Special Issue "Control and Soft Computing"

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 15989

Special Issue Editors

Prof. Dr. Rui Araújo
E-Mail Website
Guest Editor
Institute of Systems and Robotics (ISR-UC), and Department of Electrical and Computer Engineering (DEEC-UC), University of Coimbra, Pólo II, PT-3030-290 Coimbra, Portugal
Interests: data mining techniques; big data and big data analytics; multi-agent systems; swarms and swarm intelligence; sensor networks; self-* properties and adaptive algorithms; operating systems and middleware for autonomous and adaptive Systems; deep learning applications; artificial intelligence applications; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Jérôme Mendes
E-Mail Website
Guest Editor
Computational Intelligence Group, Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal
Interests: fuzzy systems; evolving systems; intelligent control; failure detection
Special Issues, Collections and Topics in MDPI journals
Dr. Francisco A. A. Souza
E-Mail Website
Guest Editor
1. Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal
2. Oncontrol Technologies, LDA, 3004-531 Coimbra, Portugal
Interests: applied machine learning; industrial systems; process control

Special Issue Information

Dear Colleagues,

Recent advances in control and soft computing systems have brought new levels of real-life applications in a wide range of areas, including control of industrial systems, internet of things (IoT), cyber-physical systems, smart grids, power and energy systems, biomedical engineering, and so on. The complexity and amount of data underlying the industrial processes have increased during the recent years, mainly with the advent of the industry 4.0 paradigm, thus requiring advanced strategies to cope with them. Soft computing techniques, as opposed to traditional computing, have been demonstrated to be a useful tool to translate the data and complexity of modern industrial systems into useful information, which can be, for example, used to help to process control and optimization, and process understanding.

This Special Issue intends to disseminate the recent developments on the topics of process control and soft computing, with an emphasis on, but not limited to, industrial systems. This being a multidisciplinary Special Issue, papers that have as main topic advanced techniques for control and soft computing algorithms are welcome, as well applications on:

  • Modern control in the various industries;
  • Computational intelligence methodologies for intelligent control and identification;
  • Failure detection and predictive maintenance;
  • Automatic decision making;
  • Soft/virtual sensors;
  • Internet of things (IoT);
  • Cyber-physical systems;
  • Smart grids;
  • Intelligent robotics;
  • Human–robot interaction;
  • Power and energy systems;
  • Smart manufacturing;
  • Biomedical engineering.

Prof. Dr. Rui Araújo
Dr. Jérôme Mendes
Dr. Francisco A. A. Souza
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. Applied Sciences 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 2300 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

  • Adaptive control
  • Distributed control
  • Networked control
  • Intelligent and AI based control (fuzzy systems, neural networks, evolutionary)
  • Optimization and robust control
  • Fault detection and control
  • Biologically inspired evolutionary algorithms
  • Fuzzy systems and neural networks
  • Evolving/iterative/self-organizing soft computing algorithms
  • Machine learning
  • Autonomous systems
  • Intelligent decision systems
  • Reinforcement and deep-learning
  • Clustering algorithms
  • Modeling
  • Soft sensors
  • Smart manufacturing and Industry 4.0
  • Robotics

Published Papers (15 papers)

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Research

Article
Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
Appl. Sci. 2021, 11(4), 1400; https://doi.org/10.3390/app11041400 - 04 Feb 2021
Viewed by 557
Abstract
This paper studies the orbital pursuit-evasion problem with imperfect information, including measurement noise and input delay. The presence of imperfect information will degrade the players’ control performance and lead to mission failure. To solve this problem, a compensation control strategy for the players [...] Read more.
This paper studies the orbital pursuit-evasion problem with imperfect information, including measurement noise and input delay. The presence of imperfect information will degrade the players’ control performance and lead to mission failure. To solve this problem, a compensation control strategy for the players is proposed. The compensation control strategy consists of two parts: the guaranteed cost strategy and the time delay compensation method. First, a near-optimal feedback strategy called guaranteed cost strategy with perfect information is proposed based on a Lyapunov-like function and matrix analysis theory. Second, a time delay compensation method based on an uncertainty set is proposed to compensate for delayed information. The compensation control strategy is derived by combining the time delay compensation method with the guaranteed cost strategy. While applying this strategy to the game, the input of the strategy is generated by processing the measured data with the state estimation algorithm based on the unscented Kalman filter (UKF). The simulation results show that the proposed strategy can handle the orbital pursuit-evasion problem with imperfect information effectively. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model
Appl. Sci. 2021, 11(4), 1361; https://doi.org/10.3390/app11041361 - 03 Feb 2021
Cited by 3 | Viewed by 934
Abstract
Milling operations in various production processes are among the most important factors in determining the quality, stability, and consumption of energy. Optimizing and stabilizing the milling process is a non-linear multivariable control problem. In specific processes that deal with natural materials (e.g., cement, [...] Read more.
Milling operations in various production processes are among the most important factors in determining the quality, stability, and consumption of energy. Optimizing and stabilizing the milling process is a non-linear multivariable control problem. In specific processes that deal with natural materials (e.g., cement, pulp and paper, beverage brewery and water/wastewater treatment industries). A novel data-driven approach utilizing real-time monitoring control technology is proposed for the purpose of optimizing the grinding of cement processing. A combined event modeling for feature extraction and the fully connected deep neural network model to predict the coarseness of cement particles is proposed. The resulting prediction allows a look ahead control strategy and corrective actions. The proposed solution has been deployed in a number of cement plants around the world. The resultant control strategy has enabled the operators to take corrective actions before the coarse return increases, both in autonomous and manual mode. The impact of the solution has improved efficiency resource use by 10% of resources, the plant stability, and the overall energy efficiency of the plant. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems
Appl. Sci. 2021, 11(1), 62; https://doi.org/10.3390/app11010062 - 23 Dec 2020
Cited by 3 | Viewed by 1300
Abstract
A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear [...] Read more.
A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy models can closely approximate the original unknown nonlinear system with a reduced number of linearizations. Based on T-S fuzzy models, optimal controllers are designed and implemented for a nonlinear two-link flexible joint robot, which demonstrates the possibility of implementing the well-established model-based optimal control method onto unknown nonlinear dynamic systems. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
Fault Diagnosis for a Class of Robotic Systems with Application to 2-DOF Helicopter
Appl. Sci. 2020, 10(23), 8359; https://doi.org/10.3390/app10238359 - 25 Nov 2020
Viewed by 665
Abstract
This paper considers a general approach to fault diagnosis using a generalized Hamiltonian system representation. It can be considered that, in general, nonlinear systems still represent a problem in fault diagnosis because there are results only for a specific class of them. Therefore, [...] Read more.
This paper considers a general approach to fault diagnosis using a generalized Hamiltonian system representation. It can be considered that, in general, nonlinear systems still represent a problem in fault diagnosis because there are results only for a specific class of them. Therefore, fault diagnosis remains a challenging research area despite the maturity of some of the available results. In this work, a type of nonlinear system that admits a generalized Hamiltonian representation is considered; in practice, there are many systems that have this kind of representation. Thereupon, an approach for fault detection and isolation based on the Hamiltonian representation is proposed. First, following the classic approach, the original system is decoupled in different subsystems so that each subsystem is sensitive to one particular fault. Then, taking advantage of the structure, a simple way to design the residuals is presented. Finally, the proposed scheme is validated at the two-degree of freedom (DOF) helicopter of Quanser®, where the presence of faults in sensors and actuators were considered. The results show the efficacy of the proposed scheme. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
Reactance Regulation Using Coils with Perpendicular Magnetic Field in the Tubular Core design
Appl. Sci. 2020, 10(21), 7645; https://doi.org/10.3390/app10217645 - 29 Oct 2020
Viewed by 687
Abstract
This article presents an efficient method for prosumer connection to the distribution line. The prosumers can be connected to the distribution line using specially designed controllable reactive impedance. The reactive impedance is controlled using specially designed coils and magnetic core. The internal coil [...] Read more.
This article presents an efficient method for prosumer connection to the distribution line. The prosumers can be connected to the distribution line using specially designed controllable reactive impedance. The reactive impedance is controlled using specially designed coils and magnetic core. The internal coil is wound in the toroidal direction (across the z-axis) and creates a toroidal shape. A thin ferromagnetic strip is coiled on this toroidal shape in the poloidal direction to form the ferromagnetic core. Then, an external coil is wound on this ferromagnetic core in the poloidal direction. The internal coil is controlled by the inductive impedance of the external coil, which is related to the anisotropic properties of ferromagnetic strips. The internal coil is connected between the power supply line and a prosumer. This arrangement confirms the magnetic independence of coils and the symmetry of the current in the internal coil. The magnetic coupling between both coils is very low (~0.015–0.017) and appropriate for engineering applications. It is approved that the impedance of the internal coil is changed due to the anisotropic magnetic properties of the core material. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances
Appl. Sci. 2020, 10(20), 7073; https://doi.org/10.3390/app10207073 - 12 Oct 2020
Cited by 3 | Viewed by 754
Abstract
In this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. [...] Read more.
In this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. The stabilization of states along the desired trajectory is ensured via a control Lyapunov function. The optimal parameters of the control law are identified by different nature-inspired metaheuristic algorithms, namely: Ant Lion Optimizer, Grey Wolf Optimizer, Harris Hawks Optimization, and Whale Optimization Algorithm. Finally, a highly nonlinear biological system subject to parameter uncertainties and external disturbances (Activated Sludge Model) is proposed to validate the control strategy. Simulation results demonstrate that the control law with Ant Lion Optimizer outperforms the other optimization methods in terms of trajectory tracking in the presence of disturbances. The control law with Harris Hawks Optimization shows a better convergence of the neural states in presence of parameter uncertainty. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
Heuristic Global Optimization of an Adaptive Fuzzy Controller for the Inverted Pendulum System: Experimental Comparison
Appl. Sci. 2020, 10(18), 6158; https://doi.org/10.3390/app10186158 - 04 Sep 2020
Cited by 8 | Viewed by 943
Abstract
In this paper an adaptive fuzzy controller is proposed to solve the trajectory tracking problem of the inverted pendulum on a cart system. The designed algorithm is featured by not using any knowledge of the dynamic model and incorporating a full-state feedback. The [...] Read more.
In this paper an adaptive fuzzy controller is proposed to solve the trajectory tracking problem of the inverted pendulum on a cart system. The designed algorithm is featured by not using any knowledge of the dynamic model and incorporating a full-state feedback. The stability of the closed-loop system is proven via the Lyapunov theory, and boundedness of the solutions is guaranteed. The proposed controller is heuristically tuned and its performance is tested via simulation and real-time experimentation. For this reason, a tuning method is investigated via evolutionary algorithms: particle swarm optimization, firefly algorithm and differential evolution in order to optimize the performance and verify which technique produces better results. First, a model-based simulation is carried out to improve the parameter tuning of the fuzzy systems, and then the results are transferred to real-time experiments. The optimization procedure is presented as well as the experimental results, which are also discussed. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules
Appl. Sci. 2020, 10(17), 5836; https://doi.org/10.3390/app10175836 - 23 Aug 2020
Cited by 8 | Viewed by 967
Abstract
The paper proposes a methodology to online self-evolve direct fuzzy logic controllers (FLCs), to deal with unknown and time-varying dynamics. The proposed methodology self-designs the controller, where fuzzy control rules can be added or removed considering a predefined criterion. The proposed methodology aims [...] Read more.
The paper proposes a methodology to online self-evolve direct fuzzy logic controllers (FLCs), to deal with unknown and time-varying dynamics. The proposed methodology self-designs the controller, where fuzzy control rules can be added or removed considering a predefined criterion. The proposed methodology aims to reach a control structure easily interpretable by human operators. The FLC is defined by univariate fuzzy control rules, where each input variable is represented by a set of fuzzy control rules, improving the interpretability ability of the learned controller. The proposed self-evolving methodology, when the process is under control (online stage), adds fuzzy control rules on the current FLC using a criterion based on the incremental estimated control error obtained using the system’s inverse function and deletes fuzzy control rules using a criterion that defines “less active” and “less informative” control rules. From the results on a nonlinear continuously stirred tank reactor (CSTR) plant, the proposed methodology shows the capability to online self-design the FLC by adding and removing fuzzy control rules in order to successfully control the CSTR plant. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
Model Predictive Control of Non-Linear Systems Using Tensor Flow-Based Models
Appl. Sci. 2020, 10(11), 3958; https://doi.org/10.3390/app10113958 - 07 Jun 2020
Cited by 2 | Viewed by 1351
Abstract
The present paper proposes an approach for the development of a non-linear model-based predictive controller (NMPC) using a non-linear process model based on Artificial Neural Networks (ANNs). This work exploits recent trends on ANN literature using a TensorFlow implementation and shows how they [...] Read more.
The present paper proposes an approach for the development of a non-linear model-based predictive controller (NMPC) using a non-linear process model based on Artificial Neural Networks (ANNs). This work exploits recent trends on ANN literature using a TensorFlow implementation and shows how they can be efficiently used as support for closed-loop control systems. Furthermore, it evaluates how the generalization capability problems of neural networks can be efficiently overcome when the model that supports the control algorithm is used outside of its initial training conditions. The process’s transient response performance and steady-state error are parameters under focus and will be evaluated using a MATLAB’s Simulink implementation of a Coupled Tank Liquid Level controller and a Yeast Fermentation Reaction Temperature controller, two well-known benchmark systems for non-linear control problems. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
A Soft Computing Framework to Support Consumers in Obtaining Sustainable Appliances from the Market
Appl. Sci. 2020, 10(9), 3206; https://doi.org/10.3390/app10093206 - 04 May 2020
Cited by 1 | Viewed by 978
Abstract
Currently, sustainability is considered a priority by society, with the household appliances being one of the economic sectors involved in achieving sustainability. However, the existence of several issues (e.g., energy and water consumption, reliability, initial cost, and illuminance, among others) together with the [...] Read more.
Currently, sustainability is considered a priority by society, with the household appliances being one of the economic sectors involved in achieving sustainability. However, the existence of several issues (e.g., energy and water consumption, reliability, initial cost, and illuminance, among others) together with the diversity of brands and models on the market, make the consumer’s decisions regarding sustainable options difficult, according to their concerns and related to each sustainability dimension (economic, environmental, and social). By combining evolutionary algorithms (EA) with multicriteria techniques, it is possible to achieve sustainable solutions for the consumer based on their requirements. In this paper, a method is presented to support the consumer by obtaining a set of sustainable household appliances on the market that suit their preferences, concerns, and needs. By using a case study to apply the approach developed here, a set of sustainable appliances from the market is obtained, where several benefits are achieved (e.g., energy and water consumption savings, avoidance of CO2 emissions) during the lifecycle of each appliance, chosen from the appliance’s industry. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
Application of Predictive Control in Scheduling of Domestic Appliances
Appl. Sci. 2020, 10(5), 1627; https://doi.org/10.3390/app10051627 - 29 Feb 2020
Cited by 7 | Viewed by 885
Abstract
In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and [...] Read more.
In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and non-thermal appliances is represented into state-space model to formulate the scheduling task into a mixed-integer-linear-programming (MILP) optimization problem. Model predictive control (MPC) strategy is used to operate the appliances in real-time. The HEMS schedules the appliances in dynamic manner without any a priori knowledge of the load-consumption pattern. At the same time, the HEMS responds to the real-time electricity market and the external environmental conditions (solar radiation, ambient temperature, etc.). Simulation results exhibit the benefits of the proposed HEMS by showing the reduction of up to 70% in electricity cost and up to 57% in peak power consumption. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
Physics-Based Vehicle Simulation Using PD Servo
Appl. Sci. 2019, 9(22), 4949; https://doi.org/10.3390/app9224949 - 17 Nov 2019
Viewed by 1267
Abstract
In this paper, we introduce a novel system for physics-based vehicle simulation from input trajectory. The proposed system approximates the physical movements of a real vehicle using a proportional derivative (PD) servo which estimates proper torques for wheels and controls a vehicle’s acceleration [...] Read more.
In this paper, we introduce a novel system for physics-based vehicle simulation from input trajectory. The proposed system approximates the physical movements of a real vehicle using a proportional derivative (PD) servo which estimates proper torques for wheels and controls a vehicle’s acceleration based on the conditions of the given trajectory. To avoid expensive simulation calculation, the input trajectory is segmented and compared to the optimized trajectories stored in a path library. Based on the similarity of the curve shape between the input and simulated trajectories, an iterative search method is introduced to generate a physically derivable trajectory for convincing simulation results. For an interaction with other objects in the virtual environment, the surface of the vehicle is subdivided into several parts and deformed individually from external forces. As demonstrated in the experimental results, the proposed system can create diverse traffic scenes with multiple vehicles in a fully automated way. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel
Appl. Sci. 2019, 9(18), 3708; https://doi.org/10.3390/app9183708 - 06 Sep 2019
Cited by 9 | Viewed by 1314
Abstract
The location, velocity, and flight path angle of an autonomous unmanned aerial vehicle (UAV) landing on a moving vessel are key factors for an optimal landing trajectory. To tackle this challenge, this paper proposes a method for calculating the optimal approach landing trajectory [...] Read more.
The location, velocity, and flight path angle of an autonomous unmanned aerial vehicle (UAV) landing on a moving vessel are key factors for an optimal landing trajectory. To tackle this challenge, this paper proposes a method for calculating the optimal approach landing trajectory between an UAV and a small vessel. A numerical approach (iterative method) is used to calculate the optimal approach landing trajectory, and the initial lead is introduced in the calculation process of the UAV trajectory for the inclination and heading angle for accuracy improvement, so that the UAV can track and calculate the optimal landing trajectory with high precision. Compared with the variational method, the proposed method can calculate an optimal turning direction angle for the UAV during the landing. Simulation experiments verify the effectiveness of the proposed algorithm and give optimal initialization values. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
Adaptive Backstepping Fractional Fuzzy Sliding Mode Control of Active Power Filter
Appl. Sci. 2019, 9(16), 3383; https://doi.org/10.3390/app9163383 - 16 Aug 2019
Cited by 8 | Viewed by 1200
Abstract
An adaptive fractional-order fuzzy control method for a three-phase active power filter (APF) using a backstepping and sliding mode controller is developed for the purpose of compensating harmonic current and stabilizing the DC voltage quickly. The dynamic model of APF is changed to [...] Read more.
An adaptive fractional-order fuzzy control method for a three-phase active power filter (APF) using a backstepping and sliding mode controller is developed for the purpose of compensating harmonic current and stabilizing the DC voltage quickly. The dynamic model of APF is changed to an analogical cascade system for the convenience of the backstepping strategy. Then a fractional-order sliding mode surface is designed and a fuzzy controller is proposed to approximate the unknown term in the controller, where parameters can be adjusted online. The simulation experiments are conducted and investigated using MATLAB/SIMULINK software package to verify the advantage of the proposed controller. Furthermore, the comparison study between the fractional-order controller and integer-order one is also conducted in order to demonstrate the better performance of the proposed controller in total harmonic distortion (THD), a significant index to evaluate the current quality in the smart grid. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Article
Pursuer’s Control Strategy for Orbital Pursuit-Evasion-Defense Game with Continuous Low Thrust Propulsion
Appl. Sci. 2019, 9(15), 3190; https://doi.org/10.3390/app9153190 - 05 Aug 2019
Cited by 3 | Viewed by 1345
Abstract
This paper studies the orbital pursuit-evasion-defense problem with the continuous low thrust propulsion. A control strategy for the pursuer is proposed based on the fuzzy comprehensive evaluation and the differential game. First, the system is described by the Lawden’s equations, and simplified by [...] Read more.
This paper studies the orbital pursuit-evasion-defense problem with the continuous low thrust propulsion. A control strategy for the pursuer is proposed based on the fuzzy comprehensive evaluation and the differential game. First, the system is described by the Lawden’s equations, and simplified by introducing the relative state variables and the zero effort miss (ZEM) variables. Then, the objective function of the pursuer is designed based on the fuzzy comprehensive evaluation, and the analytical necessary conditions for the optimal control strategy are presented. Finally, a hybrid method combining the multi-objective genetic algorithm and the multiple shooting method is proposed to obtain the solution of the orbital pursuit-evasion-defense problem. The simulation results show that the proposed control strategy can handle the orbital pursuit-evasion-defense problem effectively. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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