System Identification Techniques Research Articles

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.

Subglottal Impedance-Based Model Parameter Estimation via System Identification

Accurate mathematical modeling of different systems of the human body stands as a key issue in medical and bioengineering applications. This paper specifically considers the continuous-time parameter estimation of the impedance-based mathematical model — a mechano-acoustic representation of the subglottal system. This approach allows the customization of the model for each patient. A key advantage of having a patient-dependent model of the subglottal system is to facilitate the ambulatory non-invasive monitoring of the glottal airflow and the assessment of vocal functions, using an accelerometer on the neck skin surface. For this model of the subglottal system, the glottal airflow is the excitation signal, while the acceleration on the neck skin is the system response. In this study, continuous-time parameter estimation of the impedance-based model for the subglottal system is applied, using both frequency response and sampled data through system identification techniques.

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.

(Invited) Estimating OCP-Temperature Dynamics for Determining Lithium-Ion Battery Entropy Coefficients

The open-circuit potential (OCP) of a li-ion cell is a function of both state-of-charge and temperature. Characterising its dependence on temperature is important for thermal simulations as the derivative of the OCP with temperature acts as a reversible heat source term in the model. Known as the entropy coefficient, this derivative is a thermodynamic quantity that varies over the cell’s state-of-charge (SoC). The potentiometric approach [1] is widely used to characterise the entropy coefficient for a given SoC. This involves applying step changes in the temperature and allowing the cell OCP to reach (or approach) a steady-state value, which can take several hours (>8 hours), for a given SoC of interest.Robust techniques are required that can significantly reduce the experimental time and quantify the entropy coefficient over the full SoC window, leading to higher fidelity battery thermal models. In this work, the dynamics between the OCP and cell temperature are exploited via system identification techniques to derive the entropy coefficient. This is achieved by estimating the underlying kernel function between the cell OCP and temperature dynamics. The kernel function is a non-parametric estimate of the dynamics from which the entropy coefficient can be determined.In this work temperature signals are designed with multiple frequency components and applied to a cell via Peltier elements. The corresponding OCP response is then analysed, in the frequency domain, to estimate the kernel function. Unlike the potentiometric method, which drives the battery to steady-state and necessitates long experimental durations, the kernel function can be estimated under transients, facilitating faster characterisation. The figure demonstrates comparable results of the entropy coefficient, over the full SoC interval, when estimated via the kernel function and the potentiometric approach. The approach, compared to the potentiometric method, however, brings around a two to three times reduction in experimental time per SoC and gives insight into the OCP and temperature dynamics. The signal design, kernel estimation and entropy coefficient estimation procedures are detailed in this talk. Schmidt, J. P., Weber, A., et al., Electrochimica Acta 2014, 137, 311-319. DOI 10.1016/j.electacta.2014.05.153 Figure 1

Adaptive Control of an Inverted Pendulum by a Reinforcement Learningbased LQR Method

Inverted pendulums constitute one of the popular systems for benchmarking control algorithms. Several methods have been proposed for the control of this system, the majority of which rely on the availability of a mathematical model. However, deriving a mathematical model using physical parameters or system identification techniques requires manual effort. Moreover, the designed controllers may perform poorly if system parameters change. To mitigate these problems, recently, some studies used Reinforcement Learning (RL) based approaches for the control of inverted pendulum systems. Unfortunately, these methods suffer from slow convergence and local minimum problems. Moreover, they may require hyperparameter tuning which complicates the design process significantly. To alleviate these problems, the present study proposes an LQR-based RL method for adaptive balancing control of an inverted pendulum. As shown by numerical experiments, the algorithm stabilizes the system very fast without requiring a mathematical model or extensive hyperparameter tuning. In addition, it can adapt to parametric changes online.

The new Subspace-based poly-reference Complex Frequency (S-pCF) for robust frequency-domain modal parameter estimation

Subspace identification has been widely used in modal parameter estimation. The formulation of a parametric subspace-driven modal identification technique is achieved by using the Singular Value Decomposition (SVD) to remove most the uncertainties present in the null space of the system matrices used in the identification process, which leads to a more robust estimation of the modal properties. This idea has been used in the formulation of time domain modal identification techniques like the (time-domain) Eigensystem Realization Algorithm and the Stochastic Subspace identification techniques. In this paper, a subspace implementation of the new poly-reference Complex Frequency (pCF) formulated in Modal Model is proposed. The idea is to combine SVD with the new pCF to derive a new system identification technique that operates in the frequency domain by using either the Frequency Response Function or the Half Spectrum as primary data. Similarly to the ERA, the idea behind the subspace implementation of the pCF is to use different subspaces of the observability and frequency-domain controllability matrices, and carry out eigen-system realizations to estimate the modal properties corresponding to each chosen subspace. In order to illustrate its robustness, the subspace-driven pCF is applied to a simulated and two practical application examples.

Data-driven model based adaptive feedback-feed forward control schemes for open cycle - OTEC process

Open Cycle - Ocean Thermal Energy Conversion (OC-OTEC) is one of the most important renewable energy sources that generate electricity and fresh water from seawater utilizing the temperature gradient between the warm surface seawater and the cold deep seawater. This paper aims to develop non-linear data-driven model-based adaptive Feedback Control (FBC) schemes for the OC-OTEC process to track the output power and a dynamic Feed-Forward Control (FFC) scheme to reject the effects of temperature disturbance on power caused by climate variations in OC-OTEC. The experiments are conducted on a laboratory-scale OC-OTEC experimental setup at the National Institute of Ocean Technology, Chennai. Firstly, linear data-driven models are developed using system identification techniques. Based on the developed models, gain scheduling-based adaptive control schemes are developed: Proportional Integral (PI) control and Model Predictive Control (MPC). Secondly, the closed-loop performances of the developed FBC schemes are analysed under servo and regulatory operations. Furthermore, it is observed from the sensitivity analysis results that the temperature disturbance highly influences output over the manipulated variable. Hence, a dynamic FFC scheme is implemented to reject known disturbance of 1 °C variation in temperature gradient from 18 °C to 19 °C due to Sea Surface Temperature (SST) changes in temperature gradient. The results show that the proposed MPC-based FBC-FF scheme effectively enhances the tracking and disturbance rejection performance compared to the PI-based FBC-FF control scheme with a minimum Integral Square Error (ISE) of 3.055 and Control Effort (CE) of 7.505. This experimental data-based, data-driven modelling and adaptive control research provides a new direction for automating OC-OTEC. Further, these studies will help to develop a rugged and automated upscaling OC-OTEC plant control system with proposed feasible control schemes.

Analysis of an actively controlled composite box manipulator without external actuator

This study focuses on active vibration control for a single-link composite box manipulator by using a single actuator for driving and control actions. Model extraction and system identification techniques are studied to obtain mathematical models of the manipulator. The state space model is extracted from the finite element model using modal analysis conducted in ANSYS, while the system identification approach is an experimental modeling method using the input and output signals of the system. The input signals are defined as triangular and trapezoidal motion profiles, while the output signals are the acceleration signals of the manipulator. Proportional derivative and positive position feedback controllers are implemented on the obtained mathematical models to reduce residual vibrations. The simulation results are successfully verified by experiments, and uncontrolled/controlled results are evaluated with reduction rates.

Construction, modeling, and control of a three‐beam prototype using interactive fiber rubber composites

AbstractIn this contribution, we discuss the construction, modeling, and control of a three‐beam prototype that utilizes interactive fiber rubber composites integrated with shape memory alloys (SMAs) as actuators. These actuators are integrated into a textile layer and covered with an elastomer layer, providing flexibility and protection to the prototype. A mathematical model was developed to describe the behavior of the prototype, employing system identification techniques. However, precise position control of systems actuated by SMAs is challenging due to their inherent nonlinearities and hysteretic behavior. To tackle this challenge, a proportional‐integral (PI) controller was designed based on robust stability conditions. The effectiveness of the designed PI controller was validated through both numerical simulations and real experiments.

Gaussian process regression for forward and inverse kinematics of a soft robotic arm

The use of soft robotics to perform tasks and interact with the environment requires good system identification. Data-driven methods offer a promising alternative where traditional analytical model-based techniques have proven insufficient. However, their use has been limited and under-explored in soft robotics. The novelty of this research lies in the application of Gaussian processes to soft robotics and the exploration of approximate Gaussian processes (AGP) and deep Gaussian processes (DGP) methods. It highlights the advantages of Gaussian processes in modeling uncertainty, incorporating prior knowledge, and handling complex systems. This is achieved through the identification of the forward and inverse kinematics of a two-degree-of-freedom soft robotic arm actuated by three tendons. A comparison is made between different configurations using Gaussian processes and the results are also compared with those obtained from the analytical model of the kinematics and an artificial neural network (ANN). The research contributes to the development of more efficient and accurate techniques for system identification, kinematics modeling, and control in soft robotics.

Cortical temporal integration can account for limits of temporal perception: investigations in the binaural system

The auditory system has exquisite temporal coding in the periphery which is transformed into a rate-based code in central auditory structures, like auditory cortex. However, the cortex is still able to synchronize, albeit at lower modulation rates, to acoustic fluctuations. The perceptual significance of this cortical synchronization is unknown. We estimated physiological synchronization limits of cortex (in humans with electroencephalography) and brainstem neurons (in chinchillas) to dynamic binaural cues using a novel system-identification technique, along with parallel perceptual measurements. We find that cortex can synchronize to dynamic binaural cues up to approximately 10 Hz, which aligns well with our measured limits of perceiving dynamic spatial information and utilizing dynamic binaural cues for spatial unmasking, i.e. measures of binaural sluggishness. We also find that the tracking limit for frequency modulation (FM) is similar to the limit for spatial tracking, demonstrating that this sluggish tracking is a more general perceptual limit that can be accounted for by cortical temporal integration limits.

Construction of a feedback control system based on CFD simulations for the 64-element Canadian SCWR

The knowledge of the dynamic behaviors of the supercritical water in the fuel bundle of the nuclear reactor is necessary for the design of the control system. For this purpose, the transient full-scale computational fluid dynamics (CFD) simulations are used to obtain the dynamic input and output data sets of the 64-element Canadian supercritical water-cooled reactor (SCWR) in this study. Subsequently, the linear dynamic models of the reactor are obtained by the system identification technique and validated by the comparison with the results from CFD simulations. Based on the linear dynamic models, a feedback control system consists of three PI/PID controllers is constructed. The objective is to regulate the system back to the design point when the reactor is subjected to disturbances. Therefore, the performance of the designed feedback control system is also evaluated in this work. It shows that the designed control system can timely minimize the deviations and return the reactor back to the desired operating condition.