Disturbance Signals Research Articles

On the H2 Control of Hidden Markov Jump Linear Systems

This letter studies H2 control of linear systems with parameters driven by a hidden Markov chain, emphasizing a property of invariance of the norm to time shifts in the disturbance signal, which we call H2 time-invariance (H2TI). We show that some systems are not H2TI and that the lack of H2TI may deteriorate the performance of H2 control systems when they are subject to disturbances other than impulses at the time instant k = 0 (as in standard H2 control). This motivates us to propose a new variant of the H2-norm, based on a stochastic process having an impulse at a random time. We develop the formula for computing this new variant and apply the result to an LMI optimization problem. Numerical tests are performed, indicating the superiority of the proposed controller for systems subject to different types of disturbances.

ANALYSIS OF ACCURACY OF NONLINEAR CONTROL SYSTEMS OF MULTI-ROTOR UAVS UNDER STATIONARY RANDOM SIGNALS

A large number of articles and monographs are devoted to the development of line-ar control systems for multi-rotor UAVs, in which, based on the methods of the modern theory of automatic regulation and control, the problems of choosing the regulators that provide the required performance indices and stability margins of UAVs’ control systems are considered, taking into account structural and parametric uncertainties , nonlinearities of separate channels, various external control and disturbing signals, including determinis-tic and random signals, etc. At the same time, there are practically no works that study the issues of analyzing the dynamic accuracy of nonlinear control systems for multi-rotor UAVs under random external signals, for example, wind gusts, etc. In the article, based on the approximate method of statistic linearization, an engi-neering technique for analyzing the accuracy of nonlinear UAVs’ control systems with PID controllers in separate channels under stationary random signals with a Gaussian probabil-ity distribution and non-zero mathematical expectations are considered. It is shown that the use of the statistic linearization method allows reducing the accuracy analysis of the of independent nonlinear control channels of UAVs to a joint analysis of the accuracy of two different linear systems with respect to the mathematical expectation and the centered random component of the input signal. Analysis of the accuracy of each of these linear systems is carried out by known methods of classical control theory. A numerical example of the analysis of statistical accuracy of the control system of a quadcopter with satura-tion-type nonlinearities in the measuring devices of the system is given.

Performance Evaluation of ST-Based Methods for Simulating and Analyzing Power Quality Disturbances

Abstract The complexity of power quality disturbances (PQDs) is a significant risk factor in the electricity sector. An accurate and fast analysis of these disturbances provides crucial information to cover all the issues related to power quality. The main objective of this study is to explore a new analytic technique, including all kinds of disturbances that can appear in electrical networks, that differs from previous technologies such as the Fourier transform. Three methods based on the Stockwell transform, namely, the discrete orthonormal Stockwell transform (DOST), discrete cosine Stockwell transform (DCST), and discrete cosine transform (DCT), were used to analyze PQDs in time–frequency representation. These methods diagnose the disturbance's signal properties, which are dependent on resolution and absolute phase information. Nine PQDs, including normal sine waves, were mathematically modeled and used to evaluate the proposed methods. All the methods can effectively simulate and analyze PQDs. Among them, DOST is the most effective in providing clear and high-resolution time–frequency representations of signals. The classification of disturbances was fulfilled based on statistical features extracted from matrices derived from Stockwell transform-based methods, such as analytic approaches (mean, variation, standard deviation, entropy, skewness, and kurtosis). Neural networks, a method utilizing intelligence classifiers, were used for pattern recognition, and the patterns of the different methods were compared. Simulation results proved that DOST needs fewer samples than other methods; its capability to deal with signals in time–frequency resolution is also more viable. The neural network classifier has a higher accuracy rate than the K-nearest neighbor and decision tree methods and approximates the support vector machine method.

Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model

In this paper, a novel approach to classify the signals of power quality (PQ) disturbance is proposed based on segmented and modified S-transform (SMST), deep convolutional neural network (DCNN), and multiclass support vector machine (MSVM). The idea of frequency segmentation with different adjustable parameters was used in the Gaussian window function. The accurate time-frequency localization and efficient feature extraction of different PQ disturbances then could be achieved. Firstly, the SMST was used to analyze the PQ disturbance signals and obtained two-dimensional (2D) contour maps with high time-frequency resolution. Then, the DCNN was employed to automatically extract features from the 2D contour maps. Finally, the MSVM classifier was developed for the classification of single and complex signals of PQ disturbance. In order to demonstrate the effectiveness and robustness of the proposed model, eight single and thirteen complex waveforms of PQ disturbances were considered without noise and with different noise level, respectively. Extensive simulations were performed and compared to other existing methods. The simulation results show that the proposed method has better performance than several state-of-the-art algorithms in classifying PQ disturbances under different noise level.

АДАПТИВНОЕ БЕСПОИСКОВОЕ УПРАВЛЕНИЕ НАСОСНЫМ КОМПЛЕКСОМ В УСЛОВИЯХ НЕСТАЦИОНАРНОСТИ

At operation of the automated pump complexes except for signal (coordinate) disturbances, there are the parametric disturbances, caused by non-stationarity of parameters of the given complex, in particular hydraulic resistances of the network, pump, inlet pipeline, and also fluctuations of parameters of the pumped liquid. The traditional approach to the automatic control of the output coordinates of the pump complex is reduced to the construction of systems with coordinate feedback and, as a rule, the regulation of speed of the centrifugal pump, which is inefficient for solving the problems of fast parametric control in the conditions of the relevant perturbations. The use of automated control of the bypass channel of pumping complex allows to provide more adequate processing of parametric disturbances, such as, in particular, vibration and elements of water hammer, as well as disturbances activating various undesirable hydraulic processes, such as cavitation and surge. The article considers the issues of building an adaptive search-less self-adaptive system to solve the problem of increasing the efficiency of working off the above parametric disturbances. Research objective: Investigation of the possibility of automatic processing of parametric disturbances when controlling the pumping complex by the bypass channel by constructing an adaptive system in the class of searchless self-adaptive control systems with a reference model. Materials and methods. The methods of mathematical modelling, in particular, the method of electron analogy, methods of the theory of automatic control with use of the apparatus of transfer functions, methods of the theory of adaptive control, methods of numerical modelling with use of the program complex SimInTech have been used to conduct research. Results. The synthesis of the control algorithm of the search-free self-adaptive system with a reference model to solve the problem of parametric perturbations in the pumping complex using the bypass channel has been made. Conclusion. The results of the study substantiate the possibility of building and using the adaptive searchless control system of the pumping complex for more effective automatic processing of parametric disturbances in order to compensate and reduce their negative effects.

Online Monitoring of Lithium-Ion Battery Internal Temperature Using PWM Switching Oscillations

Online monitoring of Lithium-ion (Li-ion) battery internal temperature by electrochemical impedance spectrum (EIS) is important for the system safe and reliable operation. However, it is challenging in the high temperature region due to the limited thermal sensitivity. Moreover, additional signal injection or disturbance for online EIS measurement may interact with the operating system. To address these challenges, a Li-ion battery internal temperature continuous monitoring method using the self-excited ultrahigh frequency (UHF) PWM switching oscillations (e.g., several hundred kHz) is proposed in this article. First, the UHF EIS characteristics of a Li-ion battery are explored to find a novel thermal sensitive electric parameter applicable for high temperature measurement. Then, an PWM switching oscillation based online battery temperature monitoring scheme is presented, including the UHF oscillation sensor, resonance impedance extraction algorithm, and battery internal temperature estimation procedure. Finally, experimental work was carried out, and the results demonstrate an excellent performance, such as high sensitivity over 90 °C, ms-level fast response, and noncontract continuous monitoring.

Networked Tracking Control Based on Internal Model Principle and Adaptive Event-Triggering Mechanism

In this paper, the non-static error tracking control issue for the networked control system is considered. First, the unstable modes of the external disturbance signal and the output signal of the reference system are established to form a new series system with the controlled objects. Then, an internal model compensation controller is presented to achieve non-static tracking error. Furthermore, an adaptive event-triggering mechanism is introduced as a communication method to improve network resource utilization. By formulating the tracking control problem of the networked control system as the stabilization problem of a time-varying time-delay system, a sufficient condition such that the closed-loop system is asymptotically stable and satisfies the H∞ output tracking performance is derived. Finally, the simulation examples illustrate the feasibility and effectiveness of the proposed approach.

ROBUST CONTROL H∞ OVER AN ACTIVE DAMPING SYSTEM OF A VEHICLE ROOM

This paper aims to show and analyze the different types of suspensions: passive, semi-active, and active in a vehicle room. It presents a robust H∞ controller for an active suspension system of the quarter vehicle. Implementing this controller and estimating the weight functions are to minimize vibrations in the vertical axis. This paper compares three control systems corresponding to high comfort, balanced, and highly maneuverable systems. This comparison from the active suspension will allow us to observe that these systems reduce the deflection of the suspension and the acceleration of the sprung mass below the rattle space frequency. A time-domain simulation used a path disturbance signal representing ground irregularities to evaluate the three designs as an essential contribution to this paper. This paper shows that it is possible to develop a control strategy H∞ with robust performance to control a complex analysis system through state feedback. Keywords: Types of Suspension, Robust Controller, Damping, Frequency, Weight Functions DOI： https://doi.org/10.35741/issn.0258-2724.58.1.68

Robust dynamic control algorithm for uncertain powered wheelchairs based on sliding neural network approach

<abstract><p>The dynamic model of mobile wheelchair technology requires developing and implementing an intelligent control system to improve protection, increasing performance efficiency, and creating precise maneuvering in indoor and outdoor spaces. This work aims to design a robust tracking control algorithm based on a reference model for operating the kinematic model of powered wheelchairs under the variation of system parameters and unknown disturbance signals. The control algorithm was implemented using the pole placement method in combination with the sliding mode control (PP-SMC) approach. The design also adopted a neural network approach to eliminate system uncertainties from perturbations. The designed method utilized the sinewave signal as an essential input signal to the reference model. The stability of a closed-loop control system was achieved by adopting the Goa reaching law. The performance of the proposed tracking control system was evaluated in three scenarios under different conditions. These included assessing the tracking under normal operation conditions, considering the tracking performance by changing the dynamic system's parameters and evaluating the control system in the presence of uncertainties and external disturbances. The findings demonstrated that the proposed control method efficiently tracked the reference signal within a small error based on mean absolute error (MAE) measurements, where the range of MAE was between 0.08 and 0.12 in the presence of uncertainties or perturbations.</p></abstract>

Detection and Classification of Power Quality Disturbances Using Stock Well Transform and Improved Grey Wolf Optimization-Based Kernel Extreme Learning Machine

Power quality disturbances (PQDs) in modern electrical power systems, caused by the integration of nonlinear power electronic devices and erratic distributed generation (DG), lead to interruptions and significant energy losses for end users. However, conventional methods for PQDs classification face challenges in dealing with noise interference and feature selection. To address these challenges, this research paper proposes a novel approach that combines the stockwell transform (ST) with an improved grey wolf optimization-based kernel extreme learning machine to enhance classification accuracy. The stockwell transform is utilized to extract meaningful features from the power quality (PQ) signals, which are subsequently input into the kernel extreme learning machine (KELM). Furthermore, the parameters of the KELM model are optimized using the improved grey wolf optimization (IGWO) approach to improve the accuracy of classification. To evaluate the performance of the proposed method, real-time implementation is considered by incorporating PQDs data with signal-to-noise ratios (SNR) of 20 dB, 30 dB, and 40 dB into the original synthetic signals. Multiple noise conditions are simulated to assess the proposed model ability to identify and classify disturbance signals. The results demonstrate a detection accuracy of 99.76% under noiseless conditions, indicating the model’s high accuracy. Moreover, the proposed method exhibits robustness to noise, achieving accuracies of 98.86%, 98.32%, and 97.3% at SNR levels of 40 dB, 30 dB, and 20 dB, respectively. In the end, this work has performed a comparative study with other previously published work. Compared with other classification methods, the algorithm proposed in this paper has higher accuracy, and it is an efficient and feasible classification method.

A Hybrid Compression Method for Compound Power Quality Disturbance Signals in Active Distribution Networks

A Hybrid Compression Method for Compound Power Quality Disturbance Signals in Active Distribution Networks

Recognition of Hybrid PQ Disturbances Based on Multi-Resolution S-Transform and Decision Tree

Aiming at the problems of multiple types of power quality composite disturbances, strong feature correlation and high recognition error rate, a method of power quality composite disturbances identification based on multi-resolution S-transform and decision tree was proposed. Firstly, according to IEEE standard, the signal models of seven single power quality disturbances and 17 combined power quality disturbances are given, and the disturbance waveform samples are generated in batches. Then, in order to improve the recognition accuracy, the adjustment factor is introduced to obtain the controllable time-frequency resolution through multi-resolution S-transform time-frequency domain analysis. On this basis, five disturbance time-frequency domain features are extracted, which quantitatively reflect the characteristics of the analyzed power quality disturbance signal, which is less than the traditional method based on S-transform. Finally, three classifiers such as K-nearest neighbor, support vector machine and decision tree algorithm are used to effectively complete the identification of power quality composite disturbances. Simulation results show that the classification accuracy of decision tree algorithm is higher than that of K-nearest neighbor and support vector machine. Finally, the proposed method is compared with other commonly used recognition algorithms. Experimental results show that the proposed method is effective in terms of detection accuracy, especially for combined PQ interference.

Applications of Induced Pluripotent Stem Cell-Derived Glia in Brain Disease Research and Treatment.

Glia are integral components of neural networks and are crucial in both physiological functions and pathological processes of the brain. Many brain diseases involve glial abnormalities, including inflammatory changes, mitochondrial damage, calcium signaling disturbance, hemichannel opening, and loss of glutamate transporters. Induced pluripotent stem cell (iPSC)-derived glia provide opportunities to study the contributions of glia in human brain diseases. These cells have been used for human disease modeling as well as generating new therapies. This chapter introduces glial involvement in brain diseases, then summarizes different methods of generating iPSC-derived glia disease models of these cells. Finally, strategies for treating disease using iPSC-derived glia are discussed. The goal of this chapter is to provide an overview and shed light on the applications of iPSC-derived glia in brain disease research and treatment.

Active Disturbance Rejection Control of Quadrotor UAVs Based on Joint Observation and Feedforward Compensation

Under the influence of uncertain parameters and external disturbance, the accuracy is low and the response is slow of attitude and trajectory tracking in the flight control of the quadrotor unmanned air vehicles (UAV). In order to solve the problems, Because of the excellent adaptability and noise suppression ability of the extended Kalman filter for nonlinear system problems , it is used to suppress the high-frequency signal disturbance to reduce the estimation burden of the extended state observer, and jointly with the expanded state observer to estimate the total disturbance formed by the system uncertainty parameters and external disturbances to reduce system reliance on accurate models, and the differential values of perturbation estimated values is used by feedforward compensation to improve the tracking accuracy under abrupt disturbances and to overcome the phase lag caused by abrupt disturbances. Extended Kalman filtering, extended state observer with the feedforward compensation and PD controller with error compensation are comprehensively integrated into an improved active disturbance rejection controller which can suppress high frequency noise and abrupt disturbances to the better extent. Simulation and experiment results show that the joint observer can effectively reduce the observation error amplitude, correct the observation phase lag in advance, and obtain more accurate state information, and that the improved active disturbance rejection controller can better meet requirements of quadrotor UAV for fast response, efficient and stable control. Therefore, it can accurately and efficiently complete complex trajectory tracking operations.

Characteristics of Target Crossing the Baseline in FSR: Experiment Results

Forward scatter radar (FSR) can achieve target detection by capturing the signal disturbance caused by targets crossing the baseline, which provides a countermeasure to stealth/small targets. Firstly, the FSR net model based on the Global Navigation Satellite System (GNSS) is constructed. Then, the statistical features in the time and frequency domains are used to describe the fluctuation characteristics of the forward-scatter signal. Moreover, the influences of the relative position of the target and baseline on the statistical features are analyzed through dynamic simulations, and the change regulation of time and frequency features is revealed. Finally, a FSR experiment is carried out in an anechoic chamber and the results validate the theoretical analysis. This work provides a meaningful reference for detection and parameter estimation of targets crossing the baseline in a FSR.

A Conversion of Signal to Image Method for Two-Dimension Convolutional Neural Networks Implementation in Power Quality Disturbances Identification

The power quality is identified and monitored to prevent the worst effects arise on the electrical devices. These effects can be device failure, performance degradation, and replacement of some device parts. The deep convolutional neural networks (DCNNs) method can extract the complexity of image features. This method is adopted for the power quality disruption identification of the model. However, the power quality signal data is a time series. Therefore, this paper proposes an approach for the conversion of a power quality disturbance signal to an image. This research is conducted in several stages for constructing the approach proposed. Firstly, the size of a matrix is determined based on the sampling frequency values and cycle number of the signal. Secondly, a zero-cross algorithm is adopted to specify the number of signal sample points inserted into rows of the matrix. The matrix is then converted into a grayscale image. Furthermore, the resulting images are fed to the two-dimension (2D) CNNs model for the PQDs feature learning process. When the classification model is fit, then the model is tested for power quality data prediction. Finally, the model performance is evaluated by employing the confusion matrix method. The model testing result exhibits that the parameter values such as accuracy, recall, precision, and f1-score achieve at 99.81%, 98.95%, 98.84, and 98.87 %, respectively. In addition, the proposed method's performance is superior to the previous methods.Â

ANALYSIS OF ONLINE LEARNING MOTIVATION IN STUDENTS XI SMA NEGERI 1 JENEPONTO

The purpose of this study was to determine the motivation for online learning and to identify the factors that hinder and support the implementation of online learning in Class XI students of SMA Negeri 1 Jeneponto. The type of research used by the researcher is qualitative research. The data comes from the words or actions of the researchers' online interviews, voice recordings, and student learning documentation at SMAN 1 Jeneponto. In this case, the subjects are also Class XI students who are also part of the resource persons in this study, whose criteria are determined through purposive sampling technique. The results of the study show that for various reasons, the learning motivation of SMAN 1 Jeneponto students with online learning has decreased during the COVID-19 pandemic. In terms of inhibiting factors, one of them is that students have difficulty accessing the internet caused by network or signal disturbances when participating in online learning, while the other side is being able to reduce the spread of COVID-19.

Adaptive tracking of multisinusoidal signal for linear system with input delay and external disturbances

The problem of adaptive tracking of the output variable of a linear stationary plant behind a multiharmonic signal under conditions of control delay and external disturbances is considered. The state vector of the object is not available for direct measurements. The reference and disturbance signal parameters (amplitudes, phases, and harmonic frequencies) are a priori unknown, and a new algorithm is proposed to improve the performance of frequency estimation of a multisinusoidal signal. Examples are given that confirm the relevance of the proposed approach.

Chromatin modifiers: A new class of pollutants with potential epigenetic effects revealed by in vitro assays and transcriptomic analyses

A great variety of endocrine-disrupting chemicals (EDCs) have been used extensively and become widespread in the environment nowadays. Limited mammalian studies have shown that certain EDCs may target chromosome and epigenome of the germline, leading to adverse effects in subsequent generations, despite these progenies having never been exposed to the EDC before. However, the underlying mechanisms of chromosomal changes induced by these pollutants remain poorly known. Using the human ovarian granulosa tumor cell line COV434 as a model, we investigated and compared the transcriptomic changes induced by nine EDCs with diverse chemical structures (i.e. BDE-47, BPA, BP-3, DEHP, DHP, EE2, TCS, TDCPP and NP), to inquire if there is any common epigenetic modification associated with reproductive functions induced by these EDCs. Our results showed that COV434 cells were more responsive to BP-3, NP, DEHP and EE2, and more importantly, these four EDCs altered the expression of gene clusters related to DNA damage response, cell cycle, proliferation, and chromatin remodeling, which can potentially lead to epigenetic modifications and transgenerational inheritance. Furthermore, dysregulation of similar gene clusters was common in DEHP and NP treatments. Bioinformatics analysis further revealed that BP-3 disturbed signaling pathways associated with reproductive functions, whereas alterations in telomere-related pathways were highlighted upon EE2 exposure. Overall, this study highlighted chromatin modifications caused by a class of chemicals which that may potentially lead to epigenetic changes and transgenerational reproductive impairments.

Prescribed‐time regulation of nonlinear uncertain systems with unknown input gain and appended dynamics

AbstractThe prescribed‐time stabilization problem for a general class of uncertain nonlinear systems with unknown input gain and appended dynamics (with unmeasured state) is addressed. Unlike the asymptotic stabilization problem, the prescribed‐time stabilization objective requires convergence of the state vector to the origin in a finite time interval that can be arbitrarily picked (i.e., “prescribed”) by the control system designer irrespective of the system's initial condition. The class of systems considered is allowed to have general nonlinear uncertain terms throughout the system dynamics as well as uncertain appended dynamics (that effectively generate a time‐varying non‐vanishing disturbance signal input into the nominal system). The control design is based on a nonlinear transformation of the time scale, dynamic high‐gain scaling, adaptation dynamics with temporal forcing terms, and a composite control law that includes two components. The first component in the composite control law is analogous to prior dynamic high‐gain scaling‐based control designs, but with a time‐dependent function in place of the unknown input gain, while the second component has a non‐smooth form with time‐dependent terms that ensure prescribed‐time convergence in spite of the unknown input gain and the disturbances. The efficacy of the proposed control design is illustrated through numerical simulation studies on two example systems (a “synthetic” fifth‐order system and a “real‐world” electromechanical system).