System Identification Theory Research Articles

New Results on Differential Subordination and Superordination for Multivalent Functions Involving New Symmetric Operator

This article aims to significantly advance geometric function theory by providing a valuable contribution to analytic and multivalent functions. It focuses on differential subordination and superordination, which characterize the interactions between analytic functions. To achieve our goal, we employ a method that relies on the characteristics of differential subordination and superordination. As one of the latest advancements in this field, this technique is able to derive several results about differential subordination and superordination for multivalent functions defined by the new operator within the open unit disk . Additionally, by employing the technique, the differential sandwich outcome is achieved. Therefore, this work presents crucial exceptional instances that follow the results. The findings of this paper can be applied to a wide range of mathematical and engineering problems, including system identification, orthogonal polynomials, fluid dynamics, signal processing, antenna technology, and approximation theory. Furthermore, this work significantly advances the knowledge and understanding of the analytical functions of the unit and its interactive higher relations. The characteristics and consequences of differential subordination theory are symmetric to those of differential superordination theory. By combining them, sandwich-type theorems can be derived.

Modeling and control of electrical discharge wire sawing of single-crystal silicon

Electrical discharge wire sawing (EDWS) is a new composite processing method that combines wire electrical discharge machining with fixed abrasive wire sawing. During processing, relying on the grinding effect of the abrasive grains, craters and recast layers generated by discharge are removed. The surface roughness of the workpiece is reduced while the cutting efficiency is improved. In this study, the relationship between discharge voltage and workpiece surface roughness in composite machining is investigated. A single-input single-output control system model with applied resistance as the input and discharge voltage as the output is established. Based on the theory of system identification and parameter estimation, the order of the model is identified by calculating the variance of the model residuals and using the F-test method. The model parameters of the control system are estimated online using a recursive least squares algorithm with a forgetting factor. A generalized predictive controller is designed on the basis of the predictive control theory and the identified discharge voltage control system model. Using LabVIEW software, a discharge voltage prediction control system is constructed for the processing of single-crystal silicon by EDWS, and its prediction control performance is experimentally verified. The results show that the discharge voltage control system designed in this study can successfully control the composite machining of single-crystal silicon in real time. Under the same machining conditions, the surface roughness of the workpiece machined with predictive control was 23.5 % lower than that without predictive control.

Optimal Injection Attack Strategy for Nonlinear Cyber-Physical Systems Based on Iterative Learning

This paper aims to investigate the security problem of nonlinear cyber-physical systems (CPSs), which poses a challenge to handle compared with linear CPSs. A series of optimization problems for nonlinear CPSs under injection attack are constructed, which are based on a general model of the nonlinear systems with repetitive operation characteristics and a novel introduction of the key technical lemma. These optimization results are more general than the existing injection attack results and the requirements for attackers to obtain system information are relaxed. Also, the form of switching applied to the attack strategy possesses several advantages, including high stealthiness, lower cost, and more flexibility. Therefore, the new optimal injection attack strategies are expected to be more widespread and provide a basis for the design of defense strategies. The key to acquiring the designed optimal attack strategies is to adopt the linear input/output (I/O) data model for these systems via introducing an estimation term of the improved projection estimation method into the linear model. Finally, a networked GLUON-6L3 manipulator example validates the effectiveness of the proposed methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The main purpose of this paper is to study the cyber security of nonlinear cyber-physical systems from the perspective of attackers, which can help defenders fully understand the behavior of attackers. Most of the existing attack strategies aimed at linear systems or have a priori knowledge of the attacked systems. However, there are great limitations and difficulties in practical application. In this paper, the new attack strategies are proposed by combining system identification, iterative learning and control theory, which relaxes the requirement of the attacker’s ability. In detail, the attacker only needs to obtain the control input and output data of the attacked system and design the attack strategy according to it. Among them, the first-in, first-out queue storage method is applied to remove the requirements for the storage capability of the attacker. In practical applications, the attacker does not need to store the I/O data continuously, but only store the initial value and the data of two successive iterations. The mathematical analytic forms of two optimal attack strategies are given, and then their effectiveness are verified on a welding work of a networked six-axis manipulator. In the future, we will investigate the design of attack strategies for nonlinear systems with heterogeneous dynamics and multiple delays.

A Sample Based Algorithm for Constructing Guaranteed Confidence Ellipsoids for Linear Regression Models with Deterministic Regressor

This paper presents a novel algorithm for constructing non-asymptotic confidence ellipsoids for linear regression models with deterministic regressors. The confidence ellipsoids are centered at the least-squares estimate, and the true parameter lies within the ellipsoid with a probability specified by the user. The confidence ellipsoid is constructed by drawing independent samples of the noise sequence and exploiting an ordering property for independent and identically distributed random variables. The main assumption on the noise is that it has a known joint density so that samples can be drawn. The efficacy of the approach is demonstrated through a simulation example of an FIR system. The constructed confidence ellipsoid is compared with the confidence regions obtained using the Sign-Perturbed Sum method and asymptotic system identification theory.

Detection of Unauthorized Actions in Networks Using Wavelet Analysis

Signal processing techniques are used to analyze and detect network anomalies because of their ability to detect new and unknown intrusions. The paper proposes a method of modeling network signals for the detection of network anomalies, which combines wavelet approximation and the theory of system identification. To characterize the behavior of network traffic, fifteen functions are provided, which are used as input signals within the system. At the same time, it is assumed that security violations within the network can be detected by checking abnormal patterns of system functioning according to audit data.&#x0D; Despite the fact that machine learning methods have achieved significant results in detecting network anomalies, they still face the difficulty of using the implemented algorithms, in the presence of differences in the behavior of the training data and test data, which in turn leads to inefficient performance of the algorithms. This effect is exacerbated by the limitation of algorithms to detect previously unknown types of attacks due to the large number of false positives.&#x0D; The paper develops a new method of modeling network signals for detecting anomalies in networks using wavelet analysis. In particular, the general architecture of the approach consists of three components: feature analysis, modeling of normal network traffic based on wavelet approximation and prediction using ARX model, intrusion or non-intrusion decision making&#x0D; The result is evaluated using the DARPA intrusion detection dataset, which performs a comprehensive analysis of the intrusions in the dataset. Evaluation results show that this approach provides a high level of detection of both instances and types of attacks.

Fractional-Order Composite Adaptive Control of Robot Manipulators

The branch of system identification and control theory concerned about robot manipulators is adaptive control of non-linear multi-input multi-output dynamic systems which study constitutes a mature and well-founded discipline; nevertheless, there are fundamental problems open to date, one of them is to obtain a control law that is rich enough to guarantee its persistent excitation and consequently that the parametric error converges asymptotically to zero, while the tracking error converges globally asymptotically to zero. An extension of a globally convergent adaptive scheme control for a robot manipulator in the tracking of a determined trajectories with no consideration of the interaction with its environment is proposed, based on the fact that the parameter uncertainty is involved in both the tracking error and the identification error. The first control task is achieved by a feedback linearization technique that takes advantage of the structure of manipulator dynamics. The second task is achieved by a fractional order filtering technique to avoid the joint acceleration and enrich the regressor matrix in such way that is persistently exciting. Finally, the control law is expressed as feedforward compensation and a simple PD controller.

Ідентифікація параметрів математичних моделей та визначення діагностичних ознак

In this article, the object of research is two main units of the AR20N aggregate – electrohydraulic amplifier (EHA) and power cylinder (PC). Detection of the occurrence and development of faults in them will be considered on the basis of the identification of parameters of the developed mathematical models. The main objectives of the research were to identify the parameters of mathematical models of EHA and PC using different steps of time quantization, determination of the optimal step of time quantization for numerical integration of differential equations; determination of diagnostic features that have the most probable influence on the operation of aggregate units at the occurrence of various faults. For achievement of the set objectives the following tasks were solved: mathematical models of EHA, PC, and simulation model of the aggregate research on the test bench were developed; identification of parameters of mathematical models of EHA and PC by the method of least squares (MLS) was carried out; the permissible step of time quantization was determined; upper and lower permissible limits of the identifiable parameters for a serviceable aggregate were determined; a methodology for determining the diagnostic features that are most probable to determine the emerging faults of the aggregate has been developed. Methods are used for this purpose: analytical, numerical, statistical, hydraulic systems theory, and system identification theory. The following results were obtained: differential equations describing the displacements of the distributive spool (DS) of the EHA and the output link (OL) of the PC; algorithms for identifying the parameters of mathematical models by MLS; the permissible step of time quantization was determined; and a technique for determining the diagnostic features using the Shewhart diagnostic chart for the parameters of mathematical models was developed. The scientific and practical novelty of the obtained results consists of the following: mathematical models of EHA and PC of the aggregate have been developed; simulation model of the aggregate on the test bench has been developed; simulation of various faults of the aggregate units has been carried out and identification of parameters of mathematical models of these units has been carried out; changes of estimations of parameters of mathematical models of EHA and PC depending on changes of each influencing factor have been obtained; upper and lower permissible limits of parameters and average values of these parameters have been determined; diagnostic features that are most probable to influence aggregate performance have been determined. It is shown that time quantization step D t = 0.01 (c) provides the determination of DS and OL displacements with errors of 1.914×10-6mm and 2.157×10-5 mm.

Self-Calibration Method of Noncontact AC Voltage Measurement

Realizing stable and reliable monitoring of a distribution network voltage environment can obtain real-time power parameter information and ensure the normal and safe operation of transmission lines, which is of great research significance and engineering value. Based on the distributed capacitance relationship between sensor and transmission line, an equivalent circuit capacitance voltage dividing model is proposed, and the relevant factors affecting the stability of the voltage dividing ratio are analyzed. The self-calibration principle of noncontact AC voltage measurement is proposed based on the system identification theory. The noncontact sensing structure is designed, a sensor probe prototype is fabricated, and a back-end conditioning circuit is designed to realize the overall measurement system. Finally, the validity of the measurement model is verified by simulation and experiment, and a measurement platform is built which proves the feasibility of the self-calibration method for noncontact voltage measurement. The experimental results show that the error is less than ±2%. This method can correctly restore the measured voltage waveform, has good linearity, and can realize wireless data transmission, which provides a new idea for the voltage measurement method of a distribution network.

The Method for Analyzing the Pitching Motion Pattern of a New Surface Unmanned Wing-sliding Boat

In this paper, a new surface unmanned wing skate (NUSV) is taken as the research object, and the free decay model test under different drafts and different initial pitch angles is completed. In the case of specially selected regular waves, typical swaying tests against waves and waves were carried out. Four mathematical models for system identification of pitch motion modes are established. Based on the system identification theory and the programming idea of the improved genetic algorithm, the NUSV pitch motion mode system identification software is written in C# language. The system identification software is developed based on experimental technology and an analysis method of ship motion patterns is developed based on the optimization theory. This paper completed the following: (1) compared and calculated the mathematical models of system identification based on four-pitch motion modes and determining their motion modes, (2) based on the results of pitch motion mode identification, the predicted values and the test results are in good agreement, which verifies the rationality of the method and the reliability of the self-compiled software, (3) analyzed the variation law of the relative value of each moment that affects the NUSV pitch decay motion with the draft and initial pitch angle, (4) analyzed the influence of the NUSV in the regular wave. The variation law of each moment of the rocking motion with the draught, wave height, and encounter period, and (5) proposed the idea of establishing a rocking motion pattern map based on the rocking test and optimal identification theory. The method and research results proposed in this paper can provide a reliable motion model for the pitch motion control of this type of boat, and provide technical support for its effective regulation of the adverse effect of the pitch on the boat’s stability. It also provides a reference for the seakeeping of this type of boat and the optimization design of the subsequent boat type. The system identification software written can also be used for system identification of the rocking motion mode of this type of unmanned boat in other directions.

Computational modelling and application of mechanical waves to detect arterial network anomalies: Diagnosis of common carotid stenosis

Background and Objective: This paper proposes a novel strategy to localize anomalies in the arterial network based on its response to controlled transient waves. The idea is borrowed from system identification theories in which wave reflections can render significant information about a target system. Cardiovascular system studies often focus on the waves originating from the heart pulsations, which are of low bandwidth and, hence, can hardly carry information about the arteries with the desired resolution. Methods: Our strategy uses a relatively higher bandwidth transient signal to characterize healthy and unhealthy arterial networks through a frequency response function (FRF). We tested our novel approach on data simulated using a one-dimensional cardiovascular model that produced pulse waves in the larger arteries of the arterial network. Specifically, we excited the blood flow from the brachial artery with a relatively high bandwidth flow disturbance and collected the subsequent pressure waveform at peripheral positions. To better differentiate FRFs of healthy and unhealthy networks, we used a FRF that removes the effects of heart pulsations. Results: Results demonstrate the ability of the proposed FRF to detect and follow-up on the development of a common carotid artery (CCA) stenosis. We tested distinct geometrical variations of the stenosis (size, length and position) and observed differences between the FRFs of healthy and unhealthy networks in all cases; such differences were mainly due to geometrical variations determined by the stenosis. Conclusions: We have provided a theoretical proof of concept that demonstrates the ability of our novel strategy to detect and track the development of CCA stenosis by using peripheral pressure waves that can be measured non-invasively in clinical practice.

Research and Application of Data-Driven Modeling Method for Gear System Based on Time-Varying Characteristics

The time-varying characteristics of the gear system have an essential influence on its vibration and stability characteristics. Aiming at this characteristic and taking the generalized force caused by load torque as input and dynamic transmission error as output, a data-driven modeling method for gear time-varying system is studied using the periodic time-varying system identification theory. The effectiveness of the proposed method is verified by the lumped mass model of the gear system. This method has high modeling accuracy and can accurately characterize the time-varying characteristics of the gear system. Then, the virtual experimental platform of gear system dynamics is established based on the finite element theory. The method successfully extends to the virtual experimental platform, laying a foundation for analyzing the gear system’s dynamic characteristics.

Analysis of fault coupling vibration and transmission characteristics of multistage gear transmission system

In multi-stage gear transmission system, the accident is often not caused by a single fault, but by coupling fault. The decoupling and identification of coupling faults has always been a hot topic. Through the analysis of frequency characteristics and transmission characteristics, this paper explored the correlation of coupling faults in multistage gear transmission system, and analyzed the coupling mechanism of faults. According to the fault test-bed, a 3D model was built to analyze the contact force and vibration characteristics of gears at all stages. STFT and waterfall diagram were used to analyze the coupling correlation characteristics of experimental signals under two single faults and coupling faults: fixed-axis gear crack and planetary gear wear. The contact force transfer function of all stages of gears to the box was built by using the system identification theory. The source of the fault characteristic frequency component was found through the transfer function, and the transmission characteristic of the fault was revealed. It can be seen from the results that the fault coupling is not the superposition of frequency characteristics. In the coupled signal, the frequency characteristic with the largest amplitude increase is not the fault gear itself, but the intermediate gear connected with them and affected by them. This phenomenon is often misled and misjudged in the analysis.

Compensation of Thermally Induced Errors in Five-Axis Computer Numerical Control Machining Centers Equipped With Different Spindles

Abstract Thermally induced errors are the dominant source of inaccuracy in machine tools today and are often the most difficult type of error to reduce. Software compensation of thermally induced displacements at the tool center point (TCP) is a widely employed error reduction technique due to its cost-effectiveness and ease of implementation. Transfer function (TF)-based compensation methods lead to promising results, as has been shown in previous studies. Furthermore, machine tool manufacturers frequently offer the same type of machine tool equipped with different spindle units. This leads to different thermal deformation behavior depending on the specific spindle unit mounted in the machine tool. To demonstrate this difference, experimental research is carried out on three five-axis computer numerical control (CNC) machining centers of the same type equipped with three different spindle units. The experimentally obtained thermal errors at the TCP of three machine tools with different mounted spindle units are compared, showing a significant variation in thermal errors in the Z-direction depending on the spindle unit. The research presented in this paper shows a dynamic approach to thermal error compensation of five-axis CNC machining centers considering different spindle units. System identification theory is applied to build dynamic thermal error models for three different spindle units based on calibration experiments. Subsequently, the evaluation of model performance through spindle spectrum tests shows that a reduction in thermal error of up to 85% was achieved in the Z-direction after compensation.

Simulation and Experiment of Substation Grounding Resistance Measurement Based on Pseudorandom Signal.

The grounding resistance of a substation is an important parameter that should be designed within a reasonable range to prevent operational accidents from damaging electrical equipment due to overvoltage and ensure the safe operation of an electrical system. However, simply and accurately measuring the grounding resistance of a substation has been a difficult problem faced by engineers and technicians for a long time. This paper proposes a method of denoising by applying the m-sequence correlation identification technology to the measurement of substation resistance. We established a grounding resistance model of a grounding grid and used LabVIEW to simulate it. Based on system identification and correlation function theory, pseudorandom signals or sinusoidal signals were used as excitation signals. The output results of the system were compared when pseudorandom signals and sinusoidal signals were used as excitation signals. It was verified that the grounding resistance value measured by a pseudorandom signal was closer to the actual value, which met the design requirements. Laboratory test results verify that the method of calculating grounding resistance based on the correlation analysis method is feasible.

Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model

The performance of analog-to-digital converters (ADCs) has reached a bottleneck due to the limitations of the manufacturing process and testing environment. Time-interleaved ADC (TIADC) technology can increase the sampling rate without changing the resolution. However, channel mismatch severely degrades the dynamic performance of the TIADC system. For the channel mismatch problem of TIADC, most of the current solutions have preconditions, such as eliminating only some kind of error or increasing the complexity of the hardware. A few methods can estimate multiple errors without changing the hardware circuit. To improve the dynamic performance of the TIADC system, on the basis of an in-depth study of the channel mismatch error of TIADC, according to the system identification theory, an identification model is designed to characterize the frequency characteristics of TIADC. Using the system observation data, the transfer function parameters of the system are recursively estimated. By constructing and verifying the identification model of the TIADC system, and then through the frequency domain correction method, a digital compensation filter is established to complete the error correction of the system. The test results of the four-channel TIADC high-speed data acquisition system show that the actual input and output characteristics of the test system are consistent with the nature of the identification model. The four channels of the TIADC system are provided by four sub-channels of two AD9653 chips, and the highest sampling rate of a single channel is 125MSPS. For sinusoidal input signals from 20 MHz to 150 MHz, the sampling system can achieve a signal-to-noise ratio (SNR) above 56.8 dB and spurious free dynamic range (SFDR) above 69.7 dB. The dynamic performance of the sampling system is nearly equivalent to that of its sub-ADC; the feasibility of the model identification method and the effectiveness of error correction are verified in simulation and experiment.

Synthesis cascade estimation for aircraft system identification

PurposeThe purpose of this paper extends the authors’ previous contributions on aircraft system identification, such as open loop identification or closed loop identification, to cascade system identification. Because the cascade system is one special network system, existing in lots of practical engineers, more unknown systems are needed to identify simultaneously within the statistical environment with the probabilistic noises. Consider this problem of cascade system identification, prediction error method is proposed to identify three unknown systems, which are parameterized by three unknown parameter vectors. Then the cascade system identification is transferred as one parameter identification problem, being solved by the online subgradient descent algorithm. Furthermore, the nonparametric estimation is proposed to consider the general case without any parameterized process. To make up the identification mission, model validation process is given to show the asymptotic interval of the identified parameter. Finally, simulation example confirms the proposed theoretical results.Design/methodology/approachFirstly, aircraft system identification is reviewed through the understanding about system identification and advances in control theory, then cascade system identification is introduced to be one special network system. Secondly, for the problem of cascade system identification, prediction error method and online subgradient decent algorithm are combined together to identify the cascade system with the parameterized systems. Thirdly from the point of more general completeness, another way is proposed to identify the nonparametric estimation, then model validation process is added to complete the whole identification mission.FindingsThis cascade system corresponds to one network system, existing in lots of practice, such as aircraft, ship and robot, so it is necessary to identify this cascade system, paving a way for latter network system identification. Parametric and nonparametric estimations are all studied within the statistical environment. Then research on bounded noise is an ongoing work.Originality/valueTo the best of the authors’ knowledge, research on aircraft system identification only concern on open loop and closed loop system identification, no any identification results about network system identification. This paper considers cascade system identification, being one special case on network system identification, so this paper paves a basic way for latter more advanced system identification and control theory.

Vibration-Based Damage Detection for a Prestressed Concrete Box Girder by Means of Subspace Analysis

Vibration-based damage detection for civil structures has been actively investigated. Many acceleration-based damage detection methods have been developed, among which the subspace-based system identification theory has been well established and has been a popular way to identify the structure’s modal parameters, having the advantage of being non-iterative and computationally tractable. Nevertheless, traditional modal parameters, including modal frequencies, mode shapes, and damping ratios, are not always sensitive to damage in actual civil structures. In this paper, a series of static loading tests and nondestructive hammer tests were applied to a prestressed concrete (PC) box girder. After analyzing the structure by the traditional modal analysis method, a technique based on null subspace analysis is proposed to monitor the health state of the PC box girder, and an index reflecting the damage levels is proposed. Compared with traditional modal parameters, the proposed damage index shows higher sensitivity and efficient precision to distinguish different structural damage levels. What is more, the damage caused by fracturing of prestressing tendons, which only slightly affect changes in modal frequencies, can also be obviously observed by this method.

Locomotion Identification Method of one-DOF Six-Bar for Jumping Robot

Abstract Exploring the locomotion of creatures is a challenging task in bionic robots, and the existing iterative design methods are mainly based on one or two characteristics to optimize robots. However, it is hard to obtain other features. Here, we introduced the thinking of system identification theory to the bionic robots, averting the exploration of the dynamics and reducing the difficulty of design greatly. A one-DOF six-bar mechanism (Watt I) was designated as the model to be identified, and it was divided into two parts, i.e. a one-DOF four-bar linkage and a three-DOF series arm. Then we formed constraints and a loss function. The parameters of the model were identified based on the kinematic data of a marmoset jumping. As a result, we obtained the desired model. Then, a prototype derived from the model was fabricated, and the experiments verified the effectiveness of the method. Our method also can be applied to other motion simulation scenarios.

A rapid system identification method for measuring explosion heat by the constant temperature method

In order to reduce the risk of measurement failure caused by system failure in the classical constant temperature method for measuring explosion heat, a measurement method was developed for identifying the explosion heat value based on the water temperature rise curve of an inner barrel before failure. Firstly, the heat transfer mechanism of the measurement process was analyzed, and the heat transfer model of the calorimeter was established, and the water temperature rise curve of the inner barrel in each measurement stage was obtained. Then, based on the system identification theory, the identification algorithm of intermediate parameters was proposed, and based on the idea of isolating easily oscillating parameters, a fast system identification algorithm was given to correct the temperature rise and explosion heat. And it is proved that the identification value of explosion heat approximately converges to the classical value. Finally, the test data of 8 explosive samples with explosion heat values ranging from 4 to 9 kJ/g were used to test the algorithm, and the test criterion for judging the convergence time was put forward. The simulation results show that the identification algorithm can effectively isolate the influence of the oscillation parameters and has a strong prediction ability on the temperature change of the water in the inner barrel, and that the identification value of explosion heat can quickly and stably converge to the upper limit of relative error of 3.5% within 40 min (1/3 of the main end stage), and that the test criterion can accurately judge the convergence time of the identified value of explosion heat. This method can also be extended to the adiabatic method for calculating explosion heat.

Dynamic causal brain circuits during working memory and their functional controllability

Control processes associated with working memory play a central role in human cognition, but their underlying dynamic brain circuit mechanisms are poorly understood. Here we use system identification, network science, stability analysis, and control theory to probe functional circuit dynamics during working memory task performance. Our results show that dynamic signaling between distributed brain areas encompassing the salience (SN), fronto-parietal (FPN), and default mode networks can distinguish between working memory load and predict performance. Network analysis of directed causal influences suggests the anterior insula node of the SN and dorsolateral prefrontal cortex node of the FPN are causal outflow and inflow hubs, respectively. Network controllability decreases with working memory load and SN nodes show the highest functional controllability. Our findings reveal dissociable roles of the SN and FPN in systems control and provide novel insights into dynamic circuit mechanisms by which cognitive control circuits operate asymmetrically during cognition.