System Identification Method Research Articles

Linear system identification of the UC San Diego Geisel Library building under ambient vibration

AbstractThis paper focuses on system identification (SID) of the UC San Diego Geisel Library building using ambient vibration (AV) data, assuming that the building’s behavior can be fully described by linear models in terms of material, geometry, damping, etc. Three state-space-based, output-only time domain SID methods are applied and fully automated to identify the library’s modal properties using AV data from both a 15-day and a 486-day monitoring period. The modes identified from the AV data are higher-order coupled torsional-flexural modes. The identified modal properties are influenced by the atmospheric conditions, and the amplitude of the building’s ambient vibration. The time-varying identified modal properties show a cyclical 1-day pattern due to human activity, earth tremors, and short-term changes in atmospheric conditions such as wind speed and temperature. One of the output-only SID methods was used to estimate modal properties from ambient vibration data recorded continuously over a 486-day period, including three low-intensity earthquakes. No permanent changes in the identified modal properties were observed due to the three low-intensity earthquakes that occurred during that period. Renovations of the Geisel Library involving only non-structural components (e.g., non-load-bearing partition walls, changes in space allocations, and inertial/live loads) caused some discontinuities in the identification of the modes of interest in this study. The influence of the data window length on system identification results, in terms of identification success rate and estimation uncertainty, was investigated. The identified state-space models are also used to assess the relative contribution of the ambient base excitation to the building’s total ambient vibrational response. This research offered a unique opportunity to study linear SID of a large and complex real-world structure under ambient excitations, and the effects of changing environmental conditions on the identified modal properties. It provided insight into some of the causes of the observed temporal variation in the identified modal properties. The SID results presented in this study also provide a baseline for future structural health monitoring studies of the Geisel Library building.

Influence of Nonlinear Static Deformation on the Pre-Pazy and Pazy Wing Flutter

This paper investigates the influence of a nonlinear static deformation on the dynamic aeroelastic behavior of very flexible wings. The objects of study are the Pre-Pazy and Pazy wings, which are highly flexible wings presented by the Large Deflections Working Group of the Third Aeroelastic Prediction Workshop. A nonlinear static aeroelastic coupling analysis is performed, coupling a nonlinear beam formulation with the Vortex Lattice Method. The natural frequencies and mode shapes of the wing are obtained considering the predeformed structure, and these results serve as input data for the dynamic aeroelastic model, which considers the Unsteady Vortex Lattice Method as the aerodynamic model and serves to yield the aeroelastic responses in modal coordinates. The frequency spectrum of each time response serves as input for a system identification method, which considers the Least Squares Complex Frequency-domain estimator. The velocity–damping–frequency diagrams are obtained from the identified frequencies and damping ratios, and the critical flutter condition is determined. The flutter speeds obtained agree with the references, and the differences are up to 77% for the Pre-Pazy wing and 4% for the Pazy wing. A significant reduction in those speeds is observed with an increase in the angle of attack.

A robust variational mode decomposition based deep random vector functional link network for dynamic system identification

The complexity of system identification problems has been escalated due to their diverse range of applications. In this paper, the non-linear system identification problem is addressed by proposing a deep random vector functional link network (Deep-RVFLN) based on the optimized variational mode decomposition (OVMD). The proposed method has a faster learning speed and trains the network accurately without tuning parameters. Introducing a random link network connecting the input and output layers may lead to reduction in model complexity. To enhance the accuracy and reduce errors, a random vector functional link network (RVFLN) has been implemented with an increased number of hidden layers. The variational mode decomposition (VMD) algorithm is applied to decompose the signal and select optimum modes using an improved particle swarm optimization (IPSO) algorithm. In this method, the data fidelity factor (α) and the number of decomposition modes (k) are chosen by a new discrete Teaser energy operator (DTEO). The DTEO algorithm is utilized to estimate Teaser energy and it serves as a dependable indicator of overall system reliability. To test the efficacy of the model, three complex non-linear benchmark models named autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) have been considered with examples 1, 2, and 3 respectively. Based on the results and analysis, the proposed method was found to be better than other state-of-the-art methods. Finally, the proposed Deep-RVFLN identifier is implemented on a high-speed reconfigurable field-programmable gate array (FPGA) to validate the efficacy of the proposed method for non-linear system identification in the hardware platform.

Systemic drug repurposing for pancreatic cancer based on genetic and epigenetic network analysis using a systems biology approach and deep neural learning of drug-target interactions

Pancreatic cancer is a malignant tumor associated with a high mortality rate. This research presents a systems biology approach to explore the mechanisms of pancreatic ductal adenocarcinoma (PDAC), aiming to identify significant biomarkers that can serve as drug targets. We propose a systematic drug repurposing strategy that incorporates a deep neural network (DNN)-based drug-target interaction (DTI) model along with drug design specifications to develop a potential multi-molecule drug for PDAC treatment. We first established candidate protein-protein interaction networks and gene regulatory networks using big data mining techniques. Real PDAC and non-PDAC genome-wide genetic and epigenetic networks (GWGENs) were systematically identified using their corresponding microarray data through system identification and system order detection methods. The top 6,000 core GWGENs of PDAC and non-PDAC were extracted using the Principal Network Projection method. Subsequently, we annotated the core GWGENs using the Kyoto Encyclopedia of Genes and Genomes pathways to construct their respective core signaling pathways. By comparing upstream microenvironmental factors, core signaling pathways, and downstream aberrant cellular functions between PDAC and non-PDAC, we investigated the carcinogenic mechanisms of PDAC. Notably, c-MYC, forkhead box O3, and tumor suppressor p53 were identified as significant biomarkers for potential drug targets. Furthermore, the DNN-based DTI model predicted the interaction probabilities between candidate molecular drugs and these biomarkers. Based on drug design specifications such as regulatory ability, sensitivity, and toxicity, suitable multi-molecular potential drugs were selected. Ultimately, gemcitabine and MK-2206 were identified as a promising multi-molecular drug combination for PDAC treatment.

Multimodal modeling and identification study of a quadrotor robot

Abstract The flight control system of a flying robot plays a crucial role. Therefore, it is imperative to establish various flight mode control models. Traditional modeling methods face challenges in meeting the demands of rapid research and development due to the emergence of diverse aircraft shapes and load forms. To address these issues, this study focuses on multi-conditions modeling and system identification. Based on flight dynamics theory, a comprehensive quadrotor robot dynamics model along with its state space model was established. Flight tests were designed for system identification under different flight modes. The data obtained from the flight test was preprocessed using an extended Kalman filter algorithm based on flight path reconstruction technology to reduce constant deviation from airborne sensors. Frequency response identification technology was employed to identify control models for hovering mode and forward flight at 10m/s mode in the quadrotor robot. Model structures were determined based on precision index evaluation criteria. Time-domain validation method was utilized to simulate fitting error RMS for each channel model under multiple conditions using different signal excitations derived from datasets collected during experiments. The results confirmed that the proposed aircraft system identification method is effective and feasible.

Closed-loop continuous-time Laguerre-based subspace identification via orthogonal projection approach

This paper presents a closed-loop Laguerre-based subspace identification method for continuous-time systems via orthogonal projection approach. A bank of Laguerre filters operating in the all-pass domain, which is capable of handling the non-equidistant data, is used to get the input–output matrix equation of the systems. We put the emphasis on the parity space opposed to the observable subspace. The instrumental variable method and principal component analysis estimate the system consistently, solving the issue of biased estimate for the closed-loop operation of the system caused by the feedback controller. The simulation results demonstrate the effectiveness of the proposed approach.

Auxiliary model-based maximum likelihood multi-innovation recursive least squares identification for multiple-input multiple-output systems

The aim of this paper is to propose novel identification methods for multiple-input multiple-output systems. Through decomposing a system into subsystems, the system identification model is derived. Based on the obtained sub-model, an auxiliary model-based maximum likelihood recursive least squares algorithm is derived for parameter estimation. For further enhancing the estimation accuracy, the auxiliary model-based maximum likelihood multi-innovation recursive least squares (AM-ML-MIRLS) algorithm is proposed based on the proposed algorithm. Simulation results test the proposed algorithms are all effective, and prove that the proposed AM-ML-MIRLS algorithm has the superior performances in capturing the dynamic properties of the system.

Bridge monitoring using mobile sensing data with traditional system identification techniques

AbstractMobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode shapes (the absolute value of mode shapes, i.e., mode shapes without phase information) can be estimated. However, time‐synchronized data and improved system identification techniques are necessary to estimate frequencies, full mode shapes, and damping ratios within the same context. This paper presents a framework that uses only two time‐synchronous mobile sensors to estimate a spatially dense frequency response matrix. Subsequently, this matrix can be integrated into existing system identification methods and structural health monitoring platforms, including the natural excitation technique eigensystem realization algorithm and frequency domain decomposition. The methodology was tested numerically and using a lab‐scale experiment for long‐span bridges. In the lab‐scale experiment, synchronized smartphones atop carts traverse a model bridge. The resulting cross‐spectrum was analyzed with two system identification methods, and the efficacy of the proposed framework was demonstrated, yielding high accuracy (modal assurance criterion values above 0.94) for the first six modes, including both vertical and torsional. This novel framework combines the monitoring scalability of mobile sensing with user familiarity with traditional system identification techniques.

C-type two-thermocouple sensor design between 1000 and 1700 °C.

At the present stage of transient ultra-high-temperature energy release of boron-containing warm-pressure explosives, single thermocouples are often used for multi-point measurements in the process of their temperature field changes, and the results of their temperature field reconstruction are not satisfactory due to the limited consistency of the thermocouples. Aiming at the above-mentioned problems, a C-type two-thermocouple suitable for transient temperature measurement in high-temperature environments is designed; the system characterization of the two-thermocouple is carried out by using the blind system identification method of the inter-relationships; the identification process is evaluated by a new cost function; and the optimal solution on the new cost function is realized by using the gradient descent method. The temperature reconstruction of the two-thermocouple output excited by the simulated heat source is carried out by using the auto-regressive with extra inputs model, and the feasibility of the reconstruction results is verified. In the experimental part, a thermocouple dynamic characteristic calibration system based on a high-temperature furnace is constructed and experimental validation is carried out in the high-temperature furnace to compare the effects of different exposure lengths and different wire diameters on the output of the two-thermocouple, and as a result, the outputs are corrected and analyzed. The results show that two-thermocouple methods with different combinations of wire diameters are better for temperature measurement, with a reconstructed root mean squared error of 0.0162 and a goodness of fit of 89.13%.

Modified extended Rauch–Tung–Striebel smoother method for the dynamic external excitation identification of piezoelectric vibration energy harvesting systems

A vibration energy harvester can collect vibration energy from the environment to supply low-power devices, such as wireless sensor nodes. Reducing the size and power consumption of these devices is a challenging problem. A dual-function device, which includes energy harvesting and vibration measurement, may solve this problem. Therefore, studying inversion methods for identifying the external excitation of a harvester is meaningful. In this study, a modified inversion method that combines an extended Rauch–Tung–Striebel smoother (ERTSS) with the particle swarm optimization (PSO) algorithm, i.e., the ERTSS-PSO method, is proposed to identify the time domain signal of external excitation through the voltage response of a piezoelectric vibration energy harvester. A modified ERTSS approach with sliding windows is developed. The sliding windows are partially overlapped to eliminate identification errors between the two contiguous windows. Near real-time identification is achieved for the small sliding window. PSO is utilized to estimate state noise covariance and measurement noise covariance, improving identification accuracy compared with the L-curve approach. Under this proposed framework, prior knowledge regarding the statistical data of unknown external excitations is unnecessary, enabling a more comprehensive range of applications. To assess the performance of the proposed ERTSS-PSO method, numerical simulations, including two piezoelectric vibration energy harvesting systems with third-order and fifth-order nonlinear stiffness, are conducted to verify the effectiveness of the proposed method under harmonic, multifrequency, and random excitations. The method proposed in this study is also validated by a circular laminated piezoelectric plate harvester under harmonic and random excitations in an experiment. Results from the experimental studies demonstrate that the proposed method improves identification accuracy by at least 9% compared with the extended Kalman filter, at least 7% compared with the augmented Kalman filter (AKF) with Rauch–Tung–Striebel smoother (ARTSS), and at least 40% compared with AKF under random excitation. In addition, the proposed method is unaffected by initial conditions, and it is more stable and accurate than AKF and ARTSS. The proposed method lays the theoretical foundation for identifying the external excitation of a harvester. It is also a possible solution for the miniaturization and compactness of wireless monitoring devices.

Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method.

Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input-output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.

AI-Lorenz: A physics-data-driven framework for Black-Box and Gray-Box identification of chaotic systems with symbolic regression

Discovering mathematical models that characterize the observed behavior of dynamical systems remains a major challenge, especially for systems in a chaotic regime, due to their sensitive dependence on initial conditions and the complex non-linear interactions within the system. The challenge is even greater when the physics underlying such systems is not yet understood, and scientific inquiry must solely rely on empirical data. Despite advancements such as the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, which has shown success in identifying chaotic systems with reasonable accuracy, challenges remain in dealing with noise, sparse data, and the need for models that generalize well across different chaotic systems. Driven by the need to fill this gap, we develop a framework named AI-Lorenz that learns mathematical expressions modeling complex dynamical behaviors by identifying differential equations from noisy and sparse observable data. We train a physics-informed neural network (PINN) with the eXtreme Theory of Functional Connections (X-TFC) algorithm by using data and known physics (when available) to learn the dynamics of a system, its rate of change in time, and missing model terms, which are used as input for a symbolic regression algorithm (PySR) to autonomously distill the explicit mathematical terms. This, in turn, enables us to predict the future evolution of the dynamical behavior. The performance of this framework is validated by recovering the right-hand sides and unknown terms of certain complex, chaotic systems, such as the well-known Lorenz system, a six-dimensional hyperchaotic system, the non-autonomous Sprott chaotic system, and the slow-fast Duffing system, and comparing them with their known analytical expressions and state-of-the-art regression and system identification methods.

Систематизация показателей, отражающих состояние ключевых факторов в модели экономического развития региона

Unstable economic development affects the national economy. The rate, severity, and direction of the current transformations in the context of global changes and the new world order place new demands on the sustainability and stability of such economic systems as regions. Development is a directed process of transformation that affects all the constituent aspects of the developing object. The author examined the mechanism of regional economic development as a unity of investment attractiveness, innovative activity, and labor productivity. The research objective was to systematize the indicators of these factors as a generalized logical diagram. It is a model of economic development driven by a totality of elements aimed at creating and maintaining a system of sustainable and balanced economic development of the region. The study relied on a review of domestic publications, as well as on the method of logical analysis, content analysis, description, and generalization. The method of system identification made it possible to construct a generalized logical diagram. By using a targeted impact on economic processes, one can create a system of sustainable and balanced economic development of the region based on an efficient model of consistent economic processes. The results can be applied in various areas of economic development.

Design perspectives of a system identification approach of the NATO AVT-316 multi-swept wing aerodynamics

Air supremacy requires enhanced maneuvering capabilities at high angles of attack and even beyond the stall angle. High angle-of-attack (high-α) maneuverability can be achieved using swept wings and tailored leading-edge shapes to replace local and disorganized flow separation with controlled separation-induced leading-edge vortices (LEV). Strong leading-edge vortices delay the stall to higher angles and generate a non-linear lift increment up to the angle of attack where the jet-type flow structure of LEV changes to a reversed-flow bubble (vortex breakdown phenomenon). A multi-swept wing configuration has the potential to delay the vortex breakdown to even higher angles as the vortices formed over the front wing can energize vortices formed over the main wing and lead to vortex merging. This article is focused on understanding the unsteady interactions between multiple leading edge vortices formed over multi-swept wing configurations in the subsonic speed regime. Specifically, this article investigates the aerodynamic characteristics of wings being evaluated under the initiative of the NATO STO AVT-316 Task Group. Highly refined meshes, and the use of hybrid turbulence models such as Detached Eddy Simulations (DES) and Delayed DES are required to accurately resolve the vortical flows over these wings. Simulating all flow conditions of interest is a computationally expensive approach. A system identification method was therefore proposed to rapidly and accurately generate aerodynamic models of these wings. The method uses a novel piece-wise chirp (constant amplitude and increasing frequency) motion as an input signal (training maneuver). The motion computational cost is equivalent to the cost of six static CFD simulations, however it can predict aerodynamic responses of the wings over a wide range of angles of attack. The method has been tested for double and triple delta wings. Some design considerations are provided based on predicted flow features and aerodynamic data. Prediction results with different turbulence models, sting geometries, grid resolutions and an adaptive mesh refinement approach are provided.

Identification of fractional order time delay system with measurement noise using variable period integration operational matrix

This paper proposes a parameter identification method for fractional-order time-delay systems with measurement noise, based on the Legendre wavelet variable-period integration operational matrix. A variable-period integration operational matrix and a variable-period time-delay integration operational matrix are constructed by variable-period sampling principle. For the impulse noise, global Gaussian noise and local Gaussian noise present in the identification data, the variable-period sampling, operational matrix global coefficient decomposition and matrix local coefficient decomposition methods are used to reconstruct the variable-period integration operational matrix to separate the noise from the data; the fractional-order time-delay system is expressed as an integral equation by using the reconstructed variable-period operational matrix, this reduces the influence of measurement noise on system identification accuracy. To further improve the parameter identification accuracy, the integral equation is solved by the multi-innovation least squares algorithm. The proposed method’s effectiveness is verified through several simulations and an experimental study of a constant-temperature system.

Identification of flame transfer function and mechanism analysis of the influence of boundary impedance characteristics on thermoacoustic instability

Thermoacoustic instability is a common problem in the operation of modern gas turbines. The prediction of thermoacoustic instability and the clarification of its mechanism are the research focus and difficulty in the gas turbine industry. As a response function of flame to acoustic disturbance, flame transfer function is a key parameter in the study of thermoacoustic instability. In this paper, based on the scaled adaptive simulation (SAS) model combined with the eddy dissipation concept (EDC) combustion model, the time-domain flow field data are processed by the system identification method, and the results of flame transfer function extraction are in good agreement with the experimental values. Then, the detailed derivation process of the low-order thermoacoustic network model (LOTAN) is given to capture the behavior characteristics of the acoustic wave in the thermoacoustic system. On this basis, the effects of acoustic boundary conditions and hysteresis time on the thermoacoustic instability of the combustion system are analyzed, and the relationship between the mode shape, pressure, vibration velocity phase, and thermoacoustic instability is explored. It is found that the phase relationship between pressure and vibration mode can be used to determine the thermoacoustic instability of the system. This is of great practical significance for determining the thermoacoustic instability of the system and clarifying the internal mechanism of its generation and provides theoretical support for the subsequent thermoacoustic instability control.

Sequential Mutual-Inductance Identification Method for Wireless Power Transfer Systems of Electric Vehicles

Sequential Mutual-Inductance Identification Method for Wireless Power Transfer Systems of Electric Vehicles

Identification of generator coherency in power systems with wind farm

In the context of the "dual carbon" goal, the penetration rate of new energy represented by wind power is gradually increasing. The large-scale grid connection of wind power makes the power system more complex and uncertain. The output of wind farms changes the system flow, indirectly affecting the power angle characteristics of synchronous generators, and thereby changing the coherence between generators. Affects synchronous generator homology identification. This paper proposes a generator homology identification method for power systems containing wind farms, in response to the problem that the existing methods for identifying unit homology have not taken into account the adverse effects of wind farms on identification results. Translate the influence of wind farms on the homology of synchronous generators into the contraction admittance matrix as a static electrical distance indicator; Select dynamic data reflecting the homology of synchronous generators after disturbance, and form four dynamic indicators to measure the power angle increment curve between each generator: Euclidean distance, Chebyshev distance, grey correlation, and correlation coefficient; By using the combination weighting method to determine the weights of each indicator, a comprehensive similarity matrix is formed, and the optimal clustering results are determined using fuzzy system clustering and F-statistical values. Finally, the effectiveness of the proposed method was verified using EPRI-9 node system, EPRI-36 node system, and IEEE68 node system as examples.© 2017 Elsevier Inc. All rights reserved.

System identification and force estimation of robotic manipulator using semirecursive multibody formulation

AbstractForce estimation in multibody dynamics relies heavily on knowing the system model with a high level of accuracy. However, in complex mechatronic systems, such as robots or mobile machinery, the values of model parameters may be only roughly estimated based on design information, such as CAD data. The errors in model parameters consequently have a direct effect on force estimation accuracy because the estimator compensates the erroneous inertia, friction, and applied forces by changing the value of estimated external force. The objective of this study is to present the workflow of system identification and state/force estimation of an open-loop multibody structure. The system identification utilizes a linear regression identification method used in robotics adapted to the multibody framework. The semirecursive multibody formulation, in particular, is studied as a formulation for both system identification and force estimation. The multibody state/force estimator is constructed using extended Kalman filter. The specific aim of this paper is to demonstrate the utilization of these per se known modeling, identification, and estimation tools to address their current lack of integration as a complete toolchain in virtual sensing of multibody systems. The methodology of the study is tested with both artificial and experimental data of Stäubli TX40 robotic manipulator. In the experimental analysis, an openly available benchmark data set was used. Artificial data were created by running an inverse dynamics analysis with inertia and friction parameters taken from literature. The results show that the multibody inertia and friction parameters can be accurately identified and the identified model can be used to produce decent estimates of external forces. The proposed multibody system identification method itself opens new opportunities in tuning the multibody models used in product development. Moreover, effective use of system identification together with state estimation helps to build more accurate estimators. When the system model is accurately identified, the capability of state estimator to observe unknown inputs, such as external forces, is significantly enhanced.

A Bayesian transfer sparse identification method for nonlinear ARX systems

SummaryIn this paper, we design a transfer sparse identification algorithm under the Bayesian framework through introducing other system knowledge into the system to be identified. This method provides a new identification solution for a nonlinear autoregressive model with exogenous inputs (NARX). The estimates of the transferred parameters are calculated by adding the transfer correction term to the un‐transferred estimates. To achieve this, a joint prior distribution is devised for the parameters, ultimately enhancing the efficient utilization of existing data, reducing the reliance on new data, and achieving more accurate identification. The maximized marginal likelihood method is used to find the transfer gain and the transfer information matrix in the transfer correction term. Meanwhile, in order to make the algorithm automatically adapt to different data, we design an automatic structure detection method based on the transfer framework. The method automatically determines the sparsity threshold based on the maximum inter‐class variance. Two examples are provided to demonstrate the advantages of our algorithm.