Filter Identification Research Articles

Model complexity optimization of equivalent dynamical linearization data models used in model‐free adaptive control

AbstractThis paper discusses a strategy for optimizing the complexity of time‐varying data models as used in model‐free adaptive control (MFAC). Here the dynamic linearization in compact form (CFDL), partial form (PFDL), and full form (FFDL) are considered as data models used to describe input/output (I/O) data sets. These data models are built only for control purpose and can have various degrees of complexity depending on the size of the considered time‐window as well as the underlying algorithms. The methodology is to compare the performance of the data models according to an evaluation criterion, to analyze the order of different data models based on bias‐variance trade‐off, and to select the best‐performing model. The complexity of the selected model is compared with the reduced linear time‐invariant (LTI) model obtained by applying a combination of the eigensystem realization algorithm (ERA) and observer/Kalman filter identification (OKID) on the same I/O dataset. The I/O data are obtained from applying the MFAC controllers on a nonlinear three‐tank system (3TS) to track various desired references. The results indicate that the model complexity optimization can also be used for selecting an appropriate MFAC data model with optimal order to reduce the complexity and computational burden of the MFAC control algorithms.

FEM-based eigenstructure recovery of a space truss with active members

AbstractLarge truss structures have many potential applications in space, such as antennas, telescopes and space solar power plants. In this scenario, a natural concern is the susceptibility of these lightweight structures to be damaged during their operational life, due to impacts, transient thermal states and fatigue phenomena. The inclusion of active elements, equipped with sensor/actuator systems capable of modulating their shape and strength, makes it possible to transform the truss into a smart structure capable of remedying the damage, once it is detected. In this paper, a procedure is described that is capable of restoring at least the basic functionality of a composite truss for space applications, starting with the observation that damage has occurred, regardless of its specific location. The system eigenstructure is used as a benchmark for damage detection, as well as a target characteristic for the subsequent restoration activity. The observer/Kalman filter identification algorithm (OKID), in cascade with the eigensystem realization algorithm (ERA), is adopted to reconstruct, from sensor recordings, the dynamic response of the truss in terms of system state-space representation and eigen-characteristics. Finally, a static output feedback control is developed to recover the low-frequency dynamic behaviour of the truss. The entire procedure is tested using finite element analysis. All activities are coordinated in an innovative procedure that, within a unique Python language code, automatically generates finite element (FE) models, launches finite element analysis (FEA), extracts output data, implements OKID-ERA, processes the control law and applies it to the final FE simulation.

A novel time-domain approach for identifying nonlinear structural dynamical system with explicit model based on observer/Kalman filter identification method

In this paper, a novel time-domain method for identifying nonlinear structural dynamical system with explicit model is proposed. By exploiting the observer/Kalman filter identification (OKID) and nonlinear feedback interpretation, the nonlinear parameters of the system are estimated accurately. The effectiveness of the proposed nonlinear OKID (NOKID) method is demonstrated through three numerical examples, namely a five degree-of-freedom system with one nonlinear spring, a three degree-of-freedom system with three nonlinear springs and a nonlinear rectangular membrane structure with all edges clamped. Comparing with the time-domain nonlinear subspace identification (TNSI) method, the proposed NOKID method can identify the nonlinear parameters without system order determination, and the identification results obtained by the proposed method are real numbers and have no dependency on frequency.

Interval type-2 evolving fuzzy Kalman filter for processing of unobservable spectral components from uncertain experimental data

In this paper, an interval type-2 evolving fuzzy Kalman filter (IT2EFKF) is designed for processing unobservable spectral components of uncertain experimental data. The primary objective of the methodology proposed in the present research is to provide a novel mathematical tool for interval and spectral processing of uncertain experimental data in order to deal with real-world filtering, tracking, and forecasting problems. The adopted methodology consider the following steps: an initial model of the interval type-2 fuzzy Kalman filter, which is off-line identified from an initial window of the experimental data; the updating of antecedent proposition of interval type-2 fuzzy Kalman filter by using an interval type-2 formulation of evolving Takagi–Sugeno (eTS) clustering algorithm and the updating of consequent proposition by using a type-2 fuzzy formulation of Observer/Kalman Filter Identification (OKID) algorithm, taking into account the multivariable recursive Singular Spectral Analysis of the experimental data. Computational results for tracking the Mackey-Glass chaotic time series demonstrate the effectiveness of the proposed approach when compared to related methodologies from the literature, with superior results for RMSE of 0.0026. Experimental results for tracking a 2DoF helicopter illustrate its applicability, with superior results for RMSE and VAF of 0.00156 and 99.9874% for yaw angle, and of 0.00702 and 99.8863% for pitch angle, respectively.

System Identification of Unmanned Air Systems at Texas A&M University

This paper presents a summary of system identification flight testing and results for a variety of large and small fixed-wing and multirotor unmanned air systems at Texas A&M University from 1999 to 2023. The six different types of vehicles range from a large powered parafoil, to a fixed-wing vehicle with synthetic jet-actuated roll control effectors, to a radially asymmetric multirotor, to large and small fixed-wing vehicles, and to a Steppe eagle. The observer/Kalman filter identification algorithm is used to generate linear time-invariant state-space models, and the results for both near-real-time online model generation and postflight offline model generation are presented. The use and efficacy of a variety of test input types and their sensitivity to exogenous inputs such as turbulence, in addition to identified model evaluation and selection criteria, are discussed. Several generations of low size, weight, power, and cost flight-test instrumentation including the Developmental Flight-Test Instrumentation data acquisition package are also presented. Challenges that arose from the flight-testing campaigns along with solutions are highlighted in the paper.

Experimental modal analysis of a single-link flexible robotic manipulator with curved geometry using applied system identification methods

In this paper, the principal time-domain system identification methods are studied and used to carry out the experimental modal analysis of a single-link flexible manipulator with curved geometry in a comparative fashion. The identification process carried out in this study is aimed at deriving first-order state-space dynamical models and second-order configuration-space dynamical models of the single-link flexible manipulator by using an array of fundamental system identification procedures, which includes the ARX (AutoRegressive eXogenous method), SSEST (State-Space ESTimation method), N4SID (Numerical Algorithm for Subspace State-Space System Identification), ERA/OKID (Eigensystem Realization Algorithm combined with the Observer/Kalman Filter Identification method), and TFEST (Transfer Function ESTimation method) methods. The main goal of this paper is, therefore, to perform a comparative study between the most fundamental system identification procedures applied to the numerical and experimental analysis of the structural vibrations of a flexible manipulator having a curved geometric shape. First, this paper poses the theoretical foundations for the development of the numerical study and the experimental testing to be performed for the case study. Subsequently, the flexible manipulator analyzed in this work is modeled with various levels of complexity, which range from a simple lumped parameter model to a linear finite element model developed by integrating the interconnections between the SOLIDWORKS and ANSYS software. By doing so, the computational procedures applied to the time-domain experimental measurements of the dynamic behavior of the flexible manipulator allow for reconstructing first-order and second-order mechanical models of the structural system of interest in the MATLAB simulation environment. The numerical results and the experimental tests reported in this investigation demonstrate the effectiveness of all the time-domain system identification approaches considered in the paper.

Type-2 evolving fuzzy Kalman filter design based on unobservable spectral components space for interval tracking of non-stationary experimental data

In this paper, an interval type-2 evolving fuzzy Kalman filter is designed for processing of unobservable spectral components of uncertain experimental data. The adopted methodology consider the following steps: an initial model of the interval type-2 fuzzy Kalman filter, which is off-line identified from an initial window of the experimental data; the updating of antecedent proposition of interval type-2 fuzzy Kalman filter by using an interval type-2 formulation of evolving Takagi-Sugeno (eTS) clustering algorithm and the updating of consequent proposition by using a type-2 fuzzy formulation of Observer/Kalman Filter Identification (OKID) algorithm, taking into account the multivariable recursive Singular Spectral Analysis of the experimental data. The computational results for tracking the Mackey-Glass chaotic time series illustrate the efficiency of proposed methodology as compared to relevant approaches from literature, and the experimental results for tracking a 2DoF helicopter demonstrate its applicability.

Correction of Dynamical Properties of Data Acquisition Systems.

Accurate and fast measurements are important in many areas of everyday engineering and research activities. This paper proposes a method that gives such measurements, additionally shortening the time in which they are obtained. To achieve this, a supplementary discrete-time filter, estimating values of delayed samples of the measured signal, is attached to the output of the data acquisition system. This filter is identified with the use of classical estimation methods, based on a dynamical model of the data acquisition system. The definition of the cost function minimised during filter identification depends on the nature of the environment in which measurements are acquired. The considerations presented in this paper are illustrated with four corresponding simulated case study examples. They show how, in a very simple way, dynamical properties of data acquisition systems may be corrected, and thus improved, using the corresponding supplementary discrete-time filters. The improvement, measured by the correction quality index introduced in the paper, was from a few times up to more than 100. The paper also raises the issue of obtaining models for tuning of the supplementary discrete-time filter. The considerations presented may be applied to formulate the artificial intelligence of data acquisition systems as well as sensors. Finally, the paper proposes to implement this intelligence as a knowledge base of the expert system.

Estimation of Dynamical Thermoacoustic Modes Using an Output Only Observer Kalman Filter-Based Identification Algorithm

Abstract Thermoacoustic instabilities have plagued the operation of gas turbine engines for years and significant research is being conducted in detecting and understanding them. In this paper, an output only identification technique is employed for a noise induced dynamical system representing combustion instability behavior. This approach is called the output only observer Kalman filter identification (O3KID) and its first step solves for least squares from a set of algebraic equations constructed from just the measured output. The least squares solution gives the Markov parameters (impulse response) and the output residuals. The subsequent step takes the Markov parameters or the residuals to solve for the system matrices using any deterministic subspace identification method. In using this direct noniterative two-step algorithm, it is possible to estimate the eigenmodes and damping coefficients from output measured data. To validate the algorithm, a system of independent harmonic oscillators, excited by random noise is used to generate surrogate data representing pressure oscillations in a combustor prior to an instability. The error in estimating the eigen frequencies and damping are <1%. This fast direct approach could be used to provide an early warning indicator in industrial gas turbines by tracking the rate of damping of dominant eigenmodes. Additionally, saving the state space parameters periodically can serve as a data-lean option to track changes of the dynamics and across a gas turbine fleet.

Adaptive Kalman filtration for low noise systems

Kalman filter identification problem is considered in the situation of low noise in observations. This model of observations is widely used in trafic management Antoniou et al. (2010). The proposed procedure consists of several steps. First a preliminary consistent estimator is constructed on the vanishing learning interval. Then this estimator is used to define One-step MLE-process and finally this estimator-process is used for approximation of the unobserved state. It is shown that the estimate thus obtained is consistent, asymptotically Gaussian, and asymptotically efficient. As a result, it is possible to implement a high-speed algorithm for filtering an information process with unknown parameters, which provides good accuracy characteristics.

Robust Graph Filter Identification and Graph Denoising From Signal Observations

Robust Graph Filter Identification and Graph Denoising From Signal Observations

Nicotinamide adenine dinucleotide reduced (NADH) is a natural UV filter of certain bird lens

In this work, we for the first time report the identification of UV filters in the bird eye lens. We found that lenses of some raptors (black kite, common buzzard) and waterfowl (birds from Podicipedidae family) contain unusually high levels of reduced nicotinamide adenine dinucleotide (NADH)—a compound with high absorption in the UV-A range with a maximum at 340 nm. The lens metabolome of these birds also features an extremely low [NAD +]/[NADH] ratio. Chemometric analysis demonstrates that the differences between the metabolomic compositions of lenses with low and high NADH abundances should be attributed to the taxonomic features of bird species rather to the influence of the low [NAD +]/[NADH] ratio. We attributed this observation to the low metabolic activity in lens fiber cells, which make up the bulk of the lens tissue. Photochemical measurements show that properties of NADH as a UV filter are as good as that of UV filters in the human lens, including strong absorption in the UV-A spectral region, high photostability under both aerobic and anaerobic conditions, low yields of triplet state, fluorescence, and radicals under irradiation. Lenticular UV filters protect the retina and the lens from photo-induced damages and improve the visual acuity by reducing chromatic aberrations; therefore, the results obtained contribute to our understanding of the extremely high acuity of the raptor vision.

An online identification method for virtual sensing in ANC system

Active noise control (ANC) system generates anti-noise with the same amplitude and opposite phase to reduce unwanted acoustic noise based on acoustic wave superposition. In general, ANC can generate a zone of quiet (ZoQ) around the error microphone position. However, if the microphone cannot be placed at the desired position, the reduction effect may not be sufficient in the desired position. In such a case, we need to use the virtual sensing method to achieve noise reduction at the desired position without placing the physically microphone. In virtual sensing method, secondary path variation can significantly degrade performance, and secondary path modeling errors can cause the system to diverge. In this presentation, we introduce a new online identification method for auxiliary filter-based virtual sensing in ANC system. Virtual sensing with online identification requires re-estimation of not only the secondary path but also the auxiliary filters. The proposed online identification method has the capability of online identification of auxiliary filters in addition to the conventional online identification of secondary paths. We show that through computer simulations the noise reduction can be maintained by applying the proposed online identification method even when the secondary path varies.

Spectroelectrochemistry as a new tool for the quantification of UV filters in sun creams

This study focuses on the spectroelectrochemical quantification of four UV filters, butylmethoxy dibenzoylmethane (BM), benzophenone-3 (BP3), ethylhexyl methoxycinnamate (EM) and octocrylene (OC) used as sunscreens in cosmetics. Three of them exhibited electrochemical activity resulting in the modification of their absorption spectrum under the application of an oxidizing potential of +1.8 V vs. Ag. When working with a mixture containing both electroactive and nonelectroactive UV filters, UV–vis absorption spectra recorded before and after the application of the potential differed. The combination of spectral deconvolution of the spectra pair allowed a more accurate identification and quantification of UV filters than spectral deconvolution of the initial absorbance spectrum alone. This method was effective for the assessment of UV filters in model mixtures and commercial sun creams.

National innovation systems and dynamic impact of institutional structures on national innovation capability: A configurational approach with the OKID method

Understanding the national innovation capability involves the consideration of a variety of evolving institutional structures within the national innovation system (NIS). The variety of and interactions among institutional structures requires a systemic and dynamic approach to the NIS. In this respect, a configurational approach generates specific configurational conditions of the institutional structures, and offers a comprehensive and integrated approach to deal with the inherently complex and evolving nature of the NIS. In this research, using the Global Innovation Index (GII) data, we employ a fuzzy model with the Observer Kalman filter identification (OKID) method to develop configurational combinations. Our analysis provides a characterization of the impact of specific institutional structures on national innovation capability, while accounting for time and the innovation level of the countries. By explaining which specific institutional structures impact national innovation capability, this study contributes to the design of the innovation systems with a systemic approach and our understanding of which institutional structures to support innovation.

Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019

This paper presents a computational model based on interval type-2 fuzzy systems for analysis and forecasting of COVID-19 dynamic spreading behavior. The proposed methodology is related to interval type-2 fuzzy Kalman filters design from experimental data of daily deaths reports. Initially, a recursive spectral decomposition is performed on the experimental dataset to extract relevant unobservable components for parametric estimation of the interval type-2 fuzzy Kalman filter. The antecedent propositions of fuzzy rules are obtained by formulating a type-2 fuzzy clustering algorithm. The state space submodels and the interval Kalman gains in consequent propositions of fuzzy rules are recursively updated by a proposed interval type-2 fuzzy Observer/Kalman Filter Identification (OKID) algorithm, taking into account the unobservable components obtained by recursive spectral decomposition of epidemiological experimental data of COVID-19. For validation purposes, through a comparative analysis with relevant references of literature, the proposed methodology is evaluated from the adaptive tracking and forecasting of COVID-19 dynamic spreading behavior, in Brazil, with the better results for RMSE of 1.24×10−5, MAE of 2.62×10−6, R2 of 0.99976, and MAPE of 6.33×10−6.

System Identification of FIR Filters

System Identification of FIR Filters by Sudheesh Kannur Vasudeva Rao * , Kiran , Naveen Kumar , Mahadevaswamy Department of ECE, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India-570002 * Author to whom correspondence should be addressed. Journal of Engineering Research and Sciences, Volume 1, Issue 4, Page # 74-80, 2022; DOI: 10.55708/js0104010 Keywords: System Identification,

Identification of Triazine UV Filters as an Emerging Class of Abundant, Ubiquitous Pollutants in Indoor Dust and Air from South China: Call for More Concerns on Their Occurrence and Human Exposure.

Triazine UV filters are an important class of UV filters, but knowledge on their environmental occurrence and human exposure remains largely unknown. In this study, we performed a targeted analysis of 17 emerging triazine UV filters in indoor dust and indoor air from South China based on a newly developed LC-MS/MS method. A total of 12 of the 17 emerging triazine UV filters were first positively detected in the dust and air samples. Ethylhexyl triazone (EHT) and bis-ethylhexyloxyphenol methoxyphenyl triazine (BEMT) were identified as the most abundant compounds. The median total concentrations of triazine UV filters reached 3860 ng/g in indoor dust and 1590 pg/m3 in indoor air. Gas-particle partitioning analysis showed that triazine UV filters were predominant in the particle phase in ambient air. Significant concentration correlations were observed among most triazine UV filters. The estimated daily intake of triazine UV filters through dust ingestion and air inhalation for toddlers under high-end exposure scenarios was up to 839 ng/kg bw/day, but a lack of toxic thresholds hampers accurate risk assessment. Our work highlights another emerging class of UV filters that significantly contribute to indoor chemical mixtures and expresses concerns over their occurrence and human exposure.

General Approach to the Calibration of Innovative MFP Multichannel Digitizers

Innovative digitizers exploiting mixing, filtering, and processing (MFP) operations can grant ultrahigh bandwidth and sampling rate. Their operation combines analog processing stages with a digital signal processing stage, where massive operations are performed to obtain the digital representation of the input signal. In fact, the samples returned by the analog-to-digital converter (ADC) consist of a transformed, namely, mixed and filtered, version of the input, which shall be processed in the digital domain to reconstruct the input signal. The digital processing is also designed to attain streamline calibration, which provides both the removal of nonideal effects, such as mismatches between individual channels, and the improvement of the frequency response flatness. Calibration strategies represent an asset of manufacturers’ know-how since the performance of the digitizer largely depends on the effectiveness of the calibration process. Gain equalization and aliasing removal are performed in the digital domain by finite impulse response (FIR) filtering of the ADCs’ outputs. A general method for the identification of the calibration filters, and their translation into algorithms for MFP digitizers using two, four, or any number of channels, is proposed here. Functional- and circuit-level Cadence Virtuoso simulations in an STMicroelectronics Si–Ge heterojunction bipolar transistor (HBT) process are also carried out to evaluate the performance of the proposed method through a comparison between the digital representations of the signals obtained with and without calibration.

Kalman filter identification method for micro-vibration transfer paths of satellites

Micro-vibrations on-board a satellite have degrading effects on the performance of certain payloads like observation cameras. The major sources of vibrations include momentum wheels, solar array drives, other rotary mechanical equipment, etc. These vibrations result in loss of the pointing precision and image quality of the payload through intricate transfer paths. To improve the accuracy of a satellite system with many vibration sources and complex transfer paths, it is necessary to determine the main transfer path of vibration. In this study, a path identification method is proposed and applied to the transfer system from the momentum wheel to the camera mount. First, the observer/Kalman filter identification (OKID) algorithm is used to acquire the state-space equation of each path subsystem. Then, the subsystem order is obtained based on the slope of the singular entropy increment. In the next phase, combined with the measured disturbance force of the momentum wheel, the displacement response of the target point is predicted. Finally, the dominant transfer path of vibration is achieved by calculating the vibration contribution of each path to the response point. The results indicate that the dominant transfer path is the axial path of the horizontal momentum wheel, which contributes to the vibration of the camera mount at most. Effective vibration reduction measures should be taken to this path to suppress the vibration signal. In comparing the identified displacement response with the finite element response of the camera mount under different noise conditions, the correlation coefficients are >0.85, which proves the accuracy and anti-noise capability of the identification method.