the wage earner’s consumption, inheritance and terminal wealth, we obtain the dynamically optimal consumption, investment and life-insurance strategies for the wage earner. The method of this paper is mainly based on (dynamical) stochastic control theory and the technique of enlargement of filtrations. Moreover, sensitivity analysis is carried out, which reveals that a wage earner with inside information tends to increase his/her consumption and investment, while reducing his/her purchase of life insurance. Full article
(This article belongs to the Special Issue Dynamic Modeling and Simulation for Control Systems, 2nd Edition)
Show Figures

Figure 1

28 pages, 5994 KiB  
Article
Multi-Objective Optimization for Controlling the Dynamics of the Diabetic Population
by Karim El Moutaouakil, Abdellatif El Ouissari, Vasile Palade, Anas Charroud, Adrian Olaru, Hicham Baïzri, Saliha Chellak and Mouna Cheggour
Mathematics 2023, 11(13), 2957; https://doi.org/10.3390/math11132957 - 02 Jul 2023
Cited by 1 | Viewed by 1098
Abstract
To limit the adverse effects of diabetes, a personalized and long-term management strategy that includes appropriate medication, exercise and diet has become of paramount importance and necessity. Compartment-based mathematical control models for diabetes usually result in objective functions whose terms are conflicting, preventing [...] Read more.
To limit the adverse effects of diabetes, a personalized and long-term management strategy that includes appropriate medication, exercise and diet has become of paramount importance and necessity. Compartment-based mathematical control models for diabetes usually result in objective functions whose terms are conflicting, preventing the use of single-objective-based models for obtaining appropriate personalized strategies. Taking into account the conflicting aspects when controlling the diabetic population dynamics, this paper introduces a multi-objective approach consisting of four steps: (a) modeling the problem of controlling the diabetic population dynamics using a multi-objective mathematical model, (b) discretizing the model using the trapezoidal rule and the Euler–Cauchy method, (c) using swarm-intelligence-based optimizers to solve the model and (d) structuring the set of controls using soft clustering methods, known for their flexibility. In contrast to single-objective approaches, experimental results show that the multi-objective approach obtains appropriate personalized controls, where the control associated with the compartment of diabetics without complications is totally different from that associated with the compartment of diabetics with complications. Moreover, these controls enable a significant reduction in the number of diabetics with and without complications, and the multi-objective strategy saves up to 4% of the resources needed for the control of diabetes without complications and up to 18% of resources for the control of diabetes with complications. Full article
(This article belongs to the Special Issue Dynamic Modeling and Simulation for Control Systems, 2nd Edition)
Show Figures

Figure 1

19 pages, 7643 KiB  
Article
An Extended-State Observer Based on Smooth Super-Twisting Sliding-Mode Controller for DC-DC Buck Converters
by Dian Jiang, Yunmei Fang and Juntao Fei
Mathematics 2023, 11(13), 2835; https://doi.org/10.3390/math11132835 - 24 Jun 2023
Cited by 1 | Viewed by 666
Abstract
This paper designs a novel smooth super-twisting extended-state observer (SSTESO)-based smooth super-twisting sliding-mode control (SSTSMC) scheme to promote the robust ability and voltage-tracking performance of DC-DC buck converters. First, an SSTESO is proposed to estimate the unknown lumped disturbance and compensate for the [...] Read more.
This paper designs a novel smooth super-twisting extended-state observer (SSTESO)-based smooth super-twisting sliding-mode control (SSTSMC) scheme to promote the robust ability and voltage-tracking performance of DC-DC buck converters. First, an SSTESO is proposed to estimate the unknown lumped disturbance and compensate for the estimation of the voltage controller. The SSTESO is realized by constructing a novel smooth function to replace the nonlinear sign function in STESO, which can provide a faster convergence speed and higher estimation accuracy. The SSTSM controller is designed by adopting a similar smooth function to further suppress chattering and improve dynamic response. Comprehensive simulation results demonstrate that the proposed SSTESO-based SSTSMC scheme can improve the robustness and transient response of a DC-DC buck converter system in the presence of external disturbance and parameter uncertainties. Full article
(This article belongs to the Special Issue Dynamic Modeling and Simulation for Control Systems, 2nd Edition)
Show Figures

Figure 1

20 pages, 16324 KiB  
Article
Adaptive Super-Twisting Sliding Mode Control of Active Power Filter Using Interval Type-2-Fuzzy Neural Networks
by Jiacheng Wang, Yunmei Fang and Juntao Fei
Mathematics 2023, 11(12), 2785; https://doi.org/10.3390/math11122785 - 20 Jun 2023
Cited by 3 | Viewed by 769
Abstract
Aiming at the unknown uncertainty of an active power filter system in practical operation, combining the advantages of self-feedback structure, interval type-2 fuzzy neural network, and super-twisting sliding mode, an adaptive super-twisting sliding mode control method of interval type-2 fuzzy neural network with [...] Read more.
Aiming at the unknown uncertainty of an active power filter system in practical operation, combining the advantages of self-feedback structure, interval type-2 fuzzy neural network, and super-twisting sliding mode, an adaptive super-twisting sliding mode control method of interval type-2 fuzzy neural network with self-feedback recursive structure (IT2FNN-SFR STSMC) is proposed in this paper. IT2FNN has an uncertain membership function, which can enhance the nonlinear ability and robustness of the network. The historical information will be stored and utilized by the self-feedback recursive structure (SFR) at runtime. Therefore, the novel IT2FNN-SFR is designed to improve the dynamic approximation effect of the neural network and reduce the dependence of the controller on the actual mathematical model. The adaptive rate of each weight of the neural network is designed by the Lyapunov method and gradient descent (GD) algorithm to ensure the convergence and stability of the system. Super-twisting sliding mode control (STSMC) has strong robustness, which can effectively reduce system chattering, and improve control accuracy and system performance. The gain of the integral term in the STSMC is set as a constant, and the other gain is changed adaptively whose adaptive rate is deduced through the stability proof of the neural network, which greatly reduces the difficulty of parameter adjustment. The harmonic suppression ability of the designed control strategy is verified by simulation experiments. Full article
(This article belongs to the Special Issue Dynamic Modeling and Simulation for Control Systems, 2nd Edition)
Show Figures

Figure 1

18 pages, 2943 KiB  
Article
State Feedback with Integral Control Circuit Design of DC-DC Buck-Boost Converter
by Humam Al-Baidhani, Abdullah Sahib and Marian K. Kazimierczuk
Mathematics 2023, 11(9), 2139; https://doi.org/10.3390/math11092139 - 03 May 2023
Cited by 5 | Viewed by 2860
Abstract
The pulse-with modulated (PWM) dc-dc buck-boost converter is a non-minimum phase system, which requires a proper control scheme to improve the transient response and provide constant output voltage during line and load variations. The pole placement technique has been proposed in the literature [...] Read more.
The pulse-with modulated (PWM) dc-dc buck-boost converter is a non-minimum phase system, which requires a proper control scheme to improve the transient response and provide constant output voltage during line and load variations. The pole placement technique has been proposed in the literature to control this type of power converter and achieve the desired response. However, the systematic design procedure of such control law using a low-cost electronic circuit has not been discussed. In this paper, the pole placement via state-feedback with an integral control scheme of inverting the PWM dc-dc buck-boost converter is introduced. The control law is developed based on the linearized power converter model in continuous conduction mode. A detailed design procedure is given to represent the control equation using a simple electronic circuit that is suitable for low-cost commercial applications. The mathematical model of the closed-loop power converter circuit is built and simulated using SIMULINK and Simscape Electrical in MATLAB. The closed-loop dc-dc buck-boost converter is tested under various operating conditions. It is confirmed that the proposed control scheme improves the power converter dynamics, tracks the reference signal, and maintains regulated output voltage during abrupt changes in input voltage and load current. The simulation results show that the line variation of 5 V and load variation of 2 A around the nominal operating point are rejected with a maximum percentage overshoot of 3.5% and a settling time of 5.5 ms. Full article
(This article belongs to the Special Issue Dynamic Modeling and Simulation for Control Systems, 2nd Edition)
Show Figures

Figure 1

16 pages, 394 KiB  
Article
A Deterministic Setting for the Numerical Computation of the Stabilizing Solutions to Stochastic Game-Theoretic Riccati Equations
by Samir Aberkane and Vasile Dragan
Mathematics 2023, 11(9), 2068; https://doi.org/10.3390/math11092068 - 27 Apr 2023
Viewed by 603
Abstract
In this paper, we are interested in the numerical aspects of the class of generalized Riccati difference equations which are involved in linear quadratic (LQ) stochastic difference games. More specifically, we address the problem of the numerical computation of the stabilizing solutions for [...] Read more.
In this paper, we are interested in the numerical aspects of the class of generalized Riccati difference equations which are involved in linear quadratic (LQ) stochastic difference games. More specifically, we address the problem of the numerical computation of the stabilizing solutions for this class of nonlinear difference equations. We propose an iterative deterministic algorithm for the computation of such a global solution. The performances of the proposed algorithm are illustrated with some numerical examples. Full article
(This article belongs to the Special Issue Dynamic Modeling and Simulation for Control Systems, 2nd Edition)
Show Figures

Figure 1

21 pages, 662 KiB  
Article
Gain Scheduled Fault Detection Filter for Markovian Jump Linear System with Nonhomogeneous Markov Chain
by Leonardo Carvalho, Jonathan M. Palma, Cecília F. Morais, Bayu Jayawardhana and Oswaldo L. V. Costa
Mathematics 2023, 11(7), 1713; https://doi.org/10.3390/math11071713 - 03 Apr 2023
Viewed by 954
Abstract
In a networked control system scenario, the packet dropout is usually modeled by a time-invariant (homogeneous) Markov chain (MC) process. However, from a practical point of view, the probabilities of packet loss can vary in time and/or probability parameter dependency. Therefore, to design [...] Read more.
In a networked control system scenario, the packet dropout is usually modeled by a time-invariant (homogeneous) Markov chain (MC) process. However, from a practical point of view, the probabilities of packet loss can vary in time and/or probability parameter dependency. Therefore, to design a fault detection filter (FDF) implemented in a semi-reliable communication network, it is important to consider the variation in time of the network parameters, by assuming the more accurate scenario provided by a nonhomogeneous jump system. Such a premise can be properly taken into account within the linear parameter varying (LPV) framework. In this sense, this paper proposes a new design method of H gain-scheduled FDF for Markov jump linear systems under the assumption of a nonhomogeneous MC. To illustrate the applicability of the theoretical solution, a numerical simulation is presented. Full article
(This article belongs to the Special Issue Dynamic Modeling and Simulation for Control Systems, 2nd Edition)
Show Figures

Figure 1

Back to TopTop