System Identification Research Articles

Optimal strategy of data tampering attacks for FIR system identification with average entropy and binary‐valued observations

SummaryIn the era of digitalization boom, cyber‐physical system (CPS) has been widely used in several fields. However, malicious data tampering in communication networks may lead to degradation of the state estimation performance, which may affect the control decision and cause significant losses. In this paper, for the identification of finite impluse response (FIR) systems with binary‐valued observations under data tampering attack, an optimal attack strategy based on the average entropy is designed from the perspective of the attacker. In the case of unknown parameters, the regression matrix is used to give the estimation method of the system parameters, the algorithmic flow of the data tampering attack for the implementation of the on‐line attack is designed. Finally, the effectiveness of the algorithm and the reliability of the conclusions is verified through the examples.

Impact of H[formula omitted]-enrichment on the response of a partially premixed CH[formula omitted]–air flame to velocity and equivalence ratio fluctuations

The dynamics of hydrogen-enriched partially premixed methane–air flames are investigated, focusing on the frequency response to velocity and equivalence ratio fluctuations. Large-eddy simulations (LES) with conjugate heat transfer are performed on the partially premixed PRECCINSTA combustor for various methane–hydrogen mixtures, maintaining a constant thermal power and global equivalence ratio of 10 kW and 0.8, respectively. To reduce the computational cost of the LES, a three-step global mechanism is calibrated for each hydrogen-enriched partially premixed flame. The accuracy of the LES is assessed against experimental data, showing a good agreement with the mean velocity field and shape of the swirl-stabilized flame. The multiple-input–single-output structure for the flame transfer function is applied to separate the individual contributions of velocity and equivalence ratio fluctuations to the overall heat release rate oscillation in the partially premixed flames. System identification with broadband forcing is used to compute the various flame transfer functions. Hydrogen enrichment significantly influences the flame response to equivalence ratio fluctuations, explained by the respective impact on the response of the heat of reaction, the flame speed, and the flame surface area to equivalence ratio fluctuations. The interaction between velocity and equivalence ratio fluctuations is further characterized by the mixing transfer function, which describes the equivalence ratio fluctuations reaching the flame. The higher mass diffusivity of hydrogen results in lower equivalence ratio fluctuations reaching the flame as the frequency increases, attenuating the pronounced effect of hydrogen on the flame sensitivity to equivalence ratio fluctuations. This study contributes to the understanding of the impact of hydrogen enrichment on the thermoacoustic response of partially premixed flames.

Research on an Active Adjustment Mechanism Based on Non-Singular Terminal Sliding Mode and Finite-Time Disturbance Observer

With the continuous development of synchrotron radiation light sources, higher requirements have been put forward for the stability of double-crystal monochromators in synchrotron radiation facilities. This paper designs an active adjustment mechanism for a double-crystal monochromator to improve its stability. Firstly, three spatial degrees of freedom are designed based on the active adjustment mechanism of flexible leaf spring parallel coupling, and the prototype of the mechanism is fabricated. Secondly, system identification experiments are carried out and the system transfer function curve is fitted by the nonlinear least squares method. Thirdly, the controller based on non-singular terminal sliding modes and a finite-time disturbance observer was designed for stability control and disturbance compensation. Finally, the effectiveness of the controller is verified by a model-in-the-loop approach based on the performance of the real-time target machine. The results show that the non-singular terminal sliding mode + finite-time disturbance observer control strategy can reduce the RMS value of the vibration displacement of Axis-1/Axis-2/Axis-3 by 81.25%, 78.53%, and 71.82%.

Modeling, system identification, and friction compensation of an actuator system for heave and surge wave energy converter experiments

Modeling, system identification, and friction compensation of an actuator system for heave and surge wave energy converter experiments

Improvement of System Identification of Stochastic Systems via Koopman Generator and Locally Weighted Expectation

Improvement of System Identification of Stochastic Systems via Koopman Generator and Locally Weighted Expectation

System-level operational cyber risks identification in industrial control systems

ABSTRACT In Industrial Control Systems (ICS), where complex interdependencies abound, cyber incidents can have far-reaching consequences. Dependency modelling, a valuable technique for assessing cyber risks, aims to decipher relationships among variables. However, its effectiveness is often hampered by limited data exposure, hindering the analysis of direct and indirect impacts. We present a unique method that transforms dependency modelling data into a Bayesian Network (BN) structure and leverages causality and reasoning to extract inferences from seemingly unrelated events. Using operational ICS data, we confirm our method enables stakeholders to make better decisions about system security, stability, and reliability.

Modal analysis of non-conservative systems with friction-induced strong nonlinear damping by using response-controlled testing

In recent years, the response-controlled stepped-sine testing (RCT) method has made important contributions to the evolution of the single nonlinear mode (SNM) method from a postulate to a well-established theory with the accurate modal identification of various nonlinear mechanical systems ranging from simple benchmark beams to real missiles. Since the RCT method resides on the SNM theory which is based on Rosenberg’s periodic motion definition of nonlinear modes, it has been regarded as limited to weakly damped mechanical systems that mainly exhibit strong conservative nonlinearities. An important contribution of this current paper is extending the RCT method to non-conservative mechanical systems with complex nonlinear modes. The extension is first numerically validated on a strongly friction-damped cantilever beam and then experimentally validated on the actuation mechanism of a real control fin that exhibits strong softening-stiffening nonlinearity (up to 50% shift of natural frequency) due to friction/backlash and very high and nonlinear damping (up to 30% modal damping ratio) due to friction. Another important contribution of this paper is to demonstrate the applicability of the superposition principle in nonlinear dissipative systems with closely spaced modes at an acceptable accuracy if the nonlinear modes are similar in shape to their corresponding linear modes and the modal coupling terms in the equation of motion can be ignored. The assumption of similar nonlinear modes is reasonable for most practical applications that exhibit dissipative nonlinearities due to mechanical joints. Although the assumption of ignorable coupling terms does not reside on a solid theoretical framework, numerical studies on a friction-damped lumped system with closely spaced modes give promising results, pointing to the significant potential of the modal superposition in nonlinear systems.

Dispersed-sparsity-aware LMS algorithm for scattering-sparse system identification

We develop a least-mean-squares (LMS) algorithm to identify scattering-sparse systems, where the few significantly large coefficients of the unknown impulse response are dispersed along the full length, instead of grouped into clusters. Many wireless communication channels such as the underwater acoustic channels, cellular communication channels and aviation channels are typical scattering-sparse systems. In the proposed strategy, the difference between the ℓ1-norm and ℓ∞,1-norm of the uniformly-divided tap-weight vector of the adaptive filter is utilized as a penalty and is introduced into the mean-square-error cost function, where the ℓ∞,1-norm of the tap-weight vector is used to locate the unknown large coefficients in view of the characteristics of dispersed sparsity. A dispersed-sparsity-aware LMS (DS-LMS) algorithm is then proposed by following the stochastic subgradient method. We study the mean and mean-square behaviors of the proposed algorithm. We also provide theoretical guidelines for the parameter settings that ensure that the proposed DS-LMS algorithm converges to a lower mean-square-deviation (MSD) level than the traditional LMS algorithm. Simulation results verify the effectiveness of the proposed DS-LMS algorithm, and correctness of the theoretical findings.

Linear modeling techniques for liquid height regulation in two tank - Two variable system

In this paper, two linear modeling techniques are presented for regulating the liquid height in two tank - two variable system (TT-TVS). Initially, the nonlinear dynamics of TT-TVS are linearized using the Taylor series linearization technique. However, this method reveals that some parameters of TT-TVS are inexact, necessitating the adoption of system identification techniques or mathematical approaches to accurately identify the linear dynamics. To address this, two new approaches are proposed: (i) a mathematical approach utilizing real-time input-output data, and (ii) a Linear Variable Parameter Transfer Function (LVPTF) model identification approach, which employs real-time data and MATLAB curve fitting tool. A comparative analysis between the proposed identification techniques and existing methods from the literature is also presented. The results indicate that the LVPTF modeling technique offers superior accuracy in identifying the linear dynamics of TT-TVS.

Hermite polynomial based affine projection Blake Zisserman algorithm for identification of robust sparse nonlinear system

Hermite polynomial based affine projection Blake Zisserman algorithm for identification of robust sparse nonlinear system

Raw Materials and Lithic Production During the Early Magdalenian in Cantabrian Spain: Cova Rosa (Ribadesella, Asturias)

The lithic assemblage studied here comes from Cova Rosa, one of the main Upper Palaeolithic sites in Asturias (northern Spain). The remains were found in Layer B5, which was excavated by F. Jordá Cerdá and A. Gómez Fuentes in 1978 and are associated with an osseous assemblage and archaeozoological remains. This occupation has been dated by radiocarbon to about 16,400 BP (ca. 19.8–19.6 ka cal BP), corresponding to Archaic/Lower Magdalenian. The present study combines the determination of the raw materials and the identification of the production systems to achieve an understanding of the management of lithic resources by hunter-gatherer groups in the Late Pleistocene. The sourcing of mostly local materials (mainly Piloña flint), the wide variety of rock types of diverse provenances (up to 10 types), and the presence of well-represented lithological tracers (Flysch, Chalosse) turn Cova Rosa into an important case for studying different lithic raw material procurement models. The predominance of microlaminar production and the variability in the exploitation strategies used to obtain backed tools, as well as the poor standardisation of flake production, follow the dynamics observed in other occupations of similar chronology in a wide geographical area that includes Cantabrian Spain and south-west France.

Deep Embedding Koopman Neural Operator-Based Nonlinear Flight Training Trajectory Prediction Approach

Accurate flight training trajectory prediction is a key task in automatic flight maneuver evaluation and flight operations quality assurance (FOQA), which is crucial for pilot training and aviation safety management. The task is extremely challenging due to the nonlinear chaos of trajectories, the unconstrained airspace maps, and the randomization of driving patterns. In this work, a deep learning model based on data-driven modern koopman operator theory and dynamical system identification is proposed. The model does not require the manual selection of dictionaries and can automatically generate augmentation functions to achieve nonlinear trajectory space mapping. The model combines stacked neural networks to create a scalable depth approximator for approximating the finite-dimensional Koopman operator. In addition, the model uses finite-dimensional operator evolution to achieve end-to-end adaptive prediction. In particular, the model can gain some physical interpretability through operator visualization and generative dictionary functions, which can be used for downstream pattern recognition and anomaly detection tasks. Experiments show that the model performs well, particularly on flight training trajectory datasets.

A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions

Gear monitoring and fault diagnosis are vital for preventing accidents and minimizing economic losses in transportation and industrial systems. Traditional methods use vibration sensors and a two-stage analysis approach: preprocessing data to remove noise and extract relevant components, and generating a condition indicator to detect behavioral anomalies in the gears over time.Time synchronous averaging is a notable tool for monitoring gears at constant speeds. Such a tool filters sensor signals and extracts rotation-related components by using statistical measurements as condition indicators. However, it has limitations in scenarios with time-varying sampling rates and fluctuating speeds, where statistical measures may not fully capture changes in system parameters.This article proposes a novel methodology for monitoring gears in multivariate rotordynamical systems under fluctuating speed conditions. The method integrates time synchronous averaging, system identification algorithms, and statistical tools. It generates a time-synchronous average signal considering speed fluctuations, computes a state–space model of gear behavior in healthy states, extracts residual data from a data-driven model, and generates a condition indicator based on the Cauchy–Schwarz divergence.The proposed methodology was evaluated using experimental data from three rotor dynamical setups under different operational conditions. Validation showed its effectiveness, especially under high-load conditions with significant speed fluctuations.

Linear Motor System Identification and Simulation Experiments Based on LabVIEW

Linear Motor System Identification and Simulation Experiments Based on LabVIEW

Deep Model-Based Reinforcement Learning for Predictive Control of Robotic Systems with Dense and Sparse Rewards

Sparse rewards and sample efficiency are open areas of research in the field of reinforcement learning. These problems are especially important when considering applications of reinforcement learning to robotics and other cyber-physical systems. This is so because in these domains many tasks are goal-based and naturally expressed with binary successes and failures, action spaces are large and continuous, and real interactions with the environment are limited. In this work, we propose Deep Value-and-Predictive-Model Control (DVPMC), a model-based predictive reinforcement learning algorithm for continuous control that uses system identification, value function approximation and sampling-based optimization to select actions. The algorithm is evaluated on a dense reward and a sparse reward task. We show that it can match the performance of a predictive control approach to the dense reward problem, and outperforms model-free and model-based learning algorithms on the sparse reward task on the metrics of sample efficiency and performance. We verify the performance of an agent trained in simulation using DVPMC on a real robot playing the reach-avoid game. Video of the experiment can be found here: https://youtu.be/0Q274kcfn4c.

Navigating Digital Challenges for SMEs: A Two-Tier Approach to Risks Mitigation and Sustainability

The global significance of SMEs has traditionally been recognized as a key driver of economic growth and sustainability. The emergence of digital technologies and Information and Communication Technology for Development (ICT4D) holds significant potential to further enhance this impact. However, SMEs in developing countries have faced challenges in adopting sustainable, resource-intensive digital systems. Factors such as limited skills, financial constraints, and the alignment of suitable solutions hinder this adoption. To address these challenges and promote sustainable digital transformation, this study proposes a two-tier approach. The first prong employs Natural Language Processing (NLP) techniques, including Word2Vec, for global analysis and digital systems identification. The second prong involves a country-specific analysis of SMEs’ digital requirements. This two-tier analysis aims to uncover the actual digital needs of SMEs while shedding light on high-intensity global SME activities that, if integrated through ICT4D, could effectively address the risks and challenges SMEs face in adopting, implementing, and maintaining digital systems. In addition, the study develops systems required by SMEs to optimize their business processes and production, thereby promoting their growth and sustainability in the digital era. The results of this study demonstrate the effectiveness of these proposed methods in addressing digital challenges for SMEs and fostering sustainable development.

Model Validation and Real-Time Process Control of a Continuous Flow Ohmic Heater

Ohmic heating is a highly efficient method for rapid fluid heating, with applications in fields such as food processing, pharmaceuticals, and chemical engineering. Prior to its industrial application, thorough analysis and modeling are crucial to ensure safe and efficient operations. Therefore, this research focuses on the development and validation of a transfer function-based model for a continuous flow ohmic heater (CFOH). Validation metrics include root mean square error (RMSE) and mean absolute percentage error (MAPE). The developed model achieves an RMSE of ±1.48 and a MAPE of ±2.58% compared to experimental results, demonstrating its accuracy. Furthermore, the research presents the implementation of robust real-time applications of advanced process controllers, including PID, MPC, and AMPC. These controllers were first simulated using the developed model and subsequently deployed in the pilot plant ohmic heater system to achieve precise temperature control and optimised input voltage. The reliability of this procedure was affirmed through a comparison between simulated results and empirical data obtained from the CFOH pilot plant. The study concludes by suggesting potential applications in fault diagnosis, educational training, system identification, and controller design.

Artificial intelligence facilitates clinical management of epithelial dysplasia in multiple organs

Epithelial dysplasia is a condition characterized by a spectrum of architectural and cytological alterations to the epithelium, resulting from the accumulation of genetic alterations. It is associated with an increased risk of cancer progression in a variety of organs. However, the variability of different grading systems, as well as inter- and intra-examiner variability, gives rise to concerns regarding the reliability of the results. Histopathology represents the gold standard for the diagnosis of epithelial dysplasia. The combination of big data in pathology and artificial intelligence (AI) will facilitate the achievement of accurate diagnoses and treatments, providing objective and efficient methods to integrate and refine diverse morphological, molecular, and multi-omics information. This perspective provides a summary of the existing research and prospects for the application of AI to epithelial dysplasia in multiple organs. A number of studies have been conducted with the aim of developing a grading system and prognostic identification method for epithelial dysplasia in the oral cavity, larynx, esophagus, and stomach. Digital pathology-based AI may prove useful in facilitating the clinical management of epithelial dysplasia in multiple organs. In summary, digital pathology images obtained by scanning hematoxylin & eosin-stained slides, identifying image features, and building AI models using deep learning combined with machine learning algorithms, validated with real-world data from multicenter cohorts could provide AI as a promising clinical application in the future.

An LSTM-enhanced surrogate model to simulate the dynamics of particle-laden fluid systems

The numerical treatment of fluid–particle systems is a very challenging problem because of the complex coupling phenomena occurring between the two phases. Although an accurate mathematical modelling is available to address this kind of applications, the computational cost of the numerical simulations is very expensive. The use of the most modern high performance computing infrastructures could help to mitigate such an issue but not completely to fix it. In this work we develop a non intrusive data-driven reduced order model (ROM) for Computational Fluid Dynamics (CFD) - Discrete Element Method (DEM) simulations. The ROM is built using the proper orthogonal decomposition (POD) for the computation of the reduced basis space and the Long Short-Term Memory (LSTM) network for the computation of the reduced coefficients. We are interested to deal both with system identification and prediction. The most relevant novelties rely on (i) a filtering procedure of the full order snapshots to reduce the dimensionality of the reduced problem and (ii) a preliminary treatment of the particle phase. The accuracy of our ROM approach is assessed against the classic Goldschmidt fluidized bed benchmark problem. Finally, we also provide some insights about the efficiency of our ROM approach.

The filtering-based recursive least squares identification and convergence analysis for nonlinear feedback control systems with coloured noises

The coloured noise is ubiquitous in industrial processes. This paper addresses the identification problem for the nonlinear feedback systems with coloured noise. Firstly, a direct identification scheme based on the least squares principle is developed to estimate the whole parameters of the nonlinear feedback systems and the convergence analysis is carried out through the stochastic stability theory. Secondly, for the purpose of improving the estimation accuracy, a filtering-based identification framework is proposed by constructing a linear filter for filtering the input data, output data and the coloured noise, and the coloured noise is transformed into a white noise. This identification scheme based on the filtering technique can effectively reduce the adverse effects caused by coloured noise and parameter estimation accuracy is enhanced compared with the direct least squares algorithm. Meanwhile, the convergence analysis of the filtering-based identification algorithm is given to provide a theoretical analysis. Finally, the simulation example is carried out by performance test and comparison analysis and simulation results show the effectiveness of the proposed identification methods.