Application Of System Identification Research Articles

Congruential Summation-Triggered Identification of FIR Systems under Binary Observations and Uncertain Communications

With the advancement of network technology, there has been an increase in the volume of data being transmitted across networks. Due to the bandwidth limitation of communication channels, data often need to be quantized or event-triggered mechanisms are introduced to conserve communication resources. On the other hand, network uncertainty can lead to data loss and destroy data integrity. This paper investigates the identification of finite impulse response (FIR) systems under the framework of stochastic noise and the combined effects of the event-triggered mechanism and uncertain communications. The study provides a reference for the application of remote system identification under transmission-constrained and packet loss scenarios. First, a congruential summation-triggered communication scheme (CSTCS) is introduced to lower the communication rate. Then, parameter estimation algorithms are designed for scenarios with known and unknown packet loss probabilities, respectively, and their strong convergence is proved. Furthermore, an approximate expression for the convergence rate is obtained by data fitting under the condition of uncertain packet loss probability, treating the trade-off between convergence performance and communication resource usage as a constrained optimization problem. Finally, the rationality and correctness of the algorithm are verified by numerical simulations.

Application of nonlinear system identification and feature extraction in fault diagnosis of the cracked cantilever beam

The cantilever beam structure is extensively applied in civil, transportation, and mechanical fields. It is generally prone to structural damage like cracks arising from material defects, operation conditions, and environmental factors. Cracks in a cantilever beam generally induce nonlinear behavior, potentially compromising structural durability and even posing threats to production safety. The present study primarily focuses on online and quantitative fault diagnosis of cracks in the cantilever beam. A simulation model is established for the cantilever beam with varying cracks, where responses corresponding to excitation signals are collected. A Nonlinear Autoregressive with Exogenous inputs (NARX) model for the cantilever beam is identified, and Nonlinear Output Frequency Response Functions weighted contribution rate (WNOFRFs) are evaluated from the established NARX model. A WNOFRFs-associated index is extracted to quantify the severity of breathing cracks. Simulation results indicate that this approach can online diagnose and quantify the severity of crack faults to a certain extent. The applicability of this approach is further verified through experiments on a cracked cantilever beam.

Efficient very large-scale integration architecture design of proportionate-type least mean square adaptive filters

The effectiveness of adaptive filters are mainly dependent on the design techniques and the algorithm of adaptation. The most common adaptation technique used is least mean square (LMS) due its computational simplicity. The application depends on the adaptive filter configuration used and are well known for system identification and real time applications. In this work, a modified delayed μ-law proportionate normalized least mean square (DMPNLMS) algorithm has been proposed. It is the improvised version of the µ-law proportionate normalized least mean square (MPNLMS) algorithm. The algorithm is realized using Ladner-Fischer type of parallel prefix logarithmic adder to reduce the silicon area. The simulation and implementation of very large-scale integration (VLSI) architecture are done using MATLAB, Vivado suite and complementary metal–oxide– semiconductor (CMOS) 90 nm technology node using Cadence register transfer level (RTL) Genus Compiler respectively. The DMPNLMS method exhibits a reduction in mean square error, a higher rate of convergence, and more stability. The synthesis results demonstrate that it is area and delay effective, making it practical for applications where a faster operating speed is required.

Robust Normalized Subband Adaptive Filter Algorithm Based on Logarithmic and Total Least Squares for Correlated Input Signals and Impulsive Noise

The total least squares normalized subband adaptive filter (TLS-NSAF) algorithm proposed in recent years has shown excellent performance in processing the errors-in-variables (EIV) model of correlated input signals. However, when the system is Interferenced by the impulsive noise, the convergence of TLS-NSAF algorithm will be seriously deteriorated. To address this problem, this paper improves the TLS-NSAF algorithm by using the logarithmic function, and proposes a robust NSAF algorithm based on logarithmic and total least squares method (RNSAF-LTLS). The algorithm performs well in an environment where input signal is a correlated input signal and output signal contains impulsive noise. In addition, there are few studies in the existing literature that apply the TLS method to subbands, and there are few performance analyses associated with it. To address this problem, this paper analyzes the local stability, derives the step size that guarantees the stability of the RNSAF-LTLS algorithm, and calculates the steady-state mean squared deviation (S-MSD). Finally, the RNSAF-LTLS algorithm is verified in system identification (SI) and acoustic echo cancellation (AEC) applications. The simulation results prove the superiority of the proposed algorithm and the correctness of the theoretical analysis.

On kernel design for regularized non-causal system identification

Through one decade’s development, the kernel-based regularization method (KRM) has become a complement to the classical maximum likelihood/prediction error method and an emerging new system identification paradigm. One recent example is its application in the non-causal system identification, and the key issue lies in the design and analysis of kernels for non-causal systems. In this paper, we develop systematic ways to deal with this issue. In particular, we first introduce the guidelines for kernel design and then extend the system theoretic framework to design the so-called non-causal simulation-induced (NCSI) kernel, and we also study its structural properties, including stability and semiseparability. Finally, we consider some special cases of the NCSI kernel and show their advantage over the existing kernels through numerical simulations.

Application of system identification in a practical active noise control system

Application of system identification in a practical active noise control system

A variable step size total least squares affine-projection-like algorithm: Formula derivation and performance analysis

In the errors-in-variables (EIV) model, the steady state characteristics of the affine-projection-like (APL) algorithm are poor. Hence, the total least squares APL (TLS-APL) algorithm different from the bias-compensated is proposed, which can reduce the adverse influence of input and output noise on weight update. Then, the local extremum of the TLS-APL algorithm is verified, that is, the expected parameter is the optimal value of the iterative estimation. Moreover, the mean, mean square, and steady-state mean square deviation (MSD) performance of the proposed TLS-APL algorithm are deduced by using the Kronecker product and vec operator, respectively. To balance the convergence characteristics and steady-state characteristics, the step size optimization of the TLS-APL algorithm, namely the variable step size TLS-APL (VSS-TLS-APL) algorithm, is derived from the analysis of MSD. Considering the existence of gradient errors, the fitting effects of theoretical steady-state MSD curves and actual steady-state MSD curves under different parameters are analyzed separately, which proves the rationality of theoretical derivation. In addition, the VSS-TLS-APL algorithm with optimized step size is compared with the TLS-APL algorithm with fixed step size in the application of system identification, and the effect of the variable step size (VSS) is verified. The experiments of system identification and acoustic echo cancelation prove that the VSS-TLS-APL algorithm is superior to the existing known algorithms.

A Novel NLMS Algorithm for System Identification

In this paper, we propose a novel normalized least mean squares (NLMS) algorithm for system identification applications. Our approach involves analyzing the mean squared deviation performance of the NLMS algorithm using a random walk model to select two optimal parameters, the step size and regularization parameters, for the rapid convergence of the colored input signals. We verified that the proposed algorithm exhibited faster convergence than existing algorithms, even in scenarios of sudden system changes.

Flight-Test System Identification Techniques and Applications for Small, Low-Cost, Fixed-Wing Aircraft

This paper provides an overview of flight-test system identification methods applied in the Virginia Tech Nonlinear Systems Laboratory that focus on modeling small, inexpensive, fixed-wing aircraft controlled by a ground-based pilot. The general aircraft system identification approach is outlined with details provided on the flight-test facilities, experiment design methods, instrumentation systems, flight-test operations, data processing techniques, and model identification methods enabling small aircraft flight dynamics model development. Specific small aircraft system identification challenges are overcome, including low-cost control surface servo-actuators and instrumentation systems, as well as a greater sensitivity to atmospheric disturbances and limited piloting cues. Four recent system identification research advancements using the general system identification process are featured, including application of uncorrelated pilot inputs for remotely piloted aircraft, aero-propulsive model development for propeller aircraft, spin aerodynamic model development, and nonlinear dynamic modeling without mass properties information. Although this paper provides a summary of several research efforts, the core system identification approach is presented with sufficient detail to allow the methods to be readily adapted to other research efforts leveraging small, low-cost aircraft.

Advances and Modern Applications of Frequency-Domain Aircraft and Rotorcraft System Identification

This paper provides an overview of advances and applications of aircraft and rotorcraft flight dynamics system identification at the U.S. Army Combat Capabilities Development Command Aviation & Missile Center over the past 20 years. An overarching topic is how frequency-domain system identification has evolved to aid modeling work of modern aircraft and rotorcraft configurations. The paper starts with a brief overview of the frequency-response method for system identification. Subsequently, the paper highlights and demonstrates several new advances using flight-test data from a conventional helicopter, fixed-wing aircraft, advanced coaxial compound rotorcraft, and a small multirotor unmanned aerial vehicle. The first advancement is the use of identification results to improve simulation model fidelity. The next advancement is the Joint Input-Output Method for identification of aircraft with highly correlated inputs. Next, the paper discusses the advancement of flexible constraints, which provide the ability to directly determine physical parameters from flight-test data. The paper then discusses a methodology to identify lower-order linearized dynamic inflow models from higher-order free wake aerodynamic models. Finally, the paper discusses model stitching, which is the technique of combining or stitching together individual linear models and trim data for discrete flight conditions to produce a continuous, full flight-envelope real-time stitched simulation model.

Retrospective and Recent Examples of Aircraft and Rotorcraft System Identification at DLR

Aircraft system identification has a five-decades-long tradition at German Aerospace Center (DLR). Over the last two decades, the research covered various topics related to system identification of fixed- and rotary-wing aircraft, nonconventional applications and atmospheric effects, the development of new flight-test procedures for system identification purposes, and specific aircraft model enhancements and corresponding parameter estimation. Comprehensive tools were developed that support this research and can be applied to a variety of different problems and types of vehicles. The paper starts with a short description of the different system identification methods used at DLR and the corresponding tools. The discussion of flight-test procedures and maneuver design as well as sensor fusion and flight-path reconstruction provides information on how to optimize the flight tests for system identification and to arrive at a consistent flight-test database. The examples for fixed-wing aircraft provide information on identification including abnormal conditions such as icing and interaction with atmospheric disturbances as well as modeling of structural mechanics and loads. The identification of high-order rotorcraft models that account for rotor and engine dynamics and even structural modes is discussed, and the identification of rotor mast moments as well as the identification of non-physics-based models and their integration into physics-based models are also covered. A final section shows that system identification can also be used to derive models for gyroplanes and parachutes as well as to derive control equivalent turbulence input models and to estimate complex wind field geometries. Thus, a broad overview of possible applications of system identification is given.

Application of an offline identification algorithm for adjusting parameters of a modular manoeuvring mathematical model

The paper describes the continuation of studies carried out earlier in the application of system identification to the estimation of parameters of ship manoeuvring mathematical models. A new version of a versatile identification algorithm based on various metrics and global optimization schemes is described. The algorithm is tested on artificial training data obtained with an entirely different ship mathematical model. The presented results confirm sufficient effectiveness and robustness of the algorithm which is now considered as sufficiently matured for application to real-world experimental data. At the same time, the study demonstrates the importance of using sufficiently complete and detailed mathematical structures and of establishing appropriate and sufficiently large intervals for the model parameters’ variations.

Bias-compensated sign subband adaptive filtering algorithm with individual-weighting-factors: Performance analysis and improvement

This paper analyzes the statistical behavior of the bias-compensated sign subband adaptive filtering algorithm with individual-weighting-factors (BC-IWF-SSAF) under errors-in-variables (EIV) model. The theoretical expressions for transient and steady-state behaviors of BC-IWF-SSAF in the mean and mean-square senses are derived through utilizing some reasonable assumptions and Price's theorem. Furthermore, the stability ranges of algorithm are provided. To accomplish performance enhancement, the variable step-size version (VSS) of BC-IWF-SSAF, called VSS-BC-IWF-SSAF algorithm, is developed based on the transient model via minimizing the mean-square deviation (MSD) at every iteration to tackle the contradictory requirements of quick convergence rate and high filtering accuracy caused by fixed step-size (FSS). Finally, Monte-Carlo simulations under system identification and acoustic echo cancellation applications certificate the effectiveness of statistical analysis and variable step-size strategy.

Affine Projection Sign Subband Adaptive Filter Algorithm With Unbiased Estimation Under System Identification

The affine projection sign subband adaptive filter (AP-SSAF) was designed by utilizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> most recent input samples to further enhance the performance of the traditional SSAF. However, the abovementioned algorithms only concentrate on the noise interferences that exist in the output of system under system identification application. Actually, the input of adaptive filter could contain the noises as well. Thereby the performance of the above algorithms will inevitably deteriorate. Based on the excellent performance of AP-SSAF, in this brief, through incorporating unbiasedness criteria into the AP-SSAF, the bias-compensated AP-SSAF (BC-AP-SSAF) is devised for diminishing the impact of input noises via introduced bias compensation term, with some necessary assumptions. And the convergence is provided. Monte-Carlo simulations certify that the proposed BC-AP-SSAF holds stability under impulsive interferences and decreases the estimated bias markedly.

Application of Expert System Identification of Horticultural Plant Diseases with Certainty Factor and Forward Chaining for Smart Village Concept Development

Purpose: This research was conducted to help identify diseases early and provide suggestions for recommendation systems for these plants in general that are beneficial for farmers.Design/methodology/approach: This research goes through several stages, namely planning , analysis, design, and implementation.Findings/result: CLIPS-based Horticultural Plant Disease Identification Expert SystemOriginality/value/state of the art: In the process of diagnosing plant diseases, it requires the accuracy and thoroughness of an expert or experts on symptoms that indicate a disease because of the similarity of these symptoms. Misdiagnosis of existing symptoms causes differences in the results of the diagnosis with the actual disease suffered by the plant. Along with the development of technology, a system was devised that would help report early identification of diseases and provide suggestions for recommendation systems for these plants in general that are beneficial to farmers.

Robust Constrained Affine-Projection-Like Adaptive Filtering Algorithms Using the Modified Huber Function

In this brief, two robust constrained affine-projection-like M-estimate (CAPLM) adaptive filtering algorithms are proposed, which solve the problem that the traditional constrained affine projection (CAP) algorithm is not robust to impulsive interference, and realize unbiased output in some applications where the desired signal is unavailable or unnecessary. Specifically, a modified Huber function (MHF) based robust AP-like (APL) minimization problem with constraints is defined and can be transformed into two different unconstrained optimization problems by using Lipschitz continuity and two different methods, and then CAPLM-I and CAPLM-II algorithms are obtained respectively. Both CAPLM algorithms can avoid a certain amount of computational complexity caused by the inversion of the input signal matrix in classical AP algorithm, and realize the robustness to impulsive interference. In addition, the mean square stabilities of them are analyzed, and the corresponding stable step size ranges are also given. Simulation results show that the proposed CAPLM-I and CAPLM-II algorithms perform well in system identification and beamforming applications in impulsive noise environment, and provide lower steady-state error and faster convergence speed than other compared algorithms.

A data-driven approach for analyzing Hall thruster discharge instability leading to plasma blowoff

The Hall-effect thruster is a prominent space propulsion system, providing a high specific impulse and thrust-to-power ratio. However, detrimental phenomena occurring in Hall thrusters, such as plasma instability and blowoff, are less understood. In this study, we employ a data-driven approach for analyzing the blowoff phenomenon of the Hall-effect propulsion system. From a 600-W class Hall thruster, we observe the oscillatory behavior of the anode current with a frequency of 34–38 kHz, which sometimes leads to plasma blowoff. We employ an output-only system identification framework that uses the noise-induced dynamics at this frequency, extracting linear and nonlinear coefficients of the low-order system model. The computed coefficients suggest that the plasma blowoff is characterized by large linear and small nonlinear parameters of the stochastic Van der Pol equation. From the obtained results, we conclude that this framework can be exploited either to identify a reliable operating condition with a low possibility of plasma blowoff or to diagnose the blowoff susceptibility of the thruster. This research constitutes the first application of stochastic system identification to the Hall-effect thruster, opening new possibilities for avoiding unwanted oscillations and blowoff phenomena in electric propulsion systems.

Estimation of Soil–Structure Model Parameters for the Millikan Library Building Using a Sequential Bayesian Finite Element Model Updating Technique

We present a finite element model updating technique for soil–structure system identification of the Millikan Library building using the seismic data recorded during the 2002 Yorba Linda earthquake. A detailed finite element (FE) model of the Millikan Library building is developed in OpenSees and updated using a sequential Bayesian estimation approach for joint parameter and input identification. A two-step system identification approach is devised. First, the fixed-base structural model is updated to estimate the structural model parameters (including effective elastic modulus of structural components, distributed floor mass, and Rayleigh damping parameters) and some uncertain components of the foundation-level motion. Then, the identified structural model is used for soil–structure model updating wherein the Rayleigh damping parameters, the stiffness and viscosity of the soil subsystem (modeled using a substructure approach), and the foundation input motions (FIMs) are estimated. The identified model parameters are compared with state-of-practice recommendations. While a specific application is made for the Millikan Library, the present work offers a framework for integrating large-scale FE models with measurement data for model inversion. By utilizing this framework for different civil structures and earthquake records, key structural model parameters can be estimated from the real-world recorded data, which can subsequently be used for assessing and improving, as necessary, state-of-the-art seismic analysis and structural modeling techniques. This paper presents an effort towards using real-world measurements for large-scale FE model updating in the soil and structure, uniform soil time domain for joint parameter and input estimation, and thus paves the way for future applications in system identification, health monitoring, and diagnosis of civil structures.

Robust Exponential Hyperbolic Sine Adaptive Filter for Impulsive Noise Environments

In recent years, the hyperbolic family of adaptive algorithms have been widely used to combat impulsive noise. The novel exponential hyperbolic sine adaptive filters (EHSAF) and the normalized exponential hyperbolic sine adaptive filter (NEHSAF) suitable for impulsive noise environments are proposed in this brief. The cost function is based on the exponential hyperbolic sine-based error function. The stability condition based on the learning rate and the steady-state analysis are investigated too. Additionally, a variable scheme for the scaling parameter is proposed to remove the tradeoff between convergence speed and steady-state excess mean square error (EMSE). The computational complexity is presented too. The simulation results in the context of unknown system identification and echo cancellation application have been performed to prove the performance improvement of the proposed algorithms.

An efficient implementation for spatial–temporal Gaussian process regression and its applications

Spatial–temporal Gaussian process regression is a popular method for spatial–temporal data modeling. Its state-of-art implementation is based on the state-space model realization of the spatial–temporal Gaussian process and its corresponding Kalman filter and smoother, and has computational complexity O(NM3), where N and M are the number of time instants and spatial input locations, respectively, and thus can only be applied to data with large N but relatively small M. In this paper, our primary goal is to show that by exploring the Kronecker structure of the state-space model realization of the spatial–temporal Gaussian process, it is possible to further reduce the computational complexity to O(M3+NM2) and thus the proposed implementation can be applied to data with large N and moderately large M. The proposed implementation is illustrated over applications in weather data prediction and spatially-distributed system identification. Our secondary goal is to design a kernel for both the Colorado precipitation data and the GHCN temperature data, such that while having more efficient implementation, better prediction performance can also be achieved than the state-of-art result.