System Identification Research Articles

Olivine-type germanate phosphors doped with Ln3+ (Ln = Eu, Dy) for solid-state lighting

Olivine-type germanate ceramics doped with Ln3+ ions (where Ln = Eu, Dy) as potential candidates to generate visible luminescence were studied. Analysis of microstructure and phase identification in systems obtained using the solid-state reaction method indicate that ceramics Li2ZnGeO4 and Li2MgGeO4 crystallize in a monoclinic crystal lattice, and samples consist of agglomerates that differ in size and shape. The spectroscopic properties of the ceramics align with the structure, and crucial parameters like the R/O or Y/B ratio and luminescence lifetime strongly depend on the type of ceramic samples. The luminescence spectra for germanate ceramics were recorded depending on the excitation wavelength and analyzed in detail. The Commission Internationale de l’Éclairage (CIE) chromaticity coordinates (x, y) were calculated from the emission spectra. It was observed that the color parameters change when the excitation wavelength and composition are modified. Our results indicate that olivine-type germanate ceramics doped with Eu3+ and Dy3+ ions exhibit red-orange and yellow-white emissions, respectively.

Design of a PID Control Scheme for Pound-Drever–Hall Frequency-Tracking System Based on System Identification and Fuzzy Control

Design of a PID Control Scheme for Pound-Drever–Hall Frequency-Tracking System Based on System Identification and Fuzzy Control

Automatic regularization for linear MMSE filters

In this work, we consider the problem of regularization in the design of minimum mean square error (MMSE) linear filters. Using the Bayesian approach, the regularization parameter is found from the observed signals via simple closed-form fixed-point iteration. The proposed automatic regularization approach is illustrated in system identification and beamforming examples, where the automatic regularization is shown to yield near-optimal results.

Implementation of multi-walled carbon nanotube incorporated GFRP as an alternative for CFRP in strengthening of concrete cylinders

Carbon fibre is the most widely utilized fibre for strengthening and retrofitting applications in the construction sector. The high cost of carbon fibre necessitates the identification of an alternative system for the retrofitting and repairing of concrete structures. In the current study, the characteristics of Glass Fibre Reinforced Polymer (GFRP), Carbon Fibre Reinforced Polymer (CFRP), and Multi-Walled Carbon Nanotube (MWCNT) incorporated GRFP are assessed with epoxy as a matrix for concrete cylinder confinement. The evaluation of one and two-layer CFRP confinement on concrete cylinders is carried out and outcomes are compared with the three-layer MWCNT incorporated GFRP confinement. A significant increase was observed in the axial compressive load-bearing capability of specimens with 1 wt percentage (wt%) MWCNT incorporated GFRP confined concrete cylinders. The axial load-carrying capability of concrete specimens with one layer of CFRP confinement was equivalent to that of specimens with 1 wt% MWCNT incorporated three-layer GFRP confinement. The axial strain of MWCNT incorporated three-layer GFRP confined specimens was 75 % more than one-layer and 12 % higher than two-layer CFRP confined specimens. According to observations, MWCNT-incorporated FRP confinement improves the energy absorption and ductility index. Ultrasonic pulse velocity test results confirmed the potency of three-layer GFRP confinement as a replacement for one and two layers of CFRP. A cost analysis is also carried out to validate the economic viability of the MWCNT-based GFRP compared to CFRP confinement.

Unsupervised data-driven response regime exploration and identification for dynamical systems.

Data-Driven Response Regime Exploration and Identification (DR2EI) is a novel and fully data-driven method for identifying and classifying response regimes of a dynamical system without requiring human intervention. This approach is a valuable tool for exploring and discovering response regimes in complex dynamical systems, especially when the governing equations and the number of distinct response regimes are unknown, and the system is expensive to sample. Additionally, the method is useful for order reduction, as it can be used to identify the most dominant response regimes of a given dynamical system. DR2EI utilizes unsupervised learning algorithms to transform the system's response into an embedding space that facilitates regime classification. An active sequential sampling approach based on Gaussian Process Regression is used to efficiently sample the parameter space, quantify uncertainty, and provide optimal trade-offs between exploration and exploitation. The performance of the DR2EI method was evaluated by analyzing three established dynamical systems: the mathematical pendulum, the Lorenz system, and the Duffing oscillator, and its robustness to noise was validated across a range of noise magnitudes. The method was shown to effectively identify a variety of response regimes with both similar and distinct topological features and frequency content, demonstrating its versatility in capturing a wide range of behaviors. While it may not be possible to guarantee that all possible regimes will be identified, the method provides an automated and efficient means for exploring the parameter space of a dynamical system and identifying its underlying "sufficiently dominant" response regimes without prior knowledge of the system's equations or behavior.

Analysis of an Electronic Heating Device: System Modeling, Identification and Control

Analysis of an Electronic Heating Device: System Modeling, Identification and Control

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.

Parameter estimation in solar power plant systems: a comparative study of recursive and iterative techniques

The development of a dynamic model for a popular implemented solar power plant is a critical task for power engineers aiming to enhance the plant’s performance and reliability. In this study, we utilized the prediction error method (PEM), a robust algorithm for system identification, to capture the plant’s operational characteristics with precision. Additionally, we employed both recursive and hierarchical algorithms to identify the system parameters effectively. The results from each identification method are rigorously quantified and analyzed, allowing for a detailed comparison of their performance. This comprehensive evaluation not only highlights the strengths and weaknesses of each approach, but also provides valuable insights into their practical applications in the context of solar power systems.

Effects of noise intensity on early warning indicators of thermoacoustic instability: An experimental investigation on a lean-premixed combustion system

In this work, we experimentally investigate the noise-induced dynamics of a lean premixed combustion system operating on natural gas–air mixtures that exhibit thermoacoustic instability via a subcritical Hopf bifurcation. The investigation is done before the bistable region with equivalence ratio (ϕ) as the control parameter. We analyze the acoustic pressure oscillations (p′) in the combustor and fluctuations in the heat release rate (q′) from the laminar quasi-flat flame at increasing levels of white noise. We show the effects of noise intensity on the reliability of various types of early warning indicators (EWIs) to predict the onset of the impending thermoacoustic oscillations. We investigate the indicators based on statistical measures (variance, skewness, and kurtosis), autocorrelation and spectral properties (coherence factor), system identification (growth/decay rates of p′), multi-fractality (Hurst exponent), and time series complexity (permutation entropy and Jensen–Shannon complexity). The coherence factor, variance, and decay rates of p′ always increases as the system approaches thermoacoustic instability, indicating their robustness as an EWI under most noise levels. An increase in kurtosis cannot be employed as an EWI. Implementing autocorrelation, skewness, Hurst exponent, permutation entropy and Jensen–Shannon complexity as effective EWIs has limitations: they can be estimated accurately only from pressure oscillations (p′) data and work only above a particular threshold value of noise intensity. Our results have direct implication on early prediction and control of thermoacoustic instability in practical gas turbine combustors.Novelty and significance statementDeveloping effective early warning indicators (EWIs) to anticipate the onset of thermoacoustic instability is crucial for preventing potential damage and ensuring the reliable operation of lean premixed gas turbine combustion systems. In such systems, inherent noise dynamics undergo variations with changing operating conditions and combustor designs. Specifically, noise intensity increases as the system becomes more turbulent. In this study, we demonstrate that the inherent noise dynamics in a lean premixed combustion system play a crucial role in influencing the trends observed in early warning indicators of thermoacoustic instability. We address several key questions, including (a) whether comparative reliability assessments of different classes of EWIs exist, (b) the effect of variations in noise properties on the efficacy of EWIs, and (c) which EWIs are most reliable considering that noise characteristics depend on the system and may even change with operating conditions within a specific system. This information is critical for engine designers/users, facilitating the development of robust and effective monitoring systems for gas turbine combustion systems.

Shape function method of Tikhonov regularization for vertical bending moment identification based on parameter updating

To accurate identifying vertical bending moment and structural parameters for hull structure, a simultaneous identification technique combined with shape function method (SFM) and sensitivity method was developed. The shape function construction incorporated Tikhonov regularization to enhance the stability of SFM (SFM_Tikhonov). Numerical and experimental studies were conducted. Numerical results showed the effectiveness of SFM_Tikhonov. It achieved a reduction of over 30% in both root mean square error (RMSE) and mean absolute error (MAE) compared to SFM of moving least square fitting (SFM_MLSF). The simultaneous identification method effectively improves the accuracy of moment identification in scenarios of wrong initial parameter estimates. Experimental results show that under constant load condition, the performance of the load identification module based on SFM_Tikhonov lags behind numerical examples, although its parameter update module is unaffected. However, the proposed method remains showing its potential for advancing load identification in structural health monitoring systems.

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.

Comparative Metabolomic Analysis and Antinociceptive Effect of Methanolic Extracts from Salvia cinnabarina, Salvia lavanduloides and Salvia longispicata.

Mexico is considered one of the countries with the greatest diversity of the Salvia genus. A significant percentage of its species are known for their use in traditional medicine, highlighting their use as an analgesic. The objective of this work was to determine the chemical composition of the methanolic extracts of S. cinnabarina, S. lavanduloides and S. longispicata through untargeted metabolomics, as well as the in vivo evaluation of the antinociceptive effect and acute oral toxicity. The chemical profiling was performed using ultra-high performance liquid chromatography coupled with a high-resolution mass spectrometry (UPLC-ESI+/--MS-QTOF) system and tentative identifications were performed using a compendium of information on compounds previously isolated from Mexican species of the genus. Pharmacological evaluation was carried out using the formalin test and OECD guidelines. The analysis of the spectrometric features of the mass/charge ratios of the three salvias shows that a low percentage of similarity is shared between them. Likewise, the putative identification allowed the annotation of 46 compounds, mainly of diterpene and phenolic nature, with only four compounds shared between the three species. Additionally, the extracts of the three salvias produced a significant antinociceptive effect at a dose of 300 mg/kg administered orally and did not present an acute oral toxicity effect at the maximum dose tested, indicating a parameter of LD50 > 2000 mg/kg. The exploration of the chemical profile of the three salvias by untargeted metabolomics shows that, despite being species with antinociceptive potential, they have different chemical profiles and therefore different active metabolites.

Source identification of water distribution system contamination based on simulated annealing-particle swarm optimization algorithm.

Ensuring the safety of water supplies is critical for urban areas requires rapid response when water quality anomalies are detected in the pipeline network. Prompt action is essential to prevent widespread contamination, protect public health, and mitigate potential social unrest. The particle swarm optimization (PSO) algorithm has faced challenges for contamination source identification (CSI) in water distribution systems (WDS), primarily due to its susceptibility to locally optimal solutions. Addressing this issue is critical to quickly and accurately identify contamination sources. Therefore, this research integrates the Metropolis criterion from the simulated annealing (SA) algorithm into a SA-PSO algorithm, to overcome the limitations of PSO. This study conducts contamination localization experiments using SA-PSO, with the publicly available NET-3 pipeline network as the case to generate sudden contamination events. By collecting pollutant concentration data from predefined monitoring points over time through simulation, a simulation-optimization inverse location model is constructed to fit the pollutant concentrations at each monitoring point. The results of the case study show that SA-PSO outperforms PSO in both speed and accuracy in solving the CSI problem, and the findings provide an efficient and effective contamination localization tool for urban water supply management.

Performance analysis of position sensorless control of antagonistic shape memory alloy actuators

Abstract This paper presents a comprehensive performance analysis of position sensorless control of antagonistic shape memory alloy actuators (ASMAA), focusing on enhancing the sensorless position sensing capabilities and servo performance. The mechanisms and influencing factors of ASMAA self-sensing characteristics are comprehensively analyzed. A self-sensing model framework based on the constitutive model of ASMAA is proposed, and a self-sensing model based on differential resistance feedback is developed. An experimental platform based on the ASMAA motion mechanism is established to test and analyze SMA wires of two different materials under various pretension force conditions. The experimental results show that the antagonistic configuration mitigates hysteresis and improves linearity. A simple polynomial fitting is employed to establish the resistance-displacement relationship, achieving good accuracy with minimal residuals. The system identification is employed to avoid the parameter complexity and time-varying in the constitutive model. Based on the identified transfer function, a traditional PID controller is designed for position sensorless servo control. However, the inherent nonlinear hysteresis of ASMAA is difficult to suppress using a linear PID controller. Furthermore, a compound control strategy based on the Duhem model and PID controller is proposed, which utilizes the particle swarm optimization (PSO) algorithm to identify the Duhem inverse model controller. Experimental results demonstrate that the compound controller significantly enhances position tracking accuracy and response speed compared to a standalone PID controller.

Optimization control of time-varying cyber–physical systems via dynamic-triggered strategies

A novel approach is proposed for designing control strategies for time-varying cyber–physical systems (CPSs) with unknown dynamics, eliminating the need for system identification. Combining with the dynamic-triggered strategies (DTSs), the closed-loop system is parameterized using matrices that are depended on data obtained from a collection of input-state trajectories gathered offline. Additionally, the problem of data-driven optimization control is elegantly resolved through the utilization of classical linear quadratic regulator (LQR) technology, showcasing a remarkable innovation by obviating the necessity for the specific mathematical model of CPSs proposed in this paper. A numerical illustration is provided to illustrate these findings.

MEEF criterion-based spline adaptive filtering algorithm and its application

This paper presents an innovative the minimum error entropy with fiducial points (MEEF)-based spline adaptive filtering (S-AF) algorithm, called SAF-MEEF algorithm, which outperforms the conventional SAF algorithms that use the mean square error (MSE) criterion in reducing non-Gaussian interference. To overcome the limitation of the fixed step-size, a variable step-size strategy is also developed, resulting in the SAF-VMEEF algorithm, which improves the convergence speed and steady-state error performance. Furthermore, the computational complexity and convergence analysis of the SAF-MEEF are discussed. Nonlinear system identification simulations test the performance of the presented algorithms. Furthermore, this article accomplishes the application of nonlinear active noise control (ANC). Their effectiveness and robustness against non-Gaussian noise are demonstrated in different experimental scenarios, including α-stable noise, real-world functional magnetic resonance imaging (fMRI) noise, and real-life server room (SR) noise.

In-orbit system identification of a flexible satellite with variable mass using dual Unscented Kalman filters

Modern space mission concepts are increasingly dependent on the robust and reliable deployment of spacecraft with large appendages, such as antennas, booms or solar panels. Such deployment requires the ability to properly capture and control the coupled system dynamics, which requires accurate in-orbit system identification of the mass and structural properties. This paper utilises dual Unscented Kalman filters (DUKF) to develop an online system identification strategy that captures both the structural and mass properties, and the attitude and orbit dynamics. The dynamics of the flexible multibody problem are derived from the Lagrangian equations, with the flexible body characteristics modelled with finite element software. A genetic algorithm is used to optimise the accelerometer placement, and hence improve the DUKF performance. We demonstrate that this approach can accurately capture the coupled attitude, orbit, and structural dynamics, as well as being able to provide in-orbit updates for mass properties such as the moment of inertia. The methodology is explored for two illustrative cases: one in which the initial moment of inertia is incorrectly characterised, one in which the moment of inertia changes with time. In both cases, the DUKF approach captures both the system dynamics and the mass properties, which are captured with an error of less than 1%.

Auxiliary model maximum likelihood least squares-based iterative algorithm for multivariable autoregressive output-error autoregressive moving average systems

Identification of multivariable systems is of great significance to control systems. This paper focuses on the parameter identification problems for multivariable autoregressive output-error autoregressive moving average (M-AROEARMA) systems. On the basis of the decomposition strategy, the M-AROEARMA model is de- composed into multiple subsystem models. By means of the auxiliary model idea, the auxiliary model least squares-based iterative algorithm is derived. For the purpose of achieving highly accurate parameter identification performance under colored noises interference, an auxiliary model maximum likelihood least squares-based iterative algorithm is proposed by utilizing the maximum likelihood principle. The numerical simulation example demonstrates the effectiveness of the proposed algorithms.

Using STPA for hazard identification and comparison of hybrid power and propulsion systems at an early design stage

The decarbonization of the shipping industry is a hot topic and new emission reduction goals, regulations, and initiatives all over the world underline its importance. These goals apply to the ocean going fleet, as well as coastal shipping and fisheries. Both academia and industry, work on the development and testing of new technologies to reach emission reduction targets. Besides technical feasibility and economic viability, the safety of new power and propulsion systems must be considered in this process. In this paper, the Systems Theoretic Process Analysis (STPA) method is used for hazard identification and comparison of two alternative hybrid power and propulsion systems for a small-scall fishing vessel. The assessment method allows for identifying focus areas for hazard reduction in a systematic manner. System safety requirements derived from the assessment can be considered when designing power and propulsion systems. Based on the results, a discussion of the method and the usefulness of the results is given. The main findings are that the assessment provides valuable insights into the hazards introduced by new power and propulsion systems in a qualitative manner. For a quantification of the risk, further work and an extension of the assessment are needed. The STPA results show that especially the control of the power sources and their interactions internally and externally with the consumer side are crucial. Therefore, special attention should be given to the design of the automated control system, feedback system, and user interaction with the system.

Multi-objective decoupling control of thermal management system for PEM fuel cell

Operating temperature is an important factor that affects the efficiency, durability, and safety of proton exchange membrane fuel cells (PEMFC). Thus, a thermal management system is necessary for controlling the appropriate temperature. In this paper, a novel thermal management system based on two-stage utilization of cooling air is first established, whose core characteristic is utilizing the temperature difference between the cooling air leaving the main radiator and the auxiliary radiator. The novel thermal management system can reduce the parasitic power of the fan by 59.27 % and improve the temperature control effect to a certain extent. The traditional feedforward decoupling control based on system identification is first adopted to control the temperature and surpasses dual-PID on all the 5 indexes, which are Integral Absolute Error Criterion (IAE) of temperature difference, IAE of inlet coolant temperature, parasitic power of fan, average overshoot of temperature difference and average overshoot of inlet coolant temperature. The multi-objective decoupling control based on multi-objective optimization is then proposed to further improve the temperature control effect on the basis of traditional feedforward decoupling control. The above 5 indexes are chosen as the optimization objectives, the decoupling coefficients are chosen as the decision variables, and the Pareto set is obtained by NSGAⅡ and NSGAⅢ. The results show that the proposed multi-objective decoupling control has the main advantages as follows: (1) It can provide comprehensive optimization options for different design preferences; (2) It can significantly optimize a certain objective while other objectives are not too extreme; (3) It has the ability to surpass traditional feedforward decoupling control on all the 5 indexes; (4) It does not rely on the system identification.