Transfer Function Model Research Articles (Page 1)

In the field of rotating machinery, such as marine propulsion shafting, magnetic bearing-supported propulsion systems have garnered significant attention due to their non-mechanical contact advantages. To address the problem that the design of magnetic bearing controllers, based on theoretical models, neglects the dynamic characteristics of practical components like power amplifiers and displacement sensors, making it difficult to achieve ideal performance in practical applications, this paper proposes a control method for Hybrid Magnetic Bearings (HMBs) that combines a time-domain identification model with robust control. The method first models the power amplifier, HMB, and displacement sensor as an equivalent single system and obtains its high-precision transfer function model by performing system identification on its time-domain data using the least squares method. Based on this foundation, a PID controller is designed using the loop-shaping method to enhance the system’s robustness and control performance. Both simulations and experiments on an HMB test rig confirmed the controller’s effectiveness. The system showed excellent levitation, dynamic stability, and disturbance rejection, with experimental results closely matching simulations. The experimental results are consistent with the simulation results. This method provides a practical and feasible technical approach for enhancing the control performance of magnetic bearing-supported propulsion shafting.

The continuous development of photovoltaic and energy storage systems has enabled the emergence of microgrids, which can operate with alternating or direct current (DC), depending on the components characteristics. Since photovoltaic panels and battery storage systems operate in DC, connecting them via a DC bus simplifies power conversion, especially for DC loads. A key challenge in microgrids is defining an effective control strategy, which may be centralized, decentralized, or distributed. In this sense, the main proposal of this paper consists in the development of a decentralized control strategy for a stand-alone DC microgrid with photovoltaic generation and battery energy storage, using a controlled voltage droop, based on virtual impedance. This paper aims to contribute with the description of the implementation steps of the local controllers, using classical control techniques based on transfer function modelling. In addition, the assembling steps of a low-cost and small-scale DC microgrid prototype are presented. The control approach maintains DC bus voltage within an appropriate range without requiring communication among system components. Moreover, it supports modular expansion by enabling the seamless integration of new generation or storage units. To validate the strategy, a microgrid prototype was developed using a 60~W PV panel, a 12~V, 7~Ah lead-acid battery, and the BOOSTXL-3PhGaNInv evaluation module with a F28379D digital signal controller for converter implementation. Experimental results, including the battery charging process and variations of solar irradiance and load demand, demonstrate the effectiveness and robustness of the proposed control system.

In this pilot study, we introduce a novel approach for the upper airway acoustic modelling aimed at developing a patient-specific transfer function of the upper airway. We modelled the upper airway as an acoustic filter, and hypothesized the parameters of such model would correlate with the anatomical features. The method involved generating a signal with known frequency characteristics at the mouth while recording the output at the suprasternal notch. Five distinct protocols were tested, and a consistency study was conducted to identify the most suitable protocol. The protocols varied in terms of input sound type and breathing maneuvers. Ten healthy subjects participated in this pilot study over three days with four recordings per day. The results indicated that the most consistent protocol utilizing white noise as the input sound while the participant breathed passively. The standard error of the difference between the detected peak frequencies was less than 10%, and the standard error for the amplitude difference was less than 5dB. Additionally, the transfer function for each participant was modelled as a cascade of six 2nd order systems. A strong negative correlation of - 0.91 (p = 0.0003) was found between the first natural frequency and the participants' height, while a positive correlation of 0.69 (p = 0.027) was observed between the transfer function gain and the participants' BMI. This study presents an initial step toward developing an acoustic transfer function of the upper airway, which could ultimately be used to classify and diagnose respiratory disorders.

This paper presents a black-box mathematical model of the electrohydraulic system controlling the wave generation structure in a wave channel under various operating conditions. For the position control of the system, NI-CRIO 9074 hardware and LabVIEW software were used. Open-loop position control experiments of the hydraulic cylinder were conducted using stimulus signals with different initial positions (0, 120, and 240 mm) and amplitudes. Data were recorded for different sampling times: 1 ms, 2 ms, 5 ms, and 10 ms. The recorded data were processed using the System Identification Toolbox (SIT) in MATLAB, and system models were developed in both continuous and discrete time domains using transfer function, state space, and AutoRegressive with eXogenous input (ARX) models. These models were compared and analyzed based on their fit rates to the training and test data. Among the system models with high compliance rates, the top three models were selected for further comparison using an additional test dataset. Based on this evaluation, the transfer function model (120 mm initial condition and 1 ms sampling time) type was identified as the best-performing model. This model was successfully integrated into the real-time control study, achieving effective controller performance.

In order to quantify the dynamic characteristics of the slug calorimeter, based on the linear time-invariant system theory, the heat conduction process and temperature measurement subsystem of the sensitive element are modeled, respectively, and the dynamic response characteristics of the calorimeter are characterized by the frequency characteristics of the transfer function. The heat conduction differential equation of the sensitive element under the input of step heat flux density is established, and the analytical solution of the time domain response of the rear wall temperature is derived. Then, the transfer function between heat flux input and rear wall temperature output is obtained by using the unit impulse response method. On this basis, the transfer function of the complete measurement system is constructed by connecting the transfer functions of the temperature measurement system in series. A prototype calorimeter is developed, and the sensitivity calibration experiment is completed on the laser heat flux calibration system, which verifies the correctness of theoretical analysis. The research results show that the passband width of the sensitive element is proportional to the thermal conductivity of the material and inversely proportional to the square of the thickness. The dynamic characteristics of the temperature measurement system have a significant impact on the overall frequency response. This study provides a theoretical basis for the optimal design of heat capacity calorimeter with high dynamic response.

Various industrial technologies require flexible material webs to undergo processes such as thermal treatment (e.g., drying), printing, or laminating. Such processes are usually performed within winding systems, where the web goes through a set of rolls, and the precision of the web movement determines the quality of the final product. Therefore, high accuracy in the control of both the longitudinal and lateral movement of the web is of paramount importance. Designing the proper control system requires insightful analysis of the technological setup and precise modeling of its dynamic properties. In this paper, the transfer function model of the roll-to-roll system with closed-loop web circulation has been developed based on the mathematical description of the open-loop system. It has been proven that the analyzed system can be efficiently represented by an integral block with negligible inertia. Having established this, several control algorithms have been analyzed, and, as a result, the dedicated adaptive–predictive control algorithm has been proposed. The developed solutions have been verified both by simulations and real experiments performed using the semi-industrial laboratory setup. The high control quality of the proposed algorithm (e.g., considerable reductions in overshoot and settling time compared to PI control), outperforming classical approaches, has been confirmed under various disturbances.

This study aims to develop and validate in-lab a novel approach for estimating head linear acceleration in ice hockey impacts using IMU-instrumented helmets. The use of AutoRegressive (AR) modeling was investigated as a solution to mitigate the decoupling observed between the helmet and the head. A series of impacts were conducted on a helmeted Hybrid III 50th percentile male Anthropometric Test Device (ATD). The impacts were performed using a custom-built pendulum impactor in four directions (front, front-oblique, side and back-oblique) and at two energies, 33 and 79J, except for the back-oblique direction, which was tested only at 33J. The processing pipeline included impact segmentation, main direction estimation and application of the AR-based transfer function modeling. The error with respect to the reference signals from the headform was quantified and the transformed signals were compared with the unprocessed (raw) and lowpass filtered signals. The generalization capabilities of the transfer function were also evaluated on a different helmet type. The application of the transfer function resulted in a reduction of up to 9.04g (57%) and 27.54% for the average Root Mean Squared Error (RMSE) and peak Mean Absolute Percentage Error (MAPE), respectively, with a consistent error decrease across all impact directions, compared to the lowpass filtered signal. However, when evaluated on a different helmet model, the transfer function showed larger errors. The proposed methodology effectively improves the estimation of head linear acceleration across all impact directions. Nevertheless, performance varies with helmet type, indicating the need for helmet-specific adjustments (e.g., through model retraining).

In this paper we aim to examine the correlation between diffusion tensor imaging parameters of anatomical connectivity and characteristics of signal transmission obtained from patient-specific transfer function models. Here, we focused on elucidating the correlation between structural and functional neural connectivity within a cohort of pediatric patients diagnosed with dystonia. Diffusion tractography images were obtained from 12 patients with dystonia prior to the deep brain stimulation surgery. For each patient, we processed the imaging data to estimate anatomical measures including fractional anisotropy, axial diffusivity, number of fibre tracts per unit area, and fibre tract length. After the implantation of temporary depth leads for each patient as part of their treatment plan, intracranial signals were recorded. Transfer function models of local field potential recordings and the corresponding measures of functional connectivity were computed for each patient. Linear mixed effect analysis was then employed to determine the relationship between transfer function measures and diffusion tractography parameters. Our results illustrate a positive correlation between fractional anisotropy, AD, and intrinsic neural transmission measures, representing amplification and spread of intrinsic neural oscillations, obtained from the transfer functions models. However, no significant correlation was found between the functional connectivity and number of fibre tracts or fibre lengths. Our findings suggest that white matter integrity, as measured by fractional anisotropy and AD, can potentially reflect the amplification and spread of intrinsic brain signals throughout the network. This study underscores the significant relationship between structural and functional connectivity, offering valuable insights into propagation of neural activity in the brain network and potential implications for optimizing non-invasive treatments and planning for neurological disorders.

In this paper, a novel identification method is addressed for a class of nonlinear Wiener systems subject to time delay, and this identification problem involves the estimation of delay time, transfer function model parameters and neural fuzzy model parameters. Aiming to identify separately the linear and nonlinear blocks, the separable signals are introduced. First, the correlation characteristics of separable signals through the Wiener system are analyzed, then the correlation analysis technique is applied to calculate the unknown parameters involving time delay and transfer function model. Moreover, in the neural fuzzy model parameters estimation, we first calculate the center and width of the neural fuzzy model. Then, to improve global search mechanism ability and converge speed of particle swarm optimization method, the improved particle swarm optimization and cuckoo search techniques are introduced to figure out the weight of the neural fuzzy model, which obtains good global search ability and convergence speed. The simulation comparison results in numerical case and nonlinear process are presented to verify that the feasibility of the Wiener system identification.

This paper presents an experimental exploration of position control techniques applied to a Rotary Motion Servo Plant (SRV02), employing output feedback controllers. The transfer function model of the SRV02 is derived through first principles. Output feedback controllers, specifically Position Velocity (PV) and Multirate Output Feedback (MOF), are formulated and designed. The study evaluates the efficacy of these controllers in real-world scenarios for positioning control of the SRV02. A comparative analysis between PV and MOF controllers was conducted, assessing their static and dynamic characteristics. The results, obtained from both simulation and experimental setups, illustrate the superiority of MOF over PV controller in achieving precise position control of the SRV02. The findings of this study not only validate the effectiveness of MOF in position control applications but also provide insights into the practical implementation of output feedback controllers for enhancing the performance of rotary motion servo systems like SRV02.

This paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional–integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors.

Aiming at the problem of a full-width horizontal-axis roadheader being prone to diverge from the preset trajectory of the tunnel, a deviation correction control method based on particle swarm optimization–backpropagation (PSO-BP) neural network proportional–integral–derivative (PID) control is proposed. The track error model of the walking system and the transfer function model of the deviation correction control are established. The PSO-BP PID controller is designed; the beginning weights of BP are enhanced by the PSO, and the BP receives the optimal weights to instinctively adapt the PID parameters. An experiment on deviation correction control of the roadheader was carried out. The experimental results indicate that the maximum steady-state error of PSO-BP PID for deflection angle and angular velocity is reduced by 41.03% and 44.93%, respectively, compared with BP PID, and the average rise time for deflection angle and angular velocity is reduced by 75.76%.

Universal Programmable Transfer Function Modeling Circuit Based on Memristors for PID Simulation Application

This work introduces an extension of the iterated moving average filter, called the Extended Kolmogorov-Zurbenko (EKZ) filter for time series and spatio-temporal analysis. The iterated application of a central simple moving average (SMA) filter, also known as a Kolmogorov-Zurbenko (KZ) filter, is a low-pass filter defined by the length of the moving average window and the number of iterations. These two arguments determine the filter properties such as the energy transfer function and cut-off frequency. However, the existing KZ filter is only defined for positive odd integer window lengths. Therefore, for any finite time series dataset there is only a relatively small selection of possible window lengths, determined by the length of the dataset, with which to apply a KZ filter. This inflexibility impedes use of KZ filters for a wide variety of applications such as time series component separation, filtration, signal reconstruction, energy transfer function design, modeling, and forecasting. The proposed EKZ filter extends the KZ and SMA filters by permitting a widened range of argument selection for the filter window length providing the choice of an infinite number of filters that may be applied to a dataset, affording enhanced control over the filter characteristics and greater practical application. Simulations and real data application examples are provided.

The article examines a thermal industrial object with distributed temperature parameters. An information-geometric model based on transfer functions has been developed for the industrial object and a mathematical model has been determined. Additionally, a cross-coupling compensator has been synthesized. Sequential logarithmic identification method was used and studied to formulate the mathematical model of the object. Transmission coefficients, transport delay and time constants for each transfer function of the information-geometric model were calculated during this process. A compensator for three temperature channels was synthesized using matrix calculus approaches from linear algebra. Given the computational complexity of calculations, the authors developed and utilized specialized software capable of automatically real-time identification of smooth aperiodic transient responses and automatically synthesizing a cross-coupling compensator in real-time The Einstein digital temperature converter set was used for this purpose. It was assembled into a measurement channel with a digital output and a USB interface. The authors also investigated temperature stabilization channels in the studied industrial object using a PID controller and a relay actuator. A mathematical study of the amplitude and period of self-oscillations across the three control channels was conducted determining the impact of transport delay on the amplitude and period of temperature signal oscillations. Future research is planned for different types of industrial objects characterized by different transfer functions, specifically those involving high-frequency vibration processes. Vibration and its derivatives, namely vibrational displacement, velocity, and acceleration along with temperature, are among the most commonly monitored parameters in industrial applications.

Aquaculture has emerged as a sustainable alternative to meet the growing demand for aquatic products while preserving natural ecosystems. This study presents the design, simulation, and experimental validation of an intelligent multivariable control system for aquaculture tanks aimed at cultivating Mugil incilis, a native species of the Colombian Caribbean. The system integrates three control strategies: a classical Proportional-Integral-Derivative (PID) controller, a fuzzy logic–based PID controller, and a neural network predictive controller. All strategies were evaluated in simulation using a third-order transfer function model identified from real pond data. The fuzzy PID controller reduced the mean squared error (MSE) by 66.5% compared to the classical PID and showed faster settling times and lower overshoot. The neural predictive controller, although anticipatory, exhibited high computational cost and instability. Only the fuzzy PID controller was implemented and validated experimentally, demonstrating robust, accurate, and stable regulation of potential hydrogen (pH), dissolved oxygen, and salinity under dynamic environmental conditions. The system operated in real time on embedded hardware powered by a solar kit, confirming its suitability for rural or off-grid aquaculture contexts. This approach provides a viable and scalable solution for advancing intelligent, sustainable aquaculture practices, particularly for sensitive native species in tropical regions.

Clinical studies show that pulsatile haemodynamics and pressure waveform analysis are valuable for the diagnosis and prognosis of hypertension and heart failure (HF). While generalized transfer functions (GTFs) have shown clinical significance, some studies report limitations with GTF in capturing central pulsatile haemodynamics. This study introduces a hybrid time-frequency, machine learning-based transfer function that reconstructs central pressure waveforms from peripheral measurements, accurately capturing central pulsatile haemodynamics and arterial wave-based information. Our method uses Fourier harmonics for approximating the pressure waveform. The model is trained on these harmonics using a feed-forward neural network (FNN) with a custom time-domain cost function that captures the full temporal dynamics of physiological events during a cardiac cycle. The final hybridized-FNN transfer function model is trained, tested, and validated on data from the Framingham Heart Study (6698 participants). Our method produces carotid waveforms with median normalized mean squared error (%NMSE) values of 0.09 and 0.10 for brachial and radial inputs, compared to 0.42 and 0.26 for GTF, with similar accuracy improvements in other metrics. Correlation coefficients for the first and second forward wave times and amplitudes are 0.97, 0.93, 0.82, and 0.79 with brachial input, and 0.97, 0.92, 0.87, and 0.80 with radial input, vs. as low as 0.22 and 0.31 for GTF. Overall, our method significantly improved correlations across similarity, morphology, and wave-based parameters. Our hybridized FNN transfer function approach enables robust calculation of the central arterial pressure waveform from a single measured peripheral waveform, preserving key physiological sequences in a cardiac cycle.

To suppress the image shift of the circumferential scanning detection system, an advanced optical scanning galvanometer is used for high-speed image shift compensation. From the design of, to our knowledge, a new type of scanning galvanometer, the mathematical model of the galvanometer transfer function is obtained, the time sequence logic of circumferential scanning image shift compensation control is designed, and an image shift compensation test system is set up, including a target, an azimuthal rotary table, an image shift compensation galvanometer, and other functional components. The results show that the image shift compensation system has a sweep frequency of 80 Hz, the exposure time is 5 ms, the detector image is clear, and the circumferential scanning image shift is suppressed. The design of the control logic and control method meets the demand of the high-frequency image shift compensation in the circumferential scanning detection system. This paper has reference value for image shift compensation in circumferential scanning detection system imaging.

Effective vibration isolation is critical for minimizing the transmission of unwanted mechanical energy from a source to its surrounding environment, especially in precision systems, where even minor disturbances can degrade performance. This study addresses the challenge of low-frequency vibration transmission in lightweight, high-sensitivity audio devices such as turntables with masses below 10 kg. Traditional vibration mitigation strategies-primarily based on increasing system mass to raise the resonant frequency-are unsuitable for such systems due to weight constraints and potential impacts on operational dynamics. Previous studies have identified a critical resonance range of 5-15 Hz, corresponding to the tonearm and cartridge assembly, where transmitted vibrations can compromise signal fidelity and cause mechanical degradation. This research aims to develop an effective and universal vibration isolation solution tailored for lightweight turntables, focusing on external isolation from structural vibration sources such as furniture and flooring. To achieve this, a two-stage experimental methodology was employed. In the first stage, the excitation method with the use of a hammer tapping machine was evaluated for its ability to simulate real-world vibrational disturbances. The most representative excitation methods were then used in the second stage, where the isolation performance of various materials and systems was systematically assessed. Tested isolation strategies included steel springs, elastomeric dampers, and commercial linear vibration isolators. The effectiveness of each isolation material was quantified through spectral analysis and transfer function modeling of vibration acceleration data. The results provide comparative insights into material performance and offer design guidance for the development of compact, high-efficiency anti-vibration platforms for audio turntables and similar precision devices.

Performance analysis of permanent magnet synchronous motor based on transfer function model using PID controller tuned by Ziegler-Nichols method