Forthcoming and Online First Articles

International Journal of Modelling, Identification and Control

International Journal of Modelling, Identification and Control (IJMIC)

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International Journal of Modelling, Identification and Control (34 papers in press)

Regular Issues

  • Outlier detection algorithm based on deviation characteristic   Order a copy of this article
    by Yong Wang, Hongbin Wang, Pengcheng Sun, Xinliang Yin 
    Abstract: Outlier mining focuses on researching rare events through detection and analysis to dig out the valuable knowledge from them. In the static data set environment, the traditional LOF algorithm calculates the local outlier factor through the whole data set and requires a lot of computing time. To solve this problem, the algorithm divides the data space into grids, and calculates the local outlier factor based on the centroids of the grids. Since the grid number is less than data point number, the time complexity is obviously reduced under acceptable error. When the new data points are added, it can rapidly detect outliers. The contrast experiment results show that the new algorithm can reduce the computation time and improve the efficiency, while achieving comparable accuracy.
    Keywords: outlier detection; local outlier factor; deviation characteristic; fast LOF detection algorithm.

  • Data-driven identification for nonlinear dynamic systems   Order a copy of this article
    by Sergey Lyshevski 
    Abstract: For nonlinear dynamic systems, this paper investigates problems of identification and parameter estimation. These problems are critical in aerial, electromechanical, robotic and other systems. Analysis and control of physical systems imply the use of adequate mathematical descriptions, ensuring sufficient fidelity. Particular challenges occur if systems exhibit oscillations, limit cycles and instabilities. We apply multivariate polynomials and model-to-system mismatched measures to solve identification problems during dynamic governance. Physics-consistent nonlinear models are parameterised, truncated and validated using matrix factorization schemes and algorithms. Heterogeneous measurements adverse the information content and obscure observed data. Singular value decomposition ensures algorithmic convergence and validity. Using simulations and experimental studies, a data-driven identification concept is demonstrated and validated.
    Keywords: dynamic systems; estimation; identification; nonlinear systems.

  • Quantised global prescribed performance control of unknown strict-feedback systems   Order a copy of this article
    by Wei Ding, Jin-Xi Zhang 
    Abstract: This paper is concerned with the reference tracking problem for the unknown strict-feedback systems subject to unmatched disturbances under quantized control. The quantization error and the model uncertainty yield challenges in fast and accurate tracking control. A quantized robust global prescribed performance control approach is given to address the problem. It achieves output tracking with arbitrarily predefined settling time and accuracy and ensures boundedness of all the signals involved in the control system for any initial condition. Furthermore, the proposed control exhibits significant simplicity. It does not invoke the techniques of approximation, identification, estimation, etc, to deal with model uncertainties. On the other hand, there is no need to calculate the intermediate control signal derivatives in the recursive design. The simulation study on a jet engine compressor illustrate the above theoretical findings.
    Keywords: control simplicity; global stability; nonlinear systems; prescribed performance; quantised control.

  • Grid frequency stabilisation under magnitude and generation rate constraints   Order a copy of this article
    by Hisham Soliman, Abdellah Benzaouia, Farag Ali El-Sheikhi, Kenan Buyukatak 
    Abstract: A new load frequency control (LFC) design in a multi-area power system is introduced in this paper. The proposed design tackles the problem of constraints on both control magnitude and its rate. With this design, the control limits (corresponding to a fully open/closed fuel valve) are not violated. Also, the generation rate constraint (GRC) is kept within the permissible range imposed in practice. Violation of such constraints may lead to instability or damage of mechanical parts. A pole placement method is used to solve this inverse problem. The resulting stabiliser is state feedback plus integral control. The proposed control avoids the complexity in modelling the nonlinear like of the GRC. Unlike other designs, the suggested control represents a link between the pole placement procedure and these constraints. To validate the efficacy of the proposed design, digital simulations for single and two-area power systems are presented in this paper. A comparison is made between the proposed control and the traditional control scheme, which confirms the proposed technique's supremacy.
    Keywords: constrained control; generation rate constraints; load frequency control; positive invariance; pole placement.
    DOI: 10.1504/IJMIC.2024.10059042
  • Advanced control of an autonomous torpedo-type underwater vehicle with integral backstepping techniques in the yaw plane   Order a copy of this article
    by Hattab Abdellilah, Yahiaoui Kamel 
    Abstract: Underwater robots, especially those that are autonomous, are some of the most difficult systems that require a robust controller to handle well the tasks given to them. The difficulty lies in the variation of the parameters of the autonomous underwater robot (inertia, hydrodynamics), the external disturbances, the nonlinearity of the model to be controlled, and the measurement errors due to the sensors. A robust control law such as the backstepping technique is very demanding for this type of system, thanks to its robustness in the face of disturbances. In this work, we discuss the application of the recoil control theory on the diving autonomous robot system. This article is based on the advanced control of the yaw plane. To improve the performance of our system we make a hybridisation between the integral controller and the backstepping controller. Several simulation tests were performed to test the effectiveness of the proposed commands.
    Keywords: diver robot autonomous; recoil control; integral control; mobile robot.

  • New approach for online parameters identification for non-holonomic mobile robots   Order a copy of this article
    by Jean Marie Lauhic Ndong Mezui, Dieurdonné Ekang, Donatien Nganga-Kouya, Maarouf Saad, Aime Francis Okou 
    Abstract: The dynamic models that represent robotic systems greatly condition the strategies used for the design of their control systems. The accuracy of the model parameter values can have a significant impact on the closed-loop system performance. This paper proposes an innovative method for the online parameters identification (IOPI) of a non-holonomic mobile robot. The proposed method enables to find the inertia accurately, mass and friction parameters in the robot's model and it does not require the drive wheel input torques to be sufficiently rich signals. The identification algorithm is based on the resolution of a system of linearly independent equations. The number of equations is equal to the number of unknown parameters to be estimated. The simulation results show that with the proposed method, the estimated parameters quickly converge to the real parameters of the non-holonomic mobile robot, contrary to the recursive least squares (RLS) method.
    Keywords: non-holonomic mobile robot; parameter estimation; iterative identification in continuous time of Newton; recursive least squares method.
    DOI: 10.1504/IJMIC.2023.10059396
  • FEM and Simscape modelling and LQG control of a two-Link rigid-flexible manipulator   Order a copy of this article
    by Tariq Darabseh 
    Abstract: This study models a two-link rigid-flexible manipulator using two methods: mathematical modelling with Lagrange's equations and the Finite Elements Method (FE model) and constructing the model with MATLAB's Simscape Multibody tool (Simscape Model). The FE model is validated by comparing it to a linearized version of the physical Simscape model, taking into account structural damping, hub inertias, and payload at the flexible link's endpoint. The resonance frequencies of the first three modes are analysed in the frequency domain using both models. The open-loop responses of both models are also compared in the time domain. A linear quadratic Gaussian (LQG) controller with a Kalman filter and integral action is developed and implemented using the Simscape Model. The simulation results using MATLAB show that the proposed method is efficient and practical for joint angle trajectory tracking and flexible link vibration control of the rigid-flexible manipulator. The LQG controller is shown to be more effective in reducing vibrations and enhancing performance of flexible link manipulators when compared to a Proportional-Derivative (PD) controller.
    Keywords: rigid-flexible manipulator; LQG controller; finite element method; Kalman filter; Simscape/MATLAB.

  • A backstepping controller based on RBFNN for mobile manipulator with unknown wheel slippage   Order a copy of this article
    by Soni Soni, Naveen Kumar 
    Abstract: In this paper, a backstepping-based control scheme for position tracking of the mobile manipulator under the presence of unknown wheel slippage, disturbances, and uncertainties is presented. The proposed control scheme takes the advantages of a backstepping controller because of its ability to handle uncertainties. Due to lack of prior knowledge regarding the dynamic characteristics of the mobile manipulator, smooth nonlinear dynamic functions are unknown, and for estimation of the same radial basis function neural network is used. The adaptive compensator is used at the kinematic and dynamic levels. At the kinematic level, the adaptive compensator diminishes the unwholesome effects of unknown wheel slips. At the dynamic level, the adaptive compensator supplies the influential robustness to vanquish the uncertainties because of external disturbances, reconstruction error, etc. The stability of the whole control system is validated with Lyapunov theory. The comparative simulation results are shown to confirm the efficiency and validity of the control scheme.
    Keywords: RBF neural network; backstepping controller; adaptive compensator; unknown wheel slippage; mobile manipulator.

  • Comparative study on mechanical dynamics of four lab-developed levitation prototypes   Order a copy of this article
    by Janardan Kundu, Vinod Kumar Yadav 
    Abstract: This paper highlights a broad investigation on system dynamics and its stability aspects of four different electromagnetic attraction type levitation prototypes. An exhaustive comparative study on levitation prototypes for a spherical object, two flat plates and a C-shaped plate has been discussed broadly. A spherical geometry has its advantages owing to the axis symmetry, whereas the plates have asymmetric symmetry. This geometry change brings a few significant challenges in design, modelling, controller design, fabrication and robustness of the set-up. Thus the study will help to develop more complex prototypes for real life applications e.g. MagLev (magnetic levitation) vehicle.
    Keywords: electromagnetic levitation; lead compensator; Windows comparator; frequency analysis.

  • An adaptive distributed access control model for IoT and fog computing environments   Order a copy of this article
    by Lalla Amina, Charaf, Imam, Alihamidi, Anass, Deroussi, Abdessalam Ait Madi, Adnane Addaim, My El Hassan Charaf 
    Abstract: In this paper, we present a framework for handling access control in fog-based implementations. In order to accommodate the distributed aspect of the proposed fog-based architecture, we extend XACML-based control to a distributed adaptive XACML model (DA-XACML). As a main contribution, we define the layers of our architecture, the communication scheme between the different components of the architecture, and how to integrate DA-XACML to overcome the drawbacks of centralised access control solutions in the Core Cloud. Finally, we provide a simulation of our approach in an eHealth case study using the iFogsim2 simulator. Based on simulation results, the fog-based implementation not only yielded low energy use, network utilisation, delay, and policy execution cost, but also improved simulation time compared with cloud-based implementations.
    Keywords: fog computing; IoT; security and privacy; XACML; access control.
    DOI: 10.1504/IJMIC.2023.10057172
  • Refined vector control structure and indirect MPPT for grid-connected DFIG-based wind energy conversion system, and appraisal on matrix converter interface   Order a copy of this article
    by Gayathri Kalimuthu, Jeevananthan Seenithangam 
    Abstract: The grid integration of wind energy conversion systems involving doubly fed induction generators is actualised by either the two-stage power converter (TSPC: back-to-back converter) or the single-stage power converter (SSPC: matrix converter). Though earlier vector control attempts have been successful in achieving independent active and reactive power control, they result in high ripples and poor power quality. This paper develops a refined vector control structure and incorporates a new indirect maximum power point tracking by exploiting the rule of thumb that for every wind speed, there is a unique optimal electromagnetic torque that extracts the maximum power. An arrived variant of the suggested control structure is suitable for the SSPC drive. The TSPC assumes a stator flux-oriented control (SFOC) for the rotor side converter and a voltage-oriented control for the grid side converter, while a single SFOC is employed in the SSPC. A real-time data driven simulation study on a 2 MW DFIG system demonstrates both disturbance rejection and tracking abilities.
    Keywords: doubly fed induction generator; single-stage power converter; two-stage power converter; Venturini modulation; wind energy conversion system; total harmonic distortion.

  • Sliding-mode-based adaptive control of chaotic systems with time delays   Order a copy of this article
    by Ahmad Taher Azar  
    Abstract: Time-delay chaotic systems in nonlinear dynamical systems have recently received a lot of attention. In this paper, a synchronisation phenomenon is applied to synchronise four identical chaotic systems with time delays. For this purpose, a sliding-mode-based adaptive control is used, which has many applications owing to its ease of implementation, quick response, and good transient performance, as well as its insensitivity to parameter uncertainties and external disturbances compared with other control techniques. Only a few papers have used this controller to conduct combined synchronisation of time-delay chaotic systems. The necessary condition for the stability of an error dynamical system is derived using Lyapunov stability theory. To support the theoretical reasoning, numerical simulation is performed in Mathematica program. The complexity of this methodology contributes to increased communication security. To improve communication precision, the secret message can be partitioned into numerous components and loaded into two master schemes.
    Keywords: sliding-mode-based adaptive control; nonlinear dynamical systems; time-delay chaotic systems; Lyapunov stability.
    DOI: 10.1504/IJMIC.2023.10054105
  • Modelling, bifurcation analysis, circuit design and FPGA-based implementation of a new chaotic jerk system exhibiting Hopf bifurcations   Order a copy of this article
    by Sundarapandian Vaidyanathan, Irene Moroz, Aceng Sambas, Daniel Clemente-Lopez, Jesus Manuel Munoz-Pacheco, Jose De Jesus Rangel-Magdaleno 
    Abstract: It is well-known that chaotic systems have several applications in scientific modelling and engineering fields such as encryption, cryptosystems, secure communication, etc. This work proposes a three-dimensional mechanical chaotic system with jerk dynamics. A detailed bifurcation analysis is conducted for the proposed chaotic system. It is shown that the proposed chaotic system has two equilibrium points which exhibit Hopf bifurcations. It is also shown that the proposed chaotic system depicts multistability and coexisting chaotic attractors. Using Multisim (Version 14), an electronic circuit is designed for the proposed mechanical chaotic system with jerk dynamics. As another engineering application, Field Programmable Gate Array (FPGA) design has been made for the proposed mechanical jerk chaotic system. Euler’s finite-difference method is used for our FPGA design. A hardware implementation of the FPGA-based design is performed in this work and experimental results are given in detail.
    Keywords: mechanical systems; jerk systems; chaos; chaotic systems; multi-stability; Hopf bifurcations; MultiSim design; circuit design; FPGA implementation; Euler's method.
    DOI: 10.1504/IJMIC.2023.10054238
  • Mathematical model for impact of awareness of COVID-19 vaccination among the youth of Kenya   Order a copy of this article
    by Ancent Makau Kimulu, Abayomi Samuel Oke, Mark Kimathi, Charles Ndambuki Muli, Samuel Musili Mwalili, Winifred Nduku Mutuku 
    Abstract: Approximately 124 million cases of COVID-19 infections have been confirmed; with over 2.7 million deaths globally. Vaccines have been developed to control and contain the spread of COVID-19. However, due to the combined effects of misinformation and unawareness about the vaccines, 52% of the Kenyan youths (aged 18-35 years) are hesitantly waiting to see what happens to the vaccinated. As a result, this study models the effects of awareness on vaccine uptake among the youths in Kenya through the use of televisions, radios and social media platforms. The disease-free equilibrium (DFE) point and endemic equilibrium point of the model are obtained by setting the derivatives to zero. The reproduction number are determined by using the next-generation matrix. The condition for stability of the DFE is shown in relation to the reproduction number. The DFE was stable when the reproduction number R_0 < 1 and unstable when R_0 > 1. It is found that both means of awareness reduced the COVID-19 infections. A simultaneous use of the methods increases the number of Kenyan youths taking the COVID-19 vaccines and consequently, further reducing the number of infections among the youths.
    Keywords: COVID-19; next-generation matrix; coronavirus; awareness; vaccination.
    DOI: 10.1504/IJMIC.2023.10058163
  • Modelling of creep deformation in an annular rotating disc composed of Si-Ti-C-O fibre-bonded ceramic matrix composite using Seth's transition theory   Order a copy of this article
    by Shivdev Shahi, Gagandeep Kaur, Abhishek Sharma, P. Basker 
    Abstract: Mathematical modelling of creep deformations in annular discs experiencing high centrifugal forces using transition theory is of much significance owing to its well established reliability. In this paper, transition theory has been implemented to obtain these stresses, strain rates and displacement in a disc composed of Si-Ti-C-O fibre-bonded ceramic matrix composite, which exhibits transversely isotropic macro and micro structural symmetry. The disc has a circular perforation at the centre. The analytical solution for creep transition state further uses the material constants that have been taken from available literature or have been obtained from the transmitted wave velocities measured by a double-through-transmission method. The results of the analytical solution are plotted graphically for varying radii ratios. It is observed that with increase in centrifugal forces, there is an increase in the magnitude of radial and circumferential stress concentrations at the internal surface of discs which diminish outwards. Likewise the strain rates have been observed.
    Keywords: creep; composite; ceramic; stresses; strain.
    DOI: 10.1504/IJMIC.2023.10055100
  • Intrusion detection using a SV-SMOTE method in IoMT   Order a copy of this article
    by Yonglai Zhang 
    Abstract: Intrusion detection for the internet of medical things (IoMT) is particularly important because of the problem of network attacks. Network security has become more serious during the COVID-19 pandemic. To address the problem that edge servers in IoMT are vulnerable to network traffic attacks, we propose a SV-SMOTE oversampling algorithm, and construct an IoMT intrusion detection model with machine learning algorithms as the main body. Using a benchmark dataset of network attacks, the random forest, oversampling and XGBoost algorithms are used in the proposed model for intrusion detection comparison experiments, respectively. This study makes a significant research contribution by proposing a new methodological approach to unbalanced data sets, which can lead to improved performance of classification models. The experimental results show that the proposed method can effectively detect network traffic attacks.
    Keywords: SV-SMOTE; random forest; XGBoost; internet of medical things; IoMT; intrusion detection.
    DOI: 10.1504/IJMIC.2023.10055523
  • Upgradation of the automatic voltage regulator dynamic response by an optimally tuned fractional order controller   Order a copy of this article
    by Manjusha Silas, Surekha Bhusnur 
    Abstract: The performance of an automatic voltage regulator (AVR) system is of prime importance in the power system. This work contributes a significant approach to enhance the performance of an AVR system using a fractional order PID (F OP ID
    Keywords: AVR system; (F OP IλDµ) controller; optimiser algorithm; robust control; cost Function.
    DOI: 10.1504/IJMIC.2023.10055524
  • Boundary control for prescribed-time stabilization of delayed parabolic reaction-diffusion systems   Order a copy of this article
    by Qian Ye, Wei Wu 
    Abstract: This work addresses the boundary control problem for prescribed-time stabilization of a parabolic reaction-diffusion system (PRDS) with time delay via the backstepping-based approach and Razumikhin method. The invertible Volterra transformation is used to convert the PRDS into a suitable prescribed-time stable PRDS with time-dependent coefficient, which is related to time-dependent kernel function. The obtained kernel partial differential equations are proven to be well-posed using the successive approximation. The boundary controller is designed by solving the kernel equations to guarantee the prescribed-time stabilization of the delayed PRDS. Finally, two numerical examples are given to testify the feasibility of the proposed methodology.
    Keywords: reaction-diffusion system; distributed parameter system; boundary control; time delay; prescribed-time stabilization; backstepping approach; Razumikhin method.
    DOI: 10.1504/IJMIC.2023.10055802
  • Interval observer-based fault-tolerant control for discrete-time switched linear system   Order a copy of this article
    by Leila Dadi, Haifa Ethabet, Mohamed Aoun 
    Abstract: This work presents the problem of interval estimation and passive Faut Tolerant Control (FTC) for discrete-time Switched Linear Systems (SLS). The considered systems are affected by disturbances, measurement noises and actuator faults. The uncertainties are supposed to be unknown but bounded with a priori known bounds. Based on a state feedback, a FTC is designed to maintain desired performances and preserve stability conditions in presence of actuator faults. The control law is designed by means of interval observer structure, guaranteeing interval estimation, where the stability conditions are expressed in terms of Linear Matrix Inequalities (LMIs). Numerical simulations are provided to demonstrate the efficiency of the proposed method.
    Keywords: fault tolerant control; actuator fault; discrete-time switched systems; hybrid systems.
    DOI: 10.1504/IJMIC.2023.10055999
  • Distributed set-membership state estimation based on a zonotope method for linear parameter-varying systems   Order a copy of this article
    by Yanfei Zhu, Jiahao Yu, Chuanjiang Li, Ya Gu 
    Abstract: This paper considers the distributed set-membership state estimation (SMSE) issue for linear parameter-varying (LPV) systems with unknown but bounded (UBB) noise and disturbance. A distributed set-membership estimation method employing zonotopes has been presented to estimate the true states of the systems. The estimated errors are restricted to a family of zonotopes satisfying the dependent constraint condition. By using the F-radius of the zonotopes minimum theory, the appropriate estimators are designed and the corresponding parameters are computed. Furthermore, a recursive state estimation algorithm is introduced to address this research issue. The persuasiveness of the approach can be demonstrated through numerical simulation examples.
    Keywords: distributed set-membership estimation; unknown but bounded noise; zonotope-dependent constraint.
    DOI: 10.1504/IJMIC.2023.10056513
  • Coyote optimisation algorithm for separable nonlinear models using chaotic maps technique   Order a copy of this article
    by Xixi Ji, Jing Chen, Xia Yin 
    Abstract: In this paper, a new Chebyshev chaotic map-based chaotic coyote optimization algorithm (CCOA) is applied to identify a separable nonlinear model. The CCOA uses chaotic signals instead of random numbers in identification process to increase non-repetitiveness and ergodicity. Compared with the particle swarm optimisation (PSO) and coyote optimisation algorithm (COA), the CCOA algorithm can improve the estimation accuracy and increase the parameter estimation convergence rate. Thus, the proposed algorithm can be widely used in engineering practices. To validate the developed algorithm, a series of comparative experiments are conducted. The effectiveness of the proposed algorithm is verified by the simulation results.
    Keywords: chaotic coyote optimisation algorithm; chaotic signal; separable nonlinear model; parameter estimation.
    DOI: 10.1504/IJMIC.2023.10057075
  • Stochastic and continuous Petri nets approximation of Markovian model   Order a copy of this article
    by Hamid El-Moumen, El Akchioui Nabil, Mohammed Hassani Zerrouk 
    Abstract: Stochastic Petri nets (SPN) or Markov models (MC) are often more effective in the reliability analysis of discrete event systems. However, they present a problem of combinatorial explosion of the number of states when the systems present several interdependent components. This problem limits the use of the MC. The SPN is considered a Markov estimator, but it has a slow convergence in the calculations of stationary state probabilities. The continuous Petri nets (CPN) are developed to accelerate this convergence by capturing the SPN behaviour. This study considers three different Petri nets (PN) types. Simulations with the MC, SPN, and CPN, are presented and compared in different PN types. The obtained results show that the MC and the SPN have identical behaviour in the general case. Furthermore, the CPN exhibits identical behaviour in the first type, which is not the case in the other two types.
    Keywords: reliability analysis; Markov model; reachability graph; combinatorial explosion; stationary state; stochastic Petri nets; fluidification; continuous Petri nets.
    DOI: 10.1504/IJMIC.2023.10057520
  • Capacity optimisation of rural distributed energy system based on two-stage robust optimisation algorithm   Order a copy of this article
    by Juan Wang, Sirui Wang, Fengzhong Zhang, Xueyang Sun 
    Abstract: In order to improve the energy utilisation of rural waste, a rural distributed energy system including wind energy, photovoltaic energy, biogas energy and energy storage is proposed in this paper. The capacity of the rural distributed energy system is optimised based on the two-stage robust optimisation model. The annualised life cycle cost, the differentiated time-of-use electricity price, as well as the discrete wind turbine capacity allocation has been considered in the two-stage robust optimisation model. The results show that this distributed energy system, including wind energy, photovoltaic energy, biogas energy and energy storage, is suitable for rural energy distributed systems, it has excellent multi-energy complementary characteristics, which leads to a high utilisation rate of renewable energy. The capacity configuration without scheduling capacity can be calculated directly by the two-stage robust optimisation model. The uncertainty on the source and load side can be fully considered in the two-stage robust optimisation model, and it has excellent economy and security in the
    Keywords: distributed energy; biogas power generation; two-stage robust optimisation; uncertainty.
    DOI: 10.1504/IJMIC.2023.10057523
  • Multi-discriminant feature fall detection algorithm based on joints   Order a copy of this article
    by Jimin Lai, Tonghui He 
    Abstract: Traditional fall detection algorithm is difficult to accurately extract and recognise human posture features, and easy to lose feature joints in the process of falling, resulting in low detection accuracy. Therefore, this paper proposes a multi-discriminant feature fall detection algorithm based on joints for nursing homes, medical rehabilitation centres and other places. Firstly, the initial features of human posture are obtained by the improved VGG-19 feature extraction model, and the initial position of the joints are obtained and coded by adding a residual module. Secondly, the decoder network is used to complete deconvolution and upsampling operations to achieve greater fine-grained resolution. Finally, the image pose refinement module is designed to analyse the relationship between different adjacent feature nodes, so as to realise the accurate identification of the node position when the fall occurs. On this basis, the corresponding fall discriminant characteristics are proposed to achieve the detection of the elderly fall action. The results show that the proposed algorithm is more accurate and effective than other traditional algorithms on some datasets.
    Keywords: fall detection; convolutional neural network; residual module.
    DOI: 10.1504/IJMIC.2023.10058394
  • A new nonlinear PID controller design for a quadrotor system using teaching learning based optimisation algorithm   Order a copy of this article
    by Naima Bouhabza, Kara Kamel 
    Abstract: In this paper, a novel nonlinear proportional integral derivative controller based on the meta-heuristic optimisation technique is suggested. Owing to its straightforward implementation and structure, the proportional integral derivative controller is frequently employed in nonlinear system control. The teaching-learning-based optimisation algorithm, owing to its effectiveness, rapidity, and minimum initialisation parameter required, has gained the attention of a significant number of researchers. The quadrotor's actuation dynamics are controlled by nonlinear proportional integral derivative controllers. Moreover, under-actuated dynamics use the same controller mechanism. For each controller, six parameters are tuned using the integral time absolute error criteria. Through numerical simulation, the efficiency and control performance of the suggested scheme are proven and contrasted with those of the linear proportional integral derivative controller and the sliding mode control. The simulation research demonstrates the effectiveness and successful performance of the recommended control technique in terms of the transient response characteristics, tracking precision, and perturbation rejection.
    Keywords: quadrotor; teaching learning based optimisation; TLBO; optimisation; nonlinear PID control.
    DOI: 10.1504/IJMIC.2023.10058548

Special Issue on: Modelling, Prediction, and Control of Complex Systems

  • Research on noise field of PMSG demagnetisation fault   Order a copy of this article
    by Hailiang Zhao, Ran An, Xiangfeng Li, Wei Yang, Zhiyan Zhang, Chengwei Jiang 
    Abstract: In this paper, the mathematical models of the radial electromagnetic force and vibration noise of the PMSG is established, and the influence of the demagnetisation fault on them is analysed theoretically. A 1 kW, 12-pole prototype is studied, and the two-dimensional electromagnetic field and three-dimensional noise field models of the PMSG are established, and the radial electromagnetic force is used as the excitation to couple the electromagnetic vibration and noise. The operation states of the PMSG under normal and different demagnetisation fault conditions are simulated, and the simulation results show that the air gap flux density shows a large distortion under the demagnetisation fault conditions. The fundamental wave amplitude decreases, the sound pressure level distribution of the noise field presents a chaotic state, and the sound pressure increases significantly.
    Keywords: PMSG; demagnetisation fault; noise field; electromagnetic field.

  • Cooperative spectrum sensing based on locally linear embedding and adaboost in dynamic fading channel   Order a copy of this article
    by Yanhui Wang, Dongliang Bian, Jun Pan 
    Abstract: In mountainous areas and dense forests, the performance of spectrum sensing is largely degraded due to factors such as shadow fading and noise uncertainty, which results in serious consequences of wasting spectrum resources. To overcome these problems, a novel cooperative spectrum sensing method based on locally linear embedding (LLE) and adaboost is proposed. This method addresses the characteristics of dynamically fading channels and does not rely on any a priori information. Firstly, cognitive radio (CR) users with excellent performance are selected to participate in spectrum sensing, while later important information components of nonlinear data in the received signal are obtained through LLE, and finally the excellent classification performance of adaboost is used to achieve accurate sensing of the main user signal. Comparative experiments are conducted in a low SNR environment, the proposed algorithm can effectively obtain the received signal feature information and accurately achieve spectrum sensing.
    Keywords: cooperative spectrum sensing; CR user selection; locally linear embedding; adaboost.

  • A series arc fault diagnosis method based on random forest model   Order a copy of this article
    by Qianhong Hou, Yongxin Chou, Jicheng Liu, Haifeng Mao, Mingda Lou 
    Abstract: The current of series arc fault is too weak to be detected by the circuit breaker, which is one of the causes of electrical fires. Therefore, an intelligent diagnosis method of series arc fault based on random forest (RF) is proposed in this study. Firstly, the high-frequency current signals of six kinds of load were collected as experimental data. Then, thirteen features were extracted from time domain and frequency domain, and the feature was reduced to four dimensions by principal component analysis. Finally, a classifier for series arc fault diagnosis is designed using RF. The experimental data in this study were collected by the low-voltage AC series arc fault data acquisition device developed by ourselves. The identification accuracy of series arc fault is 99.95
    Keywords: arc fault detection; intelligent diagnosis; random forest; feature extraction; principal component analysis.

  • EEG-based epileptic seizure state detection using deep learning   Order a copy of this article
    by Vibha Patel, Dharmendra Bhatti, Amit Ganatra, Jaishree Tailor 
    Abstract: Artificial intelligence-assisted diagnostics are booming with advanced computing power and technology. An automated approach to detect the seizure state from EEG recordings is highly desirable as the manual approach is tedious, time-consuming, and prone to errors. Our work proposes a hybrid deep learning architecture for automated seizure state detection from long-term patient-specific EEG. The architecture uses one-dimensional Convolutional Neural Network (1D-CNN) and stacked Long Short-Term Memory networks (LSTM). An open-source epilepsy dataset, CHB-MIT, is used in this work for experiments. The synthetic Minority Oversampling Technique (SMOTE) is used for handling class imbalance issues. Our proposed approach achieves an average of 90% accuracy, sensitivity, and specificity with an AUC value of 0.96 and an FPR of 0.10. This performance is remarkable, considering varying EEG channels, channel montages, and EEG durations. Our work facilitates seizure detection devices for faster and more precise decision-making for epilepsy treatment.
    Keywords: artificial intelligence; machine learning; deep learning; epileptic seizure detection; convolutional neural network; long short term memory network.

  • Simulation and parametric optimal design of active radial magnetic fluid bearing   Order a copy of this article
    by Liwen Chen, Jianhua Zhao, Xiaochen Wu, Jisheng Zhao, Jia Deng 
    Abstract: This paper designs a magnetic-fluid bearing with electromagnetic-static pressure dual support, and designs the structural parameters of the radial magnetic-hydraulic bearing. Based on the optimal bearing capacity, Matlab is used to optimize the bearing cavity diameter and oil film thickness, and it is concluded that the bearing capacity is better when the diameter of the bearing cavity is 10 mm and the thickness of the oil film is 30 ?m. Then, the fluid-solid-thermal coupling analysis of the magnetic fluid bearing was carried out, using Ansys software to analyse the influence of structural parameters on the bearing temperature rising and thermal deformation, and it was concluded that the thermal deformation of the magnetic fluid bearing was the smallest when the oil film thickness was 30 ?m and the axial length of the stator was 45 mm. These lay a theoretical basis for the structural optimisation design of the bearing.
    Keywords: magnetic fluid bearing; parametric design; bearing capacity analysis; fluid-solid-thermal coupling; structural parameter optimisation.

  • Lithium battery model online parameter identification method based on multi-innovation least square   Order a copy of this article
    by Jie Wu, Huigang Xu, Peiyi Zhu 
    Abstract: Accurate lithium-ion battery models are important for the accurate estimation of battery states as well as the simulation, design, and optimisation of new energy electric vehicles. However, the traditional recursive least squares method (RLS) exhibits disadvantages, such as low accuracy and long convergence time, when applied to the identification of battery model parameters. In this paper, the second-order RC equivalent circuit model of a lithium-ion battery is studied, and the online identification of model parameters by the multi-innovation least squares method is presented, which use multi-innovation to correct the difference between the observed value output at the previous time and the estimated value of the model identified at the previous time, which extends the single information of the original least squares method to multiple innovations. Data were collected through HPPC cycle conditions and NEDC conditions experiments. The accuracy and convergence speed of the conventional RLS estimation algorithm is described, to compare the absolute error between the estimated battery port voltage and the real value of the battery with different new interest lengths of the multi-innovation least squares algorithm. The experimental results show that the multi-innovation least squares algorithm with longer new interest length has higher accuracy and convergence speed, which verifies the effectiveness and feasibility of the proposed method.
    Keywords: lithium-ion battery; estimation of battery states; RLS; battery model parameters; multi-innovation least squares; HPPC cycle conditions; NEDC conditions experiments; absolute error.

  • A fast approximate entropy algorithm for heart rate variability analysis   Order a copy of this article
    by Haiping Yang, Lijuan Chou, Yongxin Chou, Jicheng Liu 
    Abstract: Approximate entropy is widely used in medical biological signal processing. However, due to the high complexity and time-consuming of approximate entropy calculation, it is generally only used for offline signal processing. In this study, the calculation process of approximate entropy is optimized. The goal is to shorten the running time of the algorithm without changing the approximate entropy value. The correctness and timeliness of the improved algorithm are verified by random number, and the improved algorithm is applied to HRV signal. Simulation results show that the improved algorithm can shorten the running time by 27-99 times, and the longer the cache length, the better the improvement effect. This makes it possible to process real-time biological signals with approximate entropy.
    Keywords: approximate entropy; microprocessor processing; ECG; HRV.

  • An improved grid voltage observation method for grid-connected inverter   Order a copy of this article
    by Shuping Song, Xiaobo Sun, Zhihao Zhang, Huping Bao 
    Abstract: The voltage observation method via sliding mode observer (SMO) is widely used in the predictive control of grid-connected inverter (GCI) without AC voltage sensor. However, conventional methods not only need complex phase and amplitude compensation, but also are greatly affected by low-order harmonics of grid voltage. Therefore, a simple method of voltage phase and amplitude compensation is proposed, which is based on the conventional SMO. Besides, an improved phase-locked loop (PLL) is also adopted to reduce the interference of low-order harmonics in power grid. The improved PLL eliminates the interference of 5th and 7th harmonics and realizes the accurate observation of power grid frequency and angle. The effectiveness of the proposed method is verified by MATLAB/Simulink simulation.
    Keywords: grid voltage observation; predictive control; grid-connected inverter; PLL; SMO.

  • Highly efficient on-line stochastic gradient and sliding window stochastic gradient signal modelling methods for multi-frequency signals   Order a copy of this article
    by Guanglei Song, Ling Xu 
    Abstract: This paper designs the signal parameter identification methodology for the signal which is composed of the sine components and cosine components. With the help of the gradient search, a stochastic gradient modelling method is presented to estimate all of the trait parameters of the multiple sine-cosine components. Further, some improvement schemes are designed to be aimed at enhancing the precision and convergence speed. Moreover, a rolling optimisation loss function based on the cumulated dynamic measurements is proposed to present a highly efficient and high precision signal modelling methodology. Finally, the algorithm emulation is introduced to confirm the feature of the proposed signal modelling methodologies in improving the accuracy of parameter estimation.
    Keywords: signal modelling; parameter estimation; multi-frequency signal; gradient search.