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7 pages, 188 KiB

Open AccessEditorial

Special Issue “Complex Dynamic System Modelling, Identification and Control”

by Quanmin Zhu, Giuseppe Fusco, Jing Na, Weicun Zhang and Ahmad Taher Azar

Entropy 2022, 24(3), 380; https://doi.org/10.3390/e24030380 - 08 Mar 2022

Cited by 1 | Viewed by 1710

Abstract

Systems are naturally or purposely formed with functional components and connection structures [...] Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

Research

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14 pages, 3139 KiB

Open AccessArticle

X-ray Pulsar Signal Denoising Based on Variational Mode Decomposition

by Qiang Chen, Yong Zhao and Lixia Yan

Entropy 2021, 23(9), 1181; https://doi.org/10.3390/e23091181 - 08 Sep 2021

Cited by 3 | Viewed by 1822

Abstract

Pulsars, especially X-ray pulsars detectable for small-size detectors, are highly accurate natural clocks suggesting potential applications such as interplanetary navigation control. Due to various complex cosmic background noise, the original pulsar signals, namely photon sequences, observed by detectors have low signal-to-noise ratios (SNRs) [...] Read more.

Pulsars, especially X-ray pulsars detectable for small-size detectors, are highly accurate natural clocks suggesting potential applications such as interplanetary navigation control. Due to various complex cosmic background noise, the original pulsar signals, namely photon sequences, observed by detectors have low signal-to-noise ratios (SNRs) that obstruct the practical uses. This paper presents the pulsar denoising strategy developed based on the variational mode decomposition (VMD) approach. It is actually the initial work of our interplanetary navigation control research. The original pulsar signals are decomposed into intrinsic mode functions (IMFs) via VMD, by which the Gaussian noise contaminating the pulsar signals can be attenuated because of the filtering effect during signal decomposition and reconstruction. Comparison experiments based on both simulation and HEASARC-archived X-ray pulsar signals are carried out to validate the effectiveness of the proposed pulsar denoising strategy. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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23 pages, 1491 KiB

Open AccessArticle

Robust Controller Design for Multi-Input Multi-Output Systems Using Coefficient Diagram Method

by Kai Liu, Fanwei Meng, Shengya Meng and Chonghui Wang

Entropy 2021, 23(9), 1180; https://doi.org/10.3390/e23091180 - 08 Sep 2021

Cited by 1 | Viewed by 1596

Abstract

The coupling between variables in the multi-input multi-output (MIMO) systems brings difficulties to the design of the controller. Aiming at this problem, this paper combines the particle swarm optimization (PSO) with the coefficient diagram method (CDM) and proposes a robust controller design strategy [...] Read more.

The coupling between variables in the multi-input multi-output (MIMO) systems brings difficulties to the design of the controller. Aiming at this problem, this paper combines the particle swarm optimization (PSO) with the coefficient diagram method (CDM) and proposes a robust controller design strategy for the MIMO systems. The decoupling problem is transformed into a compensator parameter optimization problem, and PSO optimizes the compensator parameters to reduce the coupling effect in the MIMO systems. For the MIMO system with measurement noise, the effectiveness of CDM in processing measurement noise is analyzed. This paper gives the control design steps of the MIMO systems. Finally, simulation experiments of four typical MIMO systems demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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15 pages, 2464 KiB

Open AccessArticle

Adaptive Fixed-Time Neural Network Tracking Control of Nonlinear Interconnected Systems

by Yang Li, Jianhua Zhang, Xinli Xu and Cheng Siong Chin

Entropy 2021, 23(9), 1152; https://doi.org/10.3390/e23091152 - 01 Sep 2021

Cited by 9 | Viewed by 1722

Abstract

In this article, a novel adaptive fixed-time neural network tracking control scheme for nonlinear interconnected systems is proposed. An adaptive backstepping technique is used to address unknown system uncertainties in the fixed-time settings. Neural networks are used to identify the unknown uncertainties. The [...] Read more.

In this article, a novel adaptive fixed-time neural network tracking control scheme for nonlinear interconnected systems is proposed. An adaptive backstepping technique is used to address unknown system uncertainties in the fixed-time settings. Neural networks are used to identify the unknown uncertainties. The study shows that, under the proposed control scheme, each state in the system can converge into small regions near zero with fixed-time convergence time via Lyapunov stability analysis. Finally, the simulation example is presented to demonstrate the effectiveness of the proposed approach. A step-by-step procedure for engineers in industry process applications is proposed. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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21 pages, 5841 KiB

Open AccessArticle

A Comprehensive Diagnosis Method of Rolling Bearing Fault Based on CEEMDAN-DFA-Improved Wavelet Threshold Function and QPSO-MPE-SVM

by Yi Wang, Chuannuo Xu, Yu Wang and Xuezhen Cheng

Entropy 2021, 23(9), 1142; https://doi.org/10.3390/e23091142 - 31 Aug 2021

Cited by 20 | Viewed by 2202

Abstract

A comprehensive fault diagnosis method of rolling bearing about noise interference, fault feature extraction, and identification was proposed. Based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), detrended fluctuation analysis (DFA), and improved wavelet thresholding, a denoising method of CEEMDAN-DFA-improved wavelet [...] Read more.

A comprehensive fault diagnosis method of rolling bearing about noise interference, fault feature extraction, and identification was proposed. Based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), detrended fluctuation analysis (DFA), and improved wavelet thresholding, a denoising method of CEEMDAN-DFA-improved wavelet threshold function was presented to reduce the distortion of the noised signal. Based on quantum-behaved particle swarm optimization (QPSO), multiscale permutation entropy (MPE), and support vector machine (SVM), the QPSO-MPE-SVM method was presented to construct the fault-features sets and realize fault identification. Simulation and experimental platform verification showed that the proposed comprehensive diagnosis method not only can better remove the noise interference and maintain the original characteristics of the signal by CEEMDAN-DFA-improved wavelet threshold function, but also overcome overlapping MPE values by the QPSO-optimizing MPE parameters to separate the features of different fault types. The experimental results showed that the fault identification accuracy of the fault diagnosis can reach 95%, which is a great improvement compared with the existing methods. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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27 pages, 744 KiB

Open AccessArticle

Robust Stabilization and Synchronization of a Novel Chaotic System with Input Saturation Constraints

by Ahmad Taher Azar, Fernando E. Serrano, Quanmin Zhu, Maamar Bettayeb, Giuseppe Fusco, Jing Na, Weicun Zhang and Nashwa Ahmad Kamal

Entropy 2021, 23(9), 1110; https://doi.org/10.3390/e23091110 - 27 Aug 2021

Cited by 10 | Viewed by 2365

Abstract

In this paper, the robust stabilization and synchronization of a novel chaotic system are presented. First, a novel chaotic system is presented in which this system is realized by implementing a sigmoidal function to generate the chaotic behavior of this analyzed system. A [...] Read more.

In this paper, the robust stabilization and synchronization of a novel chaotic system are presented. First, a novel chaotic system is presented in which this system is realized by implementing a sigmoidal function to generate the chaotic behavior of this analyzed system. A bifurcation analysis is provided in which by varying three parameters of this chaotic system, the respective bifurcations plots are generated and evinced to analyze and verify when this system is in the stability region or in a chaotic regimen. Then, a robust controller is designed to drive the system variables from the chaotic regimen to stability so that these variables reach the equilibrium point in finite time. The robust controller is obtained by selecting an appropriate robust control Lyapunov function to obtain the resulting control law. For synchronization purposes, the novel chaotic system designed in this study is used as a drive and response system, considering that the error variable is implemented in a robust control Lyapunov function to drive this error variable to zero in finite time. In the control law design for stabilization and synchronization purposes, an extra state is provided to ensure that the saturated input sector condition must be mathematically tractable. A numerical experiment and simulation results are evinced, along with the respective discussion and conclusion. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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17 pages, 6185 KiB

Open AccessArticle

Observer Based Multi-Level Fault Reconstruction for Interconnected Systems

by Mei Zhang, Boutaïeb Dahhou, Qinmu Wu and Zetao Li

Entropy 2021, 23(9), 1102; https://doi.org/10.3390/e23091102 - 25 Aug 2021

Cited by 5 | Viewed by 1408

Abstract

The problem of local fault (unknown input) reconstruction for interconnected systems is addressed in this paper. This contribution consists of a geometric method which solves the fault reconstruction (FR) problem via observer based and a differential algebraic concept. The fault diagnosis (FD) problem [...] Read more.

The problem of local fault (unknown input) reconstruction for interconnected systems is addressed in this paper. This contribution consists of a geometric method which solves the fault reconstruction (FR) problem via observer based and a differential algebraic concept. The fault diagnosis (FD) problem is tackled using the concept of the differential transcendence degree of a differential field extension and the algebraic observability. The goal is to examine whether the fault occurring in the low-level subsystem can be reconstructed correctly by the output at the high-level subsystem under given initial states. By introducing the fault as an additional state of the low subsystem, an observer based approached is proposed to estimate this new state. Particularly, the output of the lower subsystem is assumed unknown, and is considered as auxiliary outputs. Then, the auxiliary outputs are estimated by a sliding mode observer which is generated by using global outputs and inverse techniques. After this, the estimated auxiliary outputs are employed as virtual sensors of the system to generate a reduced-order observer, which is caplable of estimating the fault variable asymptotically. Thus, the purpose of multi-level fault reconstruction is achieved. Numerical simulations on an intensified heat exchanger are presented to illustrate the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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17 pages, 5104 KiB

Open AccessArticle

Improved Adaptive Augmentation Control for a Flexible Launch Vehicle with Elastic Vibration

by Aiping Pang, Hongbo Zhou, Wenjie Cai and Jing Zhang

Entropy 2021, 23(8), 1058; https://doi.org/10.3390/e23081058 - 16 Aug 2021

Cited by 4 | Viewed by 1896

Abstract

The continuous development of spacecraft with large flexible structures has resulted in an increase in the mass and aspect ratio of launch vehicles, while the wide application of lightweight materials in the aerospace field has increased the flexible modes of launch vehicles. In [...] Read more.

The continuous development of spacecraft with large flexible structures has resulted in an increase in the mass and aspect ratio of launch vehicles, while the wide application of lightweight materials in the aerospace field has increased the flexible modes of launch vehicles. In order to solve the problem of deviation from the nominal control or even destabilization of the system caused by uncertainties such as unknown or unmodelled dynamics, frequency perturbation of the flexible mode, changes in its own parameters, and external environmental disturbances during the flight of such large-scale flexible launch vehicles with simultaneous structural deformation, rigid-elastic coupling and multimodal vibrations, an improved adaptive augmentation control method based on model reference adaption, and spectral damping is proposed in this paper, including a basic PD controller, a reference model, and an adaptive gain adjustment based on spectral damping. The baseline PD controller was used for flight attitude control in the nominal state. In the non-nominal state, the spectral dampers in the adaptive gain adjustment law extracted and processed the high-frequency signal from the tracking error and control-command error between the reference model and the actual system to generate the adaptive gain. The adjustment gain was multiplied by the baseline controller gain to increase/decrease the overall gain of the system to improve the system’s performance and robust stability, so that the system had the ability to return to the nominal state when it was affected by various uncertainties and deviated from the nominal state, or even destabilized. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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14 pages, 569 KiB

Open AccessArticle

Cluster-Delay Mean Square Consensus of Stochastic Multi-Agent Systems with Impulse Time Windows

by Huan Luo, Yinhe Wang, Ruidian Zhan, Xuexi Zhang, Haoxiang Wen and Senquan Yang

Entropy 2021, 23(8), 1033; https://doi.org/10.3390/e23081033 - 11 Aug 2021

Cited by 4 | Viewed by 1676

Abstract

This paper investigates the cluster-delay mean square consensus problem of a class of first-order nonlinear stochastic multi-agent systems with impulse time windows. Specifically, on the one hand, we have applied a discrete control mechanism (i.e., impulsive control) into the system instead of a [...] Read more.

This paper investigates the cluster-delay mean square consensus problem of a class of first-order nonlinear stochastic multi-agent systems with impulse time windows. Specifically, on the one hand, we have applied a discrete control mechanism (i.e., impulsive control) into the system instead of a continuous one, which has the advantages of low control cost, high convergence speed; on the other hand, we considered the existence of impulse time windows when modeling the system, that is, a single impulse appears randomly within a time window rather than an ideal fixed position. In addition, this paper also considers the influence of stochastic disturbances caused by fluctuations in the external environment. Then, based on algebraic graph theory and Lyapunov stability theory, some sufficiency conditions that the system must meet to reach the consensus state are given. Finally, we designed a simulation example to verify the feasibility of the obtained results. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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12 pages, 2158 KiB

Open AccessArticle

Adaptive Fixed-Time Control of Strict-Feedback High-Order Nonlinear Systems

by Yang Li, Jianhua Zhang, Xiaoyun Ye and Cheng Siong Chin

Entropy 2021, 23(8), 963; https://doi.org/10.3390/e23080963 - 27 Jul 2021

Cited by 4 | Viewed by 2186

Abstract

This paper examines the adaptive control of high-order nonlinear systems with strict-feedback form. An adaptive fixed-time control scheme is designed for nonlinear systems with unknown uncertainties. In the design process of a backstepping controller, the Lyapunov function, an effective controller, and adaptive law [...] Read more.

This paper examines the adaptive control of high-order nonlinear systems with strict-feedback form. An adaptive fixed-time control scheme is designed for nonlinear systems with unknown uncertainties. In the design process of a backstepping controller, the Lyapunov function, an effective controller, and adaptive law are constructed. Combined with the fixed-time Lyapunov stability criterion, it is proved that the proposed control scheme can ensure the stability of the error system in finite time, and the convergence time is independent of the initial condition. Finally, simulation results verify the effectiveness of the proposed control strategy. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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12 pages, 4120 KiB

Open AccessArticle

Structured H∞ Control for Spacecraft with Flexible Appendages

by Yuntian Zhang, Aiping Pang, Hui Zhu and Huan Feng

Entropy 2021, 23(8), 930; https://doi.org/10.3390/e23080930 - 22 Jul 2021

Cited by 5 | Viewed by 1608

Abstract

Spacecraft with large flexible appendages are characterized by multiple system modes. They suffer from inherent low-frequency disturbances in the operating environment that consequently result in considerable interference in the operational performance of the system. It is required that the control design ensures the [...] Read more.

Spacecraft with large flexible appendages are characterized by multiple system modes. They suffer from inherent low-frequency disturbances in the operating environment that consequently result in considerable interference in the operational performance of the system. It is required that the control design ensures the system’s high pointing precision, and it is also necessary to suppress low-frequency resonant interference as well as take into account multiple performance criteria such as attitude stability and bandwidth constraints. Aiming at the comprehensive control problem of this kind of flexible spacecraft, we propose a control strategy using a structured H-infinity controller with low complexity that was designed to meet the multiple performance requirements, so as to reduce the project cost and implementation difficulty. According to the specific resonant mode of the system, the design strategy of adding an internal mode controller, a trap filter, and a series PID controller to the structured controller is proposed, so as to achieve the comprehensive control goals through cooperative control of multiple control modules. A spacecraft with flexible appendages (solar array) is presented as an illustrative example in which a weighted function was designed for each performance requirement of the system (namely robustness, stability, bandwidth limit, etc.), and a structured comprehensive performance matrix with multiple performance weights and decoupled outputs was constructed. A structured H-infinity controller meeting the comprehensive performance requirements is given, which provides a structured integrated control method with low complexity for large flexible systems that is convenient for engineering practice, and provides a theoretical basis and reference examples for structured H-infinity control. The simulation results show that the proposed controller gives better control performance compared with the traditional H-infinity one, and can successfully suppress the vibration of large flexible appendages at 0.12 Hz and 0.66 Hz. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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21 pages, 731 KiB

Open AccessArticle

Constrained Active Fault Tolerant Control Based on Active Fault Diagnosis and Interpolation Optimization

by Kezhen Han, Changzhi Chen, Mengdi Chen and Zipeng Wang

Entropy 2021, 23(8), 924; https://doi.org/10.3390/e23080924 - 21 Jul 2021

Cited by 10 | Viewed by 1822

Abstract

A new active fault tolerant control scheme based on active fault diagnosis is proposed to address the component/actuator faults for systems with state and input constraints. Firstly, the active fault diagnosis is composed of diagnostic observers, constant auxiliary signals, and separation hyperplanes, all [...] Read more.

A new active fault tolerant control scheme based on active fault diagnosis is proposed to address the component/actuator faults for systems with state and input constraints. Firstly, the active fault diagnosis is composed of diagnostic observers, constant auxiliary signals, and separation hyperplanes, all of which are designed offline. In online applications, only a single diagnostic observer is activated to achieve fault detection and isolation. Compared with the traditional multi-observer parallel diagnosis methods, such a design is beneficial to improve the diagnostic efficiency. Secondly, the active fault tolerant control is composed of outer fault tolerant control, inner fault tolerant control and a linear-programming-based interpolation control algorithm. The inner fault tolerant control is determined offline and satisfies the prescribed optimal control performance requirement. The outer fault tolerant control is used to enlarge the feasible region, and it needs to be determined online together with the interpolation optimization. In online applications, the updated state estimates trigger the adjustment of the interpolation algorithm, which in turn enables control reconfiguration by implicitly optimizing the dynamic convex combination of outer fault tolerant control and inner fault tolerant control. This control scheme contributes to further reducing the computational effort of traditional constrained predictive fault tolerant control methods. In addition, each pair of inner fault tolerant control and diagnostic observer is designed integratedly to suppress the robust interaction influences between estimation error and control error. The soft constraint method is further integrated to handle some cases that lead to constraint violations. The effectiveness of these designs is finally validated by a case study of a wastewater treatment plant model. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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16 pages, 24254 KiB

Open AccessArticle

Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison

by Qian Li, Zhe Zhang and Fei Peng

Entropy 2021, 23(7), 864; https://doi.org/10.3390/e23070864 - 06 Jul 2021

Cited by 7 | Viewed by 2051

Abstract

This study investigates a critical hazard identification method for railway accident prevention. A new accident causation network is proposed to model the interaction between hazards and accidents. To realize consistency between the most likely and shortest causation paths in terms of hazards to [...] Read more.

This study investigates a critical hazard identification method for railway accident prevention. A new accident causation network is proposed to model the interaction between hazards and accidents. To realize consistency between the most likely and shortest causation paths in terms of hazards to accidents, a method for measuring the length between adjacent nodes is proposed, and the most-likely causation path problem is first transformed to the shortest causation path problem. To identify critical hazard factors that should be alleviated for accident prevention, a novel critical hazard identification model is proposed based on a controllability analysis of hazards. Five critical hazard identification methods are proposed to select critical hazard nodes in an accident causality network. A comparison of results shows that the combination of an integer programming-based critical hazard identification method and the proposed weighted direction accident causality network considering length has the best performance in terms of accident prevention. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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17 pages, 3509 KiB

Open AccessArticle

Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN

by Jinghui Pan, Lili Qu and Kaixiang Peng

Entropy 2021, 23(6), 751; https://doi.org/10.3390/e23060751 - 15 Jun 2021

Cited by 25 | Viewed by 2648

Abstract

This paper proposes a data-driven method-based fault diagnosis method using the deep convolutional neural network (DCNN). The DCNN is used to deal with sensor and actuator faults of robot joints, such as gain error, offset error, and malfunction for both sensors and actuators, [...] Read more.

This paper proposes a data-driven method-based fault diagnosis method using the deep convolutional neural network (DCNN). The DCNN is used to deal with sensor and actuator faults of robot joints, such as gain error, offset error, and malfunction for both sensors and actuators, and different fault types are diagnosed using the trained neural network. In order to achieve the above goal, the fused data of sensors and actuators are used, where both types of fault are described in one formulation. Then, the deep convolutional neural network is applied to learn characteristic features from the merged data to try to find discriminative information for each kind of fault. After that, the fully connected layer does prediction work based on learned features. In order to verify the effectiveness of the proposed deep convolutional neural network model, different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), conventional neural network (CNN) using the LeNet-5 method, and long-term memory network (LTMN) are investigated and compared with DCNN method. The results show that the DCNN fault diagnosis method can realize high fault recognition accuracy while needing less model training time. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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24 pages, 8988 KiB

Open AccessArticle

Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation

by Yusi Luan, Mengxuan Jiang, Zhenxiang Feng and Bei Sun

Entropy 2021, 23(4), 473; https://doi.org/10.3390/e23040473 - 16 Apr 2021

Cited by 5 | Viewed by 1780

Abstract

For an industrial process, the estimation of feeding composition is important for analyzing production status and making control decisions. However, random errors or even gross ones inevitably contaminate the actual measurements. Feeding composition is conventionally obtained via discrete and low-rate artificial testing. To [...] Read more.

For an industrial process, the estimation of feeding composition is important for analyzing production status and making control decisions. However, random errors or even gross ones inevitably contaminate the actual measurements. Feeding composition is conventionally obtained via discrete and low-rate artificial testing. To address these problems, a feeding composition estimation approach based on data reconciliation procedure is developed. To improve the variable accuracy, a novel robust M-estimator is first proposed. Then, an iterative robust hierarchical data reconciliation and estimation strategy is applied to estimate the feeding composition. The feasibility and effectiveness of the estimation approach are verified on a fluidized bed roaster. The proposed M-estimator showed better overall performance. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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22 pages, 1927 KiB

Open AccessFeature PaperArticle

Constrained Parameter Estimation for a Mechanistic Kinetic Model of Cobalt–Hydrogen Electrochemical Competition during a Cobalt Removal Process

by Yiting Liang, Yuanhua Zhang and Yonggang Li

Entropy 2021, 23(4), 387; https://doi.org/10.3390/e23040387 - 24 Mar 2021

Cited by 3 | Viewed by 1486

Abstract

A mechanistic kinetic model of cobalt–hydrogen electrochemical competition for the cobalt removal process in zinc hydrometallurgical was proposed. In addition, to overcome the parameter estimation difficulties arising from the model nonlinearities and the lack of information on the possible value ranges of parameters [...] Read more.

A mechanistic kinetic model of cobalt–hydrogen electrochemical competition for the cobalt removal process in zinc hydrometallurgical was proposed. In addition, to overcome the parameter estimation difficulties arising from the model nonlinearities and the lack of information on the possible value ranges of parameters to be estimated, a constrained guided parameter estimation scheme was derived based on model equations and experimental data. The proposed model and the parameter estimation scheme have two advantages: (i) The model reflected for the first time the mechanism of the electrochemical competition between cobalt and hydrogen ions in the process of cobalt removal in zinc hydrometallurgy; (ii) The proposed constrained parameter estimation scheme did not depend on the information of the possible value ranges of parameters to be estimated; (iii) the constraint conditions provided in that scheme directly linked the experimental phenomenon metrics to the model parameters thereby providing deeper insights into the model parameters for model users. Numerical experiments showed that the proposed constrained parameter estimation algorithm significantly improved the estimation efficiency. Meanwhile, the proposed cobalt–hydrogen electrochemical competition model allowed for accurate simulation of the impact of hydrogen ions on cobalt removal rate as well as simulation of the trend of hydrogen ion concentration, which would be helpful for the actual cobalt removal process in zinc hydrometallurgy. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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19 pages, 1081 KiB

Open AccessArticle

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

by Xue-Bo Jin, Xing-Hong Yu, Ting-Li Su, Dan-Ni Yang, Yu-Ting Bai, Jian-Lei Kong and Li Wang

Entropy 2021, 23(2), 219; https://doi.org/10.3390/e23020219 - 11 Feb 2021

Cited by 36 | Viewed by 2778

Abstract

Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses [...] Read more.

Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement’s causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network’s over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system’s big measurement data to improve prediction performance. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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14 pages, 1877 KiB

Open AccessArticle

A Modified FlowDroid Based on Chi-Square Test of Permissions

by Hongzhaoning Kang, Gang Liu, Zhengping Wu, Yumin Tian and Lizhi Zhang

Entropy 2021, 23(2), 174; https://doi.org/10.3390/e23020174 - 30 Jan 2021

Cited by 5 | Viewed by 1941

Abstract

Android devices are currently widely used in many fields, such as automatic control, embedded systems, the Internet of Things and so on. At the same time, Android applications (apps) always use multiple permissions, and permissions can be abused by malicious apps that disclose [...] Read more.

Android devices are currently widely used in many fields, such as automatic control, embedded systems, the Internet of Things and so on. At the same time, Android applications (apps) always use multiple permissions, and permissions can be abused by malicious apps that disclose users’ privacy or breach the secure storage of information. FlowDroid has been extensively studied as a novel and highly precise static taint analysis for Android applications. Aiming at the problem of complex detection and false alarms in FlowDroid, an improved static detection method based on feature permission and risk rating is proposed. Firstly, the Chi-square test is used to extract correlated permissions related to malicious apps, and mutual information is used to cluster the permissions to generate feature permission clusters. Secondly, risk calculation method based on permissions and combinations of permissions are proposed to identify dangerous data flows. Experiments show that this method can significantly improve detection efficiency while maintaining the accuracy of dangerous data flow detection. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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23 pages, 6966 KiB

Open AccessArticle

U-Model-Based Two-Degree-of-Freedom Internal Model Control of Nonlinear Dynamic Systems

by Ruobing Li, Quanmin Zhu, Pritesh Narayan, Alex Yue, Yufeng Yao and Mingcong Deng

Entropy 2021, 23(2), 169; https://doi.org/10.3390/e23020169 - 29 Jan 2021

Cited by 15 | Viewed by 2273

Abstract

This paper proposes a U-Model-Based Two-Degree-of-Freedom Internal Model Control (UTDF-IMC) structure with strength in nonlinear dynamic inversion, and separation of tracking design and robustness design. This approach can effectively accommodate modeling error and disturbance while removing those widely used linearization techniques for nonlinear [...] Read more.

This paper proposes a U-Model-Based Two-Degree-of-Freedom Internal Model Control (UTDF-IMC) structure with strength in nonlinear dynamic inversion, and separation of tracking design and robustness design. This approach can effectively accommodate modeling error and disturbance while removing those widely used linearization techniques for nonlinear plants/processes. To assure the expansion and applications, it analyses the key properties associated with the UTDF-IMC. For initial benchmark testing, computational experiments are conducted using MATLAB/Simulink for two mismatched linear and nonlinear plants. Further tests consider an industrial system, in which the IMC of a Permanent Magnet Synchronous Motor (PMSM) is simulated to demonstrate the effectiveness of the design procedure for potential industrial applications. Full article

(This article belongs to the Special Issue Complex Dynamic System Modelling, Identification and Control)

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