System Identification Method Research Articles

A real-time identification method of network structure in complex network systems

This paper develops a real-time identification algorithm for structures of network systems. We have already proposed a structure identification method for nonlinear network systems with nonlinear couplings by combining Koopman operator theory and a sparse identification method. However, the proposed method is only applicable for offline use under the assumption that the state of an isolated system is available. To relax this assumption, this paper shows that the dynamical model of isolated systems can be extracted from measured data of interconnected systems under some weak restrictions on network structures. We also modify the identification algorithm to obtain real-time identification results with fewer calculation resources and analyse the convergence of the proposed methods. Numerical examples demonstrate that the proposed method can identify network structures in real-time and detect changes in networks correctly.

System Identification of an Actuated Inclined Ball Mechanism Via Causation Entropy

Abstract Grey-box and black-box dynamic models are routinely used to model the behavior of real-world dynamic systems. When creating such models, the identification of an accurate model structure (often referred to as covariate selection, feature selection, or sparsity identification) is a critical step required to achieve suitable predictive performance by minimizing the effects of overfitting. Recently, causation entropy has been shown to be quite useful in data-driven covariate selection as it provides a mechanism to measure the causal relationships between the set of covariates and the state dynamics. This work extends previous results by applying the causation entropy covariate selection technique to data from an experimental nonlinear system consisting of a ball rolling on an actuated inclined ramp. Data collected from the system is processed by the causation entropy-based algorithm and covariate selection is performed on a black-box dynamic model. The resulting optimized model is shown to provide better predictive performance than an optimized black-box model which includes extraneous covariates. This study represents the first application of causation entropy-based covariate selection to real-world experimental data, illustrating its use as a practical system identification method.

Dynamic Study and PI Control of Milk Cooling Process

The background of this research is to understand the operation process, which is the main goal of developing the process model. This model is often used for operator training, process design, safety system analysis, or control system design. The dynamic model of the milk cooling process from 36ËšC to 4ËšC using chilled water available at 2ËšC was performed. Chilled water was maintained at a constant temperature by using a refrigerant unit. The process being investigated was a Packo brand milk cooling tank belonging to KUD SAE Pujon (Malang - Indonesia). A fundamental heat balance method was used to derive the model, leading to a first-order transfer function process. For a 2-hr cooling process, the gain and time constant values are 1.00 and 42.3548 mins, respectively. Heat balance was then extended to continuous processes so that its transfer function could also be obtained. This study simulated and investigated the behavior of batch and continuous processes. Process Identification via input-output data was also introduced for continuous process. The process model obtained from the system identification toolbox was very useful in control, such as for determining tuning parameters via the Ziegler-Nichol method for Proportional-Integral control. However, a small delay was required to be introduced to the system as the first order process without time Ziegler Nichol method cannot be implemented. Further research may include other system identification methods, such as ARX, ARMAX, Output-Error, Box Jenkins etc., or implementing advanced process control for milk cooling.

System Identification of a Subscale Distributed Electric Propulsion Aircraft

Flight testing of a 21%-scale Cirrus SR22T aircraft with a distributed electric propulsion system was performed with the goal of identifying the influence of distributed propulsion units on the aircraft flight dynamics. Stability and control derivatives for this configuration were identified for a linear aircraft model, including throttle-based control influences of a semispan-distributed array of propulsors. Unknown model parameters were identified from flight test data under unsteady excitation of control effectors using frequency-domain system-identification methods, and the resultant model was validated against independently acquired sets of flight data. The identified throttle-based control derivatives were characteristic of anticipated local thrust contributions from each propulsor and associated proportional yawing moments. Additional throttle-based control influences were also observed, through modulation in the aircraft lift, pitch, and roll characteristics. Furthermore, significant spanwise nonuniformity was observed in the aircraft lift sensitivity to throttle changes, which was linked to three-dimensional influences of the wing aerodynamics. These observations demonstrate the inherent degree of interconnectivity between aerodynamics, propulsion, and control subsystems characteristic of aircraft with distributed electric propulsion systems.

Study on Modeling Method of a Multi-Parameter Control System for Threshing and Cleaning Devices in the Grain Combine Harvester

In order to realize the demand for high-quality and high-efficiency harvest in modern agriculture, the grain combine harvesters must have the ability to intelligently adjust the operation parameters. The difficult problem is to establish the multi-parameter control system model for threshing and cleaning devices. The threshing and cleaning devices are located in the same rack space, and the interaction mechanism among agricultural material movement, mechanical structure, and airflow field is very complex. In view of the difficulties in the theoretical modeling of threshing and cleaning devices, a large number of operating parameters and performance indicators, strong coupling, and high requirements for real-time control, the system identification method was used to model the threshing and cleaning system in this paper. Firstly, the amplitude modulated PRBS input signals were designed as the input parameters of the system identification test, and the output signals acquisition test was carried out in the field. Then, the multi-input and multi-output signals of the system were used as training data, and the fusion method of the PSO (particle swarm optimization) algorithm and WNN (wavelet neural network) was proposed to identify it, and the optimal state-space model was obtained. Finally, the model identification and verification experiments were carried out on the threshing and cleaning system of various crops during the actual harvest. The VAF (variance-accounted-for) values of system identification model verification results were greater than or equal to 81.7%, and the RMSE (root mean square error) values were less than or equal to 0.602. The modeling method has high accuracy and adaptability, which laid a good foundation for realizing multi-parameter coordinated control of threshing and cleaning devices.

System response modeling of HMCVT for tractors and the comparative research on system identification methods

The HMCVT (Hydro-mechanical Continuously Variable Transmission) is widely applied in agricultural and engineering machinery such as tractors. Getting a correct system response model is the key to HMCVT control strategy implementation and controller design. The study decomposes and analyzes the HMCVT system and builds response models. The study proposes to decompose the HMCVT system into three dynamic response stages including the electronic proportional pressure relief valve response, the variable plunger displacement response and the pump-motor system speed ratio response, and builds the response models of three stages respectively and then obtains the general form of transfer function of HMCVT system using a tandem method. The study proposes a system identification method based on the prior-model-heuristic-intelligent-optimization algorithm, and makes a comparative analysis between the classical system identification method (the area method) and the three heuristic intelligent optimization algorithms (the FI-SA, the I-SA and the I-AFSA) in terms of system identification result. The study compares the results of identification methods through a step response test of a model of HMCVT for tractors, and verifies the correctness of the transfer function model of HMCVT system proposed. Research results show that the general form of HMCVT system response model should be a 4-order denominator 1-order numerator transfer function model (the mean identification precision of 15 groups of measurement tests is about 0.9849). The area method is affected by the sampling frequency in the response measurement and requires a complete response process measured. The system identification based on heuristic intelligent optimization algorithm has low dependence on sampling frequency and time consumed and high precision. According to the results of the calculation example in the study, the coefficient of determination about the identified model is 1 under the measurement frequency of 4 Hz and the measurement duration of 4 s by using the FI-SA, the I-SA, and the I-AFSA. The precision is improved by 4.93 % compared with using the area method. According to the actual test results, the coefficients of determination about the FI-SA and the area method are 0.9939 and 0.9834, respectively. Comparing the three heuristic intelligent optimization algorithms, the FI-SA proposed has the fastest identification speed. The results of 20 groups measurement tests show that the average iteration number of FI-SA are 43.58 % and 17.95 % lower than those of I-SA and I-AFSA, and the average time consumption of FI-SA is 43.36 % and 84.76 % lower than that of I-SA and I-AFSA.

Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications.

In this study, we provide a systems biology method to investigate the carcinogenic mechanism of oral squamous cell carcinoma (OSCC) in order to identify some important biomarkers as drug targets. Further, a systematic drug discovery method with a deep neural network (DNN)-based drug–target interaction (DTI) model and drug design specifications is proposed to design a potential multiple-molecule drug for the medical treatment of OSCC before clinical trials. First, we use big database mining to construct the candidate genome-wide genetic and epigenetic network (GWGEN) including a protein–protein interaction network (PPIN) and a gene regulatory network (GRN) for OSCC and non-OSCC. In the next step, real GWGENs are identified for OSCC and non-OSCC by system identification and system order detection methods based on the OSCC and non-OSCC microarray data, respectively. Then, the principal network projection (PNP) method was used to extract core GWGENs of OSCC and non-OSCC from real GWGENs of OSCC and non-OSCC, respectively. Afterward, core signaling pathways were constructed through the annotation of KEGG pathways, and then the carcinogenic mechanism of OSCC was investigated by comparing the core signal pathways and their downstream abnormal cellular functions of OSCC and non-OSCC. Consequently, HES1, TCF, NF-κB and SP1 are identified as significant biomarkers of OSCC. In order to discover multiple molecular drugs for these significant biomarkers (drug targets) of the carcinogenic mechanism of OSCC, we trained a DNN-based drug–target interaction (DTI) model by DTI databases to predict candidate drugs for these significant biomarkers. Finally, drug design specifications such as adequate drug regulation ability, low toxicity and high sensitivity are employed to filter out the appropriate molecular drugs metformin, gefitinib and gallic-acid to combine as a potential multiple-molecule drug for the therapeutic treatment of OSCC.

Research on system identification method of buffer system based on experimental signal pre-processing

In order to obtain the theoretical model of a gun buffer system, the dynamic vibration characteristics of the buffer system were studied by force hammer experiments, and the vibration acceleration response curves of the buffer system after being excited by the impact load were obtained. The acquired signals were processed by noise reduction, detrending, data smoothing and frequency domain integration, and the second-order free vibration model was used to identify the stiffness, damping and other parameters of the system, and compared with the experimental results. The deviation between the calculated results of the identification model and the experimental results is less than 4%, which compared with the general direct identification method, the error is reduced by about 10%. This method can provide a simple and fast technical way for the accurate identification of vibration system models.

Control theory applications when describing centrifugal concentration processes for mineral raw materials

The article discusses an approach aimed at developing an application methodology for the principles and models used in the theory of process control automation. The problems solved in the article are as follows: to try and represent the gravity concentration process (centrifugal concentration) in the form of a transfer function and to establish its parameters. In this case, the limited amount of data obtained in the course of the experiments is the main limitation for building models in the form of differential equations. An original processing and application methodology is proposed for the averaged data used when designing the model. This approach may be applicable to other ore preparation and processing stages as it involves equipment operation using the black box principle. The study is based on replacement of instantaneous characteristics of the input stream with their averaged values and subsequent integration, allowing the creation of a numerical model that coincides with the actual data. Transfer functions and their parameters for tailings and concentrate were established separately. MatLAB software with the Simulink package was used to verify the calculations. As a result, the transfer function was found for a KC-CVD6 device used at a gold deposit and satisfactory simulation results were obtained. The work uses automatic systems identification, system analysis, and mathematical modeling methods, namely, numerical solutions of differential equations using the MatLAB desktop environment with the Simulink package.

Active control of combustion oscillation with active disturbance rejection control (ADRC) method

This research aims to develop a novel method for the active control of combustion oscillation based on active disturbance rejection control (ADRC). The effectiveness of parameter tuning and the controller in suppressing combustion oscillation was studied by simulation and experiments. The control performance was verified using a Rijke tube and turbulent premixed swirling flame within a model combustor. For the former, the results show that the first-order ADRC can rapidly mitigate the limit cycle oscillation and maintain the effectiveness of the control when the oscillation frequency changes. For the latter, a system identification method based on an acoustic network was adopted. Combining the identification results with a thermoacoustic network, the formation process of the limit cycle oscillation and the effectiveness of the first-order ADRC were simulated by SIMULINK. The experimental results show that the first-order ADRC performs better than the proportional–integral–derivative (PID) controller. At the same time, the first-order ADRC and second-order ADRC achieve good outcomes when the equivalence ratio changes. However, the second-order ADRC exhibits a better pressure-suppression effect and smaller controller output voltage.

System Parameter Identification of a Colored-Noise-Driven Rijke Tube Simulator

Abstract In this paper, we report an experimental investigation on the influence of colored noise (generated by the Ornstein–Uhlenbeck (OU) process) on thermoacoustic coupling in an electro-acoustic Rijke tube simulator. In the absence of noise, the simulator exhibits subcritical Hopf bifurcation. Although noise in a practical system has a finite correlation time, yet the system identification methods are based on the assumption of white noise. In this study, we investigate the effects of correlation time, and intensity of colored noise on the estimation of the growth rates of acoustic oscillations determined using Fokker–Planck equation in stable, bistable, and linearly unstable regions. Subsequently, we compare the findings against results obtained considering white noise approximations. We report the observed deviation of the estimated growth rates from the actual values as a function of noise intensity and correlation time. We find that with the colored noise model, the deviation in the estimated growth rates lies within the range of 0–10% compared to the deviation of 5–25% observed considering the white noise approximation. We also report that increasing noise amplitudes leads up to a deviation of approximately 30% in the estimated growth rates from the actual values.

Bridge modal property identification based on asynchronous mobile sensing data

There is a growing attention in real-time bridge condition assessment using data from drive-by vehicles as a potentially scalable approach. Most system identification methods are based on synchronized vibration data collection for this purpose. This study presents an approach for bridge modal identification that estimates high-resolution absolute value of the operational mode shapes using asynchronous mobile data. With each trip of a vehicular sensor, the spatio-temporal response of the bridge is sampled, along with various sources of noise, e.g. vehicle dynamics, environmental effects, road profile, etc. The crowdsourced modal identification using continuous wavelet (CMICW) method is proposed that gradually magnifies the bridge dynamical signatures and mitigates noise over the spatio-temporal map. The performance of the CMICW method is validated in an experimental setting. The method successfully identifies natural frequencies and absolute value of the operational mode shapes of a bridge with high resolution and accuracy. Notably, by including data collected from various bridge lanes, the method can reconstruct 3D representation of the mode shapes. The influence of the speed of the mobile sensors on the accuracy of the estimated modal properties is investigated as well. Using a hybrid simulation framework, the effect of vehicle dynamic is included in the mobile sensing data. The study shows that the CMICW method is successful in discounting the effect of vehicle dynamics, thereby strengthening the bridge modal information. Finally, a blind source separation technique is implemented to separate the effects of road irregularities, which further improves the accuracy of modal property estimates. This study contributes to the growing body of knowledge on mobile crowdsensing for physical properties of transportation infrastructure.

Validation of a dynamic non-linear grinding circuit model for process control

A step-wise algebraic routine is used to fit a dynamic non-linear model, specifically developed for process control, to steady-state process data of an industrial single-stage grinding mill circuit. Step-test data from the industrial plant is used to validate the response of the non-linear model. The results indicate that the model provides a qualitatively accurate response of the main process variables. Because the non-linear model parameters can be calculated from steady-state data, it provides an advantage over classical system identification methods as it does not require an expensive and disruptive step-test campaign to develop linear transfer function models. The model is ideal for model-based predictive process control.

A Comparative Study of Parameter Identification Methods for Asymmetric Nonlinear Systems with Quadratic and Cubic Stiffness

Understanding the nonlinear dynamic characteristics of engineering structures is challenging, especially for the systems that exhibit asymmetric nonlinear behavior. This paper compared four parameter identification methods for asymmetric nonlinear systems incorporating quadratic and cubic stiffness nonlinearities. Hilbert transform, zero-crossing, direct quadrature, and wavelet transform were used to obtain the backbone, envelope, and restoring force curves from the free vibration time history. A nonlinear curve-fitting method was then applied to estimate the stiffness parameters of the asymmetric systems, and a linear least square fitting approach was utilized to estimate the damping parameters of the asymmetric systems. We used the Helmholtz–Duffing oscillator as a numerical example and a nonlinear vibration absorber with geometric imperfections to verify the feasibility and accuracy of these methods. The advantages and disadvantages of these methods and the deviations in estimated results are discussed.

Model Öngörülü Kontrol ile DA Motor Konum Kontrolü

Bu çalışmada sabit mıknatıslı fırçalı doğru akım (DA) motor sisteminin konum kontrolü için modern kontrol yöntemlerinden model öngörülü kontrolcü tasarımı gerçekleştirilmiş ve önerilen kontrolcü tasarımı farklı testler ile gerçek zamanlı olarak doğrulanmıştır. Ele alınan sistem modeli, sistem tanımlama yöntemlerinden kara kutu modeli yaklaşımı ile elde edilmiştir. Deneysel veri setleri ile elde edilen sistem modeli üzerinden model öngörülü kontrolcü tasarımı geliştirilmiştir. Geliştirilen tasarımcı modeli, Matlab destekli Waijung blok seti ve STM32F4 uygulama geliştirme kiti kullanılarak gerçek zamanlı olarak gerçekleştirilmiştir. Farklı referans girişleri ve parametre değişimleri altında kontrolcü performansı test edilmiştir. Sistem çıkışının tüm referans girişleri başarılı bir şekilde takip etmesi ve değişen sistem parametrelerine rağmen başarısını sürdürmesi önerilen kontrolcü tasarımını doğrulamıştır. Ayrıca klasik kontrol yaklaşımlarından PID denetleyici ile sonuçlar karşılaştırılarak model öngörülü kontrolcü üstünlüğü deneysel sonuçlarla vurgulanmıştır.

Sensitivity-based constitutive parameter identification of nonlinear structures with unknown input earthquake excitation

System identification methods are widely developed in structural health monitoring, in which the force or structural damage is identified from a linearised structure. However, the real-world civil infrastructures are generically nonlinear and subject to extreme loading conditions, such as earthquake. This study proposes a system identification method for material constitutive nonlinear structures subject to earthquake, where sensitivity analysis is developed to accelerate the complicated nonlinear system identification. First, the nonlinear structural model based on the material constitutional law is constructed by fiber beam-column elements. The earthquake input is parameterized by Chebyshev orthogonal polynomials in a recognizable form. Afterwards, the dynamic response sensitivities with respect to the constitutive parameters and the orthogonal factors of earthquake excitation are derived to accelerate the system identification from a complex material constitutive model. Finally, the constitutive parameters and the earthquake input are simultaneously identified by an optimization process. Numerical structures subjected to earthquake excitation are employed to verify the accuracy of the proposed method, by which the influence of parameter's order, input intensity and measurement noise is discussed. The proposed method develops Chebyshev parameterization to recognize the earthquake input and sensitivity analysis to accelerate the optimization, which leads to an accurate nonlinear system identification of civil structures.

Cover Feature: Investigation of the Effect of Temperature on Lithium‐Sulfur Cell Cycle Life Performance Using System Identification and X‐Ray Tomography (Batteries & Supercaps 8/2022)

The Cover Feature illustrates a discharge profile of a 19 Ah lithium-sulfur (Li-S) pouch cell subject to the required power on the Millbrook London Transport Bus (MLTB) driving cycle. In this study, cycle life performance of the Li-S cell is investigated at various temperatures using system identification and X-ray tomography methods. More information can be found in the Research Article by A. Fotouhi and co-workers.

IDENTIFICATION OF LINEAR DYNAMIC SYSTEMS IN THE ENVIRONMENT OF POLYNOMYAL SIGNALS

A method for the structural and parametric identification of one-dimensional linear stationary dynamic systems, represented by differential "inputoutput" constraint equations, is proposed. The method is focused on both active and passive experiments. The method is based on a polynomialrepresentation of the input and output signals of the identified dynamic system. A compact vector-matrix representation of polynomials is proposed,which makes it possible to find the forced component of the solution of linear differential equations as a result of performing simple linear algebraicoperations. The vector-matrix representation of polynomials made it possible to quite simply solve the problem of inversion of linear dynamicalsystems and the problem of compensating the measured perturbation. The issues of representing time signals in polynomial form are not considered inthis paper. Based on the obtained linear representation of a one-dimensional dynamic system, which links the parameters of the input and outputsignals with the parameters of the differential equation of the identified dynamic system mathematical model, a linear system of algebraic equations forunknown coefficients of the differential process equation is obtained. In the general case, the resulting system belongs to the class of overdeterminedsystems, and therefore its solution can be obtained by the least-square technique and is reduced to finding a pseudoinverse matrix. A block diagram ofsoftware for solving the problem of structural and parametric identification in the environment of polynomial signals is proposed. The algorithmincludes the procedure of comparing the results of numerical simulation of the identified model with the output experimental signal and correcting thestructure of the model based on the results of the comparison.

Robotic Disassembly of Electric Vehicles’ Battery Modules for Recycling

Manual disassembly of the lithium-ion battery (LIB) modules of electric vehicles (EVs) for recycling is time-consuming, expensive, and dangerous for technicians or workers. Dangers associated with high voltage and thermal runaway make a robotic system suitable for the automated or semi-automated disassembly of EV batteries. In this paper, we explore battery disassembly using industrial robots. To understand the disassembly process, human workers were monitored, and the operations were analyzed and broken down into gripping and cutting operations. These operations were selected for automation, and path planning was performed offline. For the gripper, a linear quadratic regulator (LQR) control system was implemented. A system identification method was also implemented in the form of a batch least squares estimator to form the state space representation of the planar linkages used in the control strategy of the gripper. A high-speed rotary cut-off wheel was adapted for the robot to perform precise cutting at various points in the battery module case. The simulation results were used to program an industrial robot for experimental validation. The precision of the rotary cutter allowed for a more direct disassembly method as opposed to the standard manual method. It was shown that the robot was almost twice as fast in cutting but slower in pick and place operations. It has been shown that the best option for disassembly of a LIB pack is a human–robot collaboration, where the robot could make efficient cuts on the battery pack and the technician could quickly sort the battery components and remove connectors or fasteners with which the robot would struggle. This collaboration also reduces the danger encountered by the technician because the risk of shorting battery cells while cutting would be eliminated, but the time efficiency would be significantly improved. This paper demonstrates that a robot offers both safety and time improvements to the current manual disassembly process for EV LIBs.

An Enhanced Time-Delay-Based Reference Current Identification Method for Single-Phase System

This article proposes an enhanced time-delay-based current component identification method for single-phase systems to overcome the absence of filtering and poor grid frequency response of conventional method. The proposed technique integrates a delayed signal cancellation with a time-delay unit, to incorporate a dc offset rejection feature, while maintaining speedy and precise extraction of orthogonal current components. The frequency adaption is also developed through the incorporation of linear interpolation. The dynamic and steady-state performance of the proposed method is analyzed and compared with two advanced second-order generalized integrator (SOGI) based methods, namely, modified SOGI and cascaded SOGI under different operating scenarios. The simulation and experimental results substantiate the advantage of zero steady-state error and lower settling time of the proposed solution while exhibiting comparable overshoot with advanced SOGI-based methods.