System Identification Techniques Research Articles

Continuous-time model identification of the subglottal system

Mathematical models that accurately simulate the physiological systems of the human body serve as cornerstone instruments for advancing medical science and facilitating innovative clinical interventions. One application is the modeling of the subglottal tract and neck skin properties for its use in the ambulatory assessment of vocal function, by enabling non-invasive monitoring of glottal airflow via a neck surface accelerometer. For the technique to be effective, the development of an accurate building block model for the subglottal tract is required. Such a model is expected to utilize glottal volume velocity as the input parameter and yield neck skin acceleration as the corresponding output. In contrast to preceding efforts that employed frequency-domain methods, the present paper leverages system identification techniques to derive a parsimonious continuous-time model of the subglottal tract using time-domain data samples. Additionally, an examination of the model order is conducted through the application of various information criteria. Once a low-order model is successfully fitted, an inverse filter based on a Kalman smoother is utilized for the estimation of glottal volume velocity and related aerodynamic metrics, thereby constituting the most efficient execution of these estimates thus far. Anticipated reductions in computational time and complexity due to the lower order of the subglottal model hold particular relevance for real-time monitoring. Simultaneously, the methodology proves efficient in generating a spectrum of aerodynamic features essential for ambulatory vocal function assessment.

Scenario based optimisation over uncertain system identification models

Models of dynamical systems are often built from observed data using system identification techniques, and the uncertainty in the model parameters is commonly described using confidence regions to which the true model parameters belong with a certain probability. In this paper we propose methods which use the scenario approach to solve robust optimisation problems for model based design where there is uncertainty in the model parameters. The methods draw samples (scenarios) of the uncertain model parameters and solve an optimisation problem only involving the drawn scenarios. Both a Bayesian system identification setting where the model parameters are stochastic, and a non-Bayesian system identification setting where the model parameters are treated as being deterministic are considered. The Bayesian and non-Bayesian settings require different treatment, and whereas standard scenario optimisation theory can be directly utilised in the Bayesian setting, this is not the case in the non-Bayesian setting. For that reason the model class is restricted to linear regression models with deterministic regressors in the non-Bayesian case. Algorithms are proposed for drawing samples of the parameters such that the uncertainties in the system identification models are reflected in the samples. Theoretical results are given bounding the probability that the found solution to the scenario optimisation problem will give a worse performance when applied to the true system than seen on the drawn scenarios. The methods are illustrated in simulation examples showing good performance.

Intelligent Structure Identification and Robust Control Implementation

This study outlines a comprehensive strategy for designing and implementing robust controllers tailored for intelligent structures. This study presents a robust control-based structural identification technique that uses the input/output data of the system to construct a state-space mode and frequency domain. To reduce vibrations, a robust controller is created using the control Simulink model. The identification and robust control of smart structures using Simulink involve a combination of system identification techniques and control design within the MATLAB Simulink environment. The key challenge is dealing with uncertainties and variations in system dynamics. Robust control methods have been employed to suppress the vibrations during dynamic disturbances. These methods are important for mechanical systems operating under stochastic loading conditions.

Feedforward decoupling vibration control of an unbalanced maglev rotor subjected to base excitation

ABSTRACT The maglev rotor installed on a moving base is subjected to both the unbalanced force of the rotor itself and the base excitation caused by external interferences. The interaction between the maglev rotor and the moving base gives rise to a combination of challenges. Specifically, the base excitation can propagate to the rotor, inducing vibration, while the unbalanced vibration can reciprocally transmit to the base, thereby augmenting the complexity of the base vibration. This paper introduces a feedforward decoupling control approach to address the base excitation vibration of the maglev rotor, utilising the base acceleration as the reference signal. The foundation of the feedforward controller model for countering base excitation vibration is constructed through the utilisation of inverse system principles and system identification techniques. The resultant decoupling model is engineered to facilitate decentralised control of the rotor across four radial directions. Simultaneously, to mitigate the impact of rotor unbalance on the base acceleration reference signal used by the feedforward controller, the least mean square (LMS) adaptive filtering algorithm is employed. This strategy facilitates unconstrained rotation of the rotor around its inertial axis, thereby reducing the unbalanced inertial forces and enabling isolated control of both base excitation vibration and rotor unbalance-induced vibration in the maglev system. An experimental device is constructed to validate the effectiveness of the proposed control methodology. The experimental results show that the maximum vibration amplitude of the maglev rotor decreases by 47% ~71% after the control method is applied.

Tracking Modal Parameters of Structures Online Using Recursive Stochastic Subspace Identification under Ambient Excitations

Continuous and autonomous system identification is an alternative to regular inspection during operations, which is essential for structural integrity management (SIM) as well as structural health monitoring (SHM). In this regard, online (or real-time) system identification techniques that have recently received considerable attention can be used to assess the current condition and performance during operations and, in the meantime, can be utilized to detect any damage or deterioration. For example, stochastic subspace identification (SSI), based on recursive formulation, has proven its capability in tracking modal parameters as well as time-variant dynamic behaviors. This study proposes the implementation of recursive SSI (RSSI) using the matrix inversion lemma to track slow time-varying parameter changes under ambient excitations. Subsequently, some investigations for practical implementation are examined and discussed. For verifying the reliability of SHM applications based on the proposed methods, two datasets measured from different experiments are exploited to identify the modal parameters reclusively. The results from both numerical simulations and experimental investigations demonstrated the effectiveness of tracking the modal parameters exhibiting time-varying dynamic characteristics under white noise excitations (or ambient excitations).

Automated Seizure Detection Based on State-Space Model Identification.

In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signal epochs. Such parameters were used as features for the classifiers in our study. We analyzed 69 seizure and 55 non-seizure recordings and an additional 10 continuous recordings from Thomas Jefferson University Hospital, alongside a larger dataset from the CHB-MIT database. By dividing EEGs into epochs (1 s, 2 s, 5 s, and 10 s) and employing fifth-order state-space dynamic systems for feature extraction, we tested various classifiers, with the decision tree and 1 s epochs achieving the highest performance: 96.0% accuracy, 92.7% sensitivity, and 97.6% specificity based on the Jefferson dataset. Moreover, as the epoch length increased, the accuracy dropped to 94.9%, with a decrease in sensitivity to 91.5% and specificity to 96.7%. Accuracy for the CHB-MIT dataset was 94.1%, with 87.6% sensitivity and 97.5% specificity. The subject-specific cases showed improved results, with an average of 98.3% accuracy, 97.4% sensitivity, and 98.4% specificity. The average false detection rate per hour was 0.5 ± 0.28 in the 10 continuous EEG recordings. This study suggests that using a system identification technique, specifically, state-space modeling, combined with machine learning classifiers, such as decision trees, is an effective and efficient approach to automated seizure detection.

Manufacturing, development and control of a two-way 3D printed soft actuator actuated with SMAs

In this contribution, the development of a novel two-way 3D printed soft actuator actuated with shape memory alloys (SMAs) is presented, considering all the stages from the design, manufacturing, control, and implementation. The SMAs are integrated into the 3D printed composite using thermoplastic polyurethane (TPU). In order to measure the deflection of the soft actuator a computer vision system was implemented. With these measures and using system identification techniques, a mathematical model was developed, which describes the dynamics of the prototype and helps to design of a controller. However, precise control of deflection in systems actuated by SMAs is challenging due to their inherent nonlinearities and hysteretic behavior. To face this challenge, a proportional-integral (PI) controller was designed based on robust stability conditions. The effectiveness of the designed PI controller was validated through experimental results.

An optimal cyber–physical attack strategy on DC microgrids

This paper proposes an optimal attack strategy against Direct Current MGs (DCMGs). In the proposed strategy, attackers first eavesdrop on the data communicated between a target converter and the MG Control Center (MGCC) to model (i) the converter by its state-space representation using system identification techniques, and (ii) the rest of the system by its Thevenin equivalent circuit using the least-square method. Afterwards, the obtained model is used in an optimal control framework to design a False Data Injection Attack (FDIA) that controls the converter’s output to achieve the attackers’ objective, e.g., triggering the overcurrent or under-voltage protection of the converter. The proposed framework fulfills the objective while minimizing the rate of change of the converter’s voltage and the required control effort during the FDIA (to keep the attack stealthy) in the presence of the attack constraints. Such an attack strategy provides cyber-security experts with insight into potential attacks and their impacts on the operation and stability of DCMGs, and helps them to design and test their cyber-security measures more effectively. Simulation results in MATLAB/Simulink corroborates the effectiveness of the proposed attack strategy.

Attitude modelling and real-time robust control of a 3-DoF quadcopter UAV test bench

Abstract In this work, a three degrees-of-freedom (3-DoF) static quadcopter unmanned aerial vehicle (UAV) test-rig of a pendulum-type configuration is custom-designed, developed, instrumented, and interfaced with a PC. The rig serves as a test bed to develop high-fidelity mathematical models as well as to investigate autopilot designs and real-time closed-loop controllers’ performances. The Simulink Desktop Real-Time software is employed for the quadcopter’s attitude signals acquisition and real-time implementation of closed-loop controllers on a target microcontroller hardware. The mathematical models for pitch, roll, and yaw axes are derived via the first principle and validated with the experimental linear system identification (SI) techniques. Subsequently, employing the multi-parameter root contour technique, the classical proportional integral derivative (PID) controllers are designed and implemented in real-time on the quadcopter UAV test rig. This served as a benchmark controller for comparing it with an integral-based linear quadratic regulator (LQR) controller. Further, to improve the transient response of the LQR controller, a novel robust integral-based LQR controller with a feedforward term (LQR-FF) is implemented, which shows much superior performance than the benchmark and basic LQR controller. This work thus will act as a precursor for a more complex 3-DoF autopilot design of an untethered quadcopter.

Estimation of Joint Parameters Using Frequency-Based Substructuring Techniques

This study presents frequency-based substructuring (FBS) techniques and an identification method for predicting joint parameters. Two FBS techniques, FBS-1 and FBS-2, were derived by assuming pseudomasses at the joint nodes between adjacent substructures. It is estimated that the main reason for the discrepancy with the analytical FRFs is the difficulty in describing the low-frequency responses owing to the assumed pseudomasses of the substructures. Although the FRF curve based on the FBS-2 technique is very close to the analytical FRF curve up to the first resonance frequency, some inconsistencies occur thereafter. It is analyzed that the FRFs up to the first resonance frequency can be utilized for data expansion methods and system identification techniques. Paying attention to this result, this study also provides an identification method to estimate the joint parameters based on the FRF variation. Its validity is illustrated using a numerical example.

Identification of flutter boundary of a bridge deck section using CFD and flutter margin analysis

Aeroelastic flutter identification of a two-dimensional bridge deck section using time domain techniques is studied. Aeroelastic simulations are performed using the aeroelastic solver available in open source OpenFOAM 5.0, wherein aerodynamic loads are computed using Unsteady Reynolds Average Naiver–Stokes (URANS) solver. The bridge deck section, modelled as a rigid body, exhibits pitch and heave degrees of freedom. Time domain system identification techniques, which include VAR and ERA (Vector auto-regressive and Eigensystem realization algorithm), are used to extract frequency and damping parameters from the aeroelastic response for various wind speeds.Two approaches are used for identification of the flutter boundary. The first consists of tracking the behaviour of damping and identifying the speed at which damping vanishes. The second approach is based on the flutter margin for discrete-time system (FMDS), which exhibits a linear behaviour with dynamic pressure in the vicinity of flutter.Results presented indicate that the ERA yielded slightly better estimates of damping compared to VAR. Furthermore, the FMDS parameter provided a reasonable estimate of the flutter boundary using only two data points, suggesting that this is an efficient strategy (as compared to tracking the damping) for flutter identification when the cost per aeroelastic response data point is high.

Large Eddy Simulation Combined with System Identification Method to Analyze Non-Premixed Flame Transfer Function

ABSTRACT The heat release response (HRR) of the flame is a pivotal aspect of combustion instability investigation, commonly characterized by the Flame Transfer Function (FTF). This study employed a combination of large eddy simulation (LES) and system identification (SI) techniques to derive the FTF for a methane-air non-premixed swirling burner. The influence of memory time and excitation signals within the LES+SI was investigated as only a few studies have been conducted in this direction so far, and validated against experimental data. Variations in memory time led to notable shifts in frequency and amplitude for gain peaks and valleys, with modest effects on phase delay within the 20–140 Hz range. The number of corner frequencies increased with memory time, whereby smaller memory times flattened the gain curve and underestimated phase delay, and larger memory times intensified curve undulations, distorting finer details. By minimizing the Mean Relative Error (MRE) between LES+SI results and experimental data, the optimized memory time of 0.017 s was identified, which closely aligned with the convection time of 0.0167 s. Consequently, convection time could be referenced in a priori estimation approach for memory time in a non-premixed swirling burner. In comparing FTF obtained from different excitation signals, the Superimposed Sinusoidal Signal (SINE) excelled in the gain curve within the 20–100 Hz range, accurately capturing the 30 Hz gain peak and 80 Hz valley, while the Broadband White Noise (BBWN) excelled in the phase curve within the same range, both yielding a smaller mean relative error (MRE). However, the Discrete Random Binary Signal (DRBS) demonstrated the best consistency with experimental data within the 100–190 Hz range, and boasted an MRE of 38.01% and 28.55% respectively for the gain and phase curve within the entire frequency range. Consequently, we inferred that DRBS functioned as the excitation signal with the most minimal identification error.

Operational modal analysis, seismic vulnerability assessment and retrofit of a degraded RC bell tower

This paper presents damage assessment through Operational Modal Analysis (OMA) and Finite Element (FE) model updating of the bell tower of the church of Castro in Bergamo, Italy. The tower is a 39 m high reinforced concrete structure with hollow cross-section and double-curved shape. The research was dictated by the need to identify the actual damage state of the structure, which was found through visual inspections. Piezoelectric accelerometers were used to record the ambient vibrations in subsequent test setups, using the roving technique for system identification. A detailed FE model was created with shell elements and calibrated to match the system identification results. A simplified beam model was then developed based on the modal analysis results of the detailed model. A sensitivity analysis was performed to identify the most influential model parameters on the modal characteristics of the system. Subsequently, the optimal values of these parameters were determined by an optimisation procedure carried out using a typical global optimization algorithm. The updating results allowed assessment of the actual condition of the bell tower and its seismic vulnerability. Finally, a seismic strengthening solution was recommended.

Modeling and Control of an Indirect Solar Dryer in Forced Convection Mode Using System Identification and Model Predictive Control

In nonlinear process, system modeling is vital part to predict and examine the performance of a plant. Here, indirect solar dryer performed in forced convection mode taken as a plant. Several experimental tests were performed by drying tomato slices, both in forced as well as in natural convection mode to study the dynamic characteristics of it. The efficiency of indirect solar dryer was better with forced convection than the natural convection mode. In this investigation System Identification technique is used to predict the model equation for forced convection. Designing a conventional control strategy to ensure efficiency is cumbersome due to the plant’s multivariable nature and nonlinear dynamics. The regular proportional-integral-derivative (PID) controller provides unproductive control action for nonlinear system. A model predictive controller (MPC) is intended for the proposed system ensuring efficiency of the indirect solar dryer. The MPC parameters must be selected appropriately to ensure optimal performance. The Matlab platform was used to get the both MPC algorithms and conventional PID performance.

A Nonparametric Regularization for Spectrum Estimation of Time-Varying Output-Only Measurements

In this work, an advanced 2D nonparametric correlogram method is presented to cope with output-only measurements of linear (slow) time-varying systems. The proposed method is a novel generalization of the kernel function-based regularization techniques that have been developed for estimating linear time-invariant impulse response functions. In the proposed system identification technique, an estimation method is provided that can estimate the time-varying auto- and cross-correlation function and indirectly, the time-varying auto- and cross-correlation power spectrum estimates based on real-life measurements without measuring the perturbation signals. The (slow) time-varying behavior means that the dynamic of the system changes as a function of time. In this work, a tailored regularization cost function is considered to impose assumptions such as smoothness and stability on the 2D auto- and cross-correlation function resulting in robust and uniquely determined estimates. The proposed method is validated on two examples: a simulation to check the numerical correctness of the method, and a flutter test measurement of a scaled airplane model to illustrate the power of the method on a real-life challenging problem.

Accurate Liquid Level Measurement with Minimal Error: A Chaotic Observer Approach

This paper delves into precisely measuring liquid levels using a specific methodology with diverse real-world applications such as process optimization, quality control, fault detection and diagnosis, etc. It demonstrates the process of liquid level measurement by employing a chaotic observer, which senses multiple variables within a system. A three-dimensional computational fluid dynamics (CFD) model is meticulously created using ANSYS to explore the laminar flow characteristics of liquids comprehensively. The methodology integrates the system identification technique to formulate a third-order state–space model that characterizes the system. Based on this mathematical model, we develop estimators inspired by Lorenz and Rossler’s principles to gauge the liquid level under specified liquid temperature, density, inlet velocity, and sensor placement conditions. The estimated results are compared with those of an artificial neural network (ANN) model. These ANN models learn and adapt to the patterns and features in data and catch non-linear relationships between input and output variables. The accuracy and error minimization of the developed model are confirmed through a thorough validation process. Experimental setups are employed to ensure the reliability and precision of the estimation results, thereby underscoring the robustness of our liquid-level measurement methodology. In summary, this study helps to estimate unmeasured states using the available measurements, which is essential for understanding and controlling the behavior of a system. It helps improve the performance and robustness of control systems, enhance fault detection capabilities, and contribute to dynamic systems’ overall efficiency and reliability.

Design of a soft sensor based on silver-coated polyamide threads and stress-strain modeling via Gaussian processes

The demand for reliable and efficient soft sensors has grown exponentially with the evolution of wearable devices and smart textiles. However, existing soft sensors often face challenges related to hysteresis, noise, and accuracy, hindering their seamless integration into practical applications. Our research presents a pioneering stress–strain soft sensor model based on silver-coated polyamide threads, augmented with additional silicone and graphite coatings for enhanced properties. Specifically, the silicone coating proves instrumental in elevating the sensor’s gauge factor and reducing noise, resulting in heightened accuracy. The extensive data analysis reveals the presence of hysteresis and non-linearities; however, our data exhibits remarkable robustness, as indicated by the high Spearman correlation coefficient values. In the context of system identification, a comparative analysis between traditional regression methods and Gaussian Process (GPs) Regression demonstrates the superior performance of GPs: this technique outperforms conventional regression techniques, obtaining 8.75 ± 4.06% Root Mean Square Error (RMSE) compared to the 12.70 ± 7.04% error observed in traditional methods. This research not only advances the field of soft sensor technology by developing an accurate, affordable and adaptable device, but also offers valuable insights into highly effective system identification techniques tailored for wearable devices.

Performance monitoring and redesign of power system stabilizers based on system identification techniques

To maintain stable power system operation, damping control systems are used in conventional power plants, known as Power System Stabilizers (PSS). However, to derive suitable controller parameters, the current methods require dynamic models that are difficult to maintain and update, and in some cases, might not be available. This makes it challenging for grid operators to maintain system stability when the system is under stringent operating conditions or undergoes the loss of major transmission corridors, which would require a new stabilizer design to provide adequate damping.Leveraging the availability of real-time measurements and “probing” technologies, this paper provides a complementary approach that does not require power system simulation models, but is based on system identification techniques that allow to derive simple and accurate models based on data collected on the system. The proposed method allows to monitor the performance of damping controllers and even to perform redesign based on the models derived with system identification. The resulting redesign could be used to update PSS parameters and improve damping without the need of removing existing damping control systems from service.

Esophageal balloon catheter system identification to improve respiratory effort time features and amplitude determination

Objective. Understanding a patient’s respiratory effort and mechanics is essential for the provision of individualized care during mechanical ventilation. However, measurement of transpulmonary pressure (the difference between airway and pleural pressures) is not easily performed in practice. While airway pressures are available on most mechanical ventilators, pleural pressures are measured indirectly by an esophageal balloon catheter. In many cases, esophageal pressure readings take other phenomena into account and are not a reliable measure of pleural pressure. Approach. A system identification approach was applied to provide accurate pleural measures from esophageal pressure readings. First, we used a closed pressurized chamber to stimulate an esophageal balloon and model its dynamics. Second, we created a simplified version of an artificial lung and tried the model with different ventilation configurations. For validation, data from 11 patients (five male and six female) were used to estimate respiratory effort profile and patient mechanics. Main results. After correcting the dynamic response of the balloon catheter, the estimates of resistance and compliance and the corresponding respiratory effort waveform were improved when compared with the adjusted quantities in the test bench. The performance of the estimated model was evaluated using the respiratory pause/occlusion maneuver, demonstrating improved agreement between the airway and esophageal pressure waveforms when using the normalized mean squared error metric. Using the corrected muscle pressure waveform, we detected start and peak times 130 ± 50 ms earlier and a peak amplitude 2.04 ± 1.46 cmH2O higher than the corresponding estimates from esophageal catheter readings. Significance. Compensating the acquired measurements with system identification techniques makes the readings more accurate, possibly better portraying the patient’s situation for individualization of ventilation therapy.

Investigation on the Airworthiness of a Novel Tri-Rotor Configuration for a Fixed Wing VTOL Aircraft

In this paper, a novel tri-rotor configuration is proposed with the goal of granting vertical take-off and landing capabilities to a future concept of tiltrotor, fixed-wing, aircraft while minimizing the overall mass of the propulsive system and the amount of aerodynamic drag developed during horizontal flight. The novelty of the presented configuration is related not only to the thrust vectoring capabilities of all three rotors but also to the constraints surrounding the action of the rear rotor, which will be required to provide thrust during both vertical and horizontal flight stages while drawing power from an internal combustion engine fixed inside the aircraft's fuselage. Another distinctive feature of the proposed configuration is related to the 20/80 thrust distribution which exists between the front and rear rotors respectively in vertical flight, unlike the more conventional approach of having all three rotors evenly loaded. The proposed rotorcraft configuration was then translated into a test vehicle which was subjected to several stages of ground and flight testing, with the ultimate goal of evaluating the airworthiness of this multi-rotor configuration as a concept. This process also encompasses the development of a custom flight control firmware in PX4, required to operate not only this vehicle but also any other multi-rotor or Vertical Take-Off and Landing system with such configuration. Finally, a frequency-response based system identification technique is applied to the collected flight data as to obtain a suitable flight dynamics model for future autopilot tuning.