Single-input Single-output Model Research Articles

Uniaxial and multiaxial cyclic deformation behavior prediction of Z2CN18.10 austenitic stainless steel based on Transformer deep learning method

In this study, the experimental study of cyclic deformation behavior of Z2CN18.10 austenitic stainless steel under uniaxial, cross, rhombus and circular paths is investigated. Based on Transformer deep learning method, three models are built to predict the cyclic deformation behavior of the material. One is the single input–single output model, which is used to predict the uniaxial cyclic deformation behavior. The second is the double input–single output model, which is used to predict the biaxial cyclic deformation behavior. The third is the time series multi-step model, which is used to predict the stress evolution under all paths. The Cross-Entropy loss function is adopted to train the models and the rationality of its application in the regression model is deduced from the theoretical point of view. Transformer deep learning method is based on attention mechanism, which can focus on the historical effect of Z2CN18.10 stainless steel cyclic deformation and accurately describe the uniaxial and multiaxial cyclic deformation behavior of the material.

Least Squares Projection Onto the Behavior for SISO LTI Models

We consider the least squares projection onto the behavior for linear time-invariant (LTI) single-input single-output (SISO) models, in which the observed input-output data are modified in a least squares (LS) sense by subtracting so-called misfits, so that the modified data satisfy a given linear dynamic relation. We show that the LS-criterion of the projection problem induces an orthogonal decomposition of the ambient data space and we characterize this decomposition by means of banded block-Toeplitz matrices, the elements of which are the coefficients of the difference equation describing the SISO LTI dynamics. We thereby generalize earlier results in the literature on autonomous LTI models to the more complicated SISO case. Additionally, we illustrate that the novel characterization is equivalent (up to a change of model representation) to results derived using (isometric) state-space representations in the literature on behavioral systems theory.

Developing high-dimensional machine learning models to improve generalization ability and overcome data insufficiency for mixed sugar fermentation simulation

Biorefinery can be promoted by building accurate machine learning models. This work proposed a strategy to enhance model’s generalization ability and overcome insufficient data conditions for mixed sugar fermentation simulation. Multiple inputs single output models, using initial glucose, initial xylose, and time together as inputs, have higher generalization ability than single input single output models with time as sole input in predicting glucose, xylose, ethanol, or biomass separately. Multiple inputs multiple outputs models, integrating outputs, enhanced model accuracy and resulted in an average R2 at 0.99. To overcome data insufficiency conditions, consensus yeast (CY) model, through consolidating data from 4 yeasts, obtained R2 at 0.90. By adjusting the pretrained CY model, the model can save more than 50% data and get R2 at 0.95 and 0.93 for yeast and bacterial fermentation simulation. The strategy can expand the application range and save costs of data curation for ANN models.

Stability analysis of two-terminal HVDC transmission systems using SISO open-loop gains

The ports of a three-phase voltage source converter have multiple frequency electrical parameters, which makes HVDC systems with these converters present multiple input multiple output (MIMO) characteristics. The generalized Nyquist criterion of the MIMO model is often used to analyze the system stability. However, this model is more difficult to obtain than the single-input single-output (SISO) model, and the stability margin and the oscillation frequency band are difficult to capture. To solve these problems, this paper took the two-terminal HVDC transmission system as the research object to derive a SISO model that can be measured by a port. Moreover, this study also proposed a method for analyzing the system stability through envelope SISO open-loop gains. Finally, the key port and parameters affecting the system stability were analyzed according to the stability margin, which provided theoretical support for the design of the system parameters. The established model and the proposed analysis method were verified in a MATLAB/Simulink simulation model.

Wear prediction of 3D-printed acrylonitrile butadiene styrene-carbon nanotube nanocomposites at elevated temperatures

Abstract In this study, multi-wall carbon nanotube (MWCNT) reinforced acrylonitrile butadiene styrene (ABS) nanocomposite filaments are produced. Filaments are examined through thermogravimetric analysis (TGA) and definitive scanning calorimetry (DSC) analysis. Produced nanocomposite filaments are used in the fused deposition modeling (FDM) process to manufacture parts. Wear tests are conducted on 3D-printed parts using wear test apparatus with an attached heating module under different ambient temperatures. Hence, the influence of CNT reinforcement, along with different FDM process parameters and varying test conditions on the wear behavior of 3D-printed ABS-CNT parts, are examined. Worn surfaces of the specimens are examined by scanning electron microscopy (SEM). Nonlinear autoregressive exogenous (NARX) models are proposed for the prediction of the wear behavior of 3D-printed ABS-CNT nanocomposites. While wear rate is taken as output, ambient temperature and amount of nanofiller are accounted as input parameters along with the variation of coefficient of friction (COF) which is obtained from measured frictional force and three input-one output model structure is proposed for NARX. The use of multiple input-single output (MISO) model structure and examining the wear behavior of 3D-printed ABS-CNT samples under different wear test conditions with different FDM process parameters are the novelties in this work.

A New Model Reduction Technique for the Simplification and Controller Design of Large-Scale Systems

In this article, a new method for the simplification and controller design for large-scale linear dynamic plants is proposed. This method is applicable for both large-scale single input single output (SISO) and multiple input multiple output (MIMO) models. The diminution procedure is simple and it assurances the stability of the lower-order plant provided that the higher-order system (HOS) is stable. The characteristic equation of the desired system is evaluated by using the stability equation technique and the coefficients of the numerator polynomial are calculated by applying a simple mathematical process as mentioned in the proposed scenario. This algorithm is suggested for circumventing the limitations of the stability equation scheme and preserving its important features such as stability, passivity, etc. The proposed method ensures the retention of stability and static characteristic of the higher-order plant in the reduced model. For validating the accuracy and effectiveness of the proposed technique, it is implemented on the three standard real-time systems. These systems are further used for the comparison of the proposed method with some other popular existing algorithms. By using the proposed lower-order plant, a controller is obtained for the large-scale system with the help of the moment matching technique. The design of the controller is demonstrated and validated by a real-time system taken from the literature.

Multi-port admittance model and SISO stability criterion for oscillatory stability analysis of VSC-based AC/DC interconnected renewable systems

The voltage-source converter (VSC) is physically a multi-port circuit consisting ac and dc terminals. The impedance/admittance models (IMs/AMs) of VSC are generally developed considering the frequency-couplings at its ac port, while couplings between ac and dc ports are paid less attention. The ac AM cannot directly reflect oscillatory risks between dc grid and VSC’s dc port, and vice versa. This paper proposes a multi-port admittance modeling-based framework to simplify the above modeling and analysis procedure. First, a VSC is viewed as a three-port circuit modeled by a three-dimensional admittance matrix (3DAM). Both ac and dc port dynamics are incorporated into 3DAM with couplings among ports explicitly expressed by off-diagonal elements. Then, the dc and ac grids are easily modeled into 3DAM by sequentially terminating the VSC’s three ports with grid impedances. In doing so, the three-port circuit is reduced into a one-port circuit. The principle and steps to transform the 3DAM to a single-input–single-output (SISO) equivalent form are introduced. The resulting SISO model preserves all coupling information in 3DAM and facilitates a more intuitive stability analysis procedure, where a single Nyquist curve suffices. Finally, the 3DAM and SISO model are applied to identify the dominant factors of oscillatory instability. Time-domain simulations validate the effectiveness of the proposed modeling and analysis methods.Index Terms: Admittance matrix, circuit theory, stability criterion, transfer function.

Performance analysis and precoder design for IRS-assisted MIMO system with non-linear power amplifiers

In this work, a point-to-point intelligent-reflecting-surface (IRS) assisted single input single output (SISO) model is first developed and later modified to the multiple-input multiple-output (MIMO) case. For the SISO case, the transmitter or the access point (AP) is assumed to be equipped with a non-linear memory-less power amplifier (PA). A PA is assumed to be present for each data stream at the AP or base station (BS) side for the MIMO case. For both cases, three different types of PAs (A) Soft envelope limiter (SEL), (B) Solid-state power amplifier (SSPA), and (C) Travelling wave tube amplifier (TWTA) are considered. A Bussgang decomposition of the transmitted signal is then utilized for further analysis. A modified channel model is conceived for the AP-IRS-User equipment (UE) link based on the reflecting IRS elements’ radar cross-section (RCS). For the SISO system, both perfect and imperfect geometries of IRS elements with uniformly distributed element size errors are considered. An approximate closed-form expression for the upper bound on the above system’s spectral efficiency (SE) with ideal IRS elements is derived. The expression of the SE for two special cases of (A) Infinitely large IRS and (B) Ideal PA is deduced. Approximate closed-form expressions of various quality parameters for the IRS-assisted MIMO system with ideal and non-ideal PAs are also derived and verified numerically. Maximum ratio transmission (MRT) based precoder for this system is designed assuming that the information about the BS-UE link, BS-IRS-UE link, and the non-linear PA statistics is available at the BS. A closed-form expression for the SE of this system with this precoder is then derived and verified for various parametric situations.

Experimental Modelling of an Isolated WECfarm Real-Time Controllable Heaving Point Absorber Wave Energy Converter

To offer point absorber wave energy converters (WECs) as a bankable product on the marine renewable energy market, multiple WECs will be installed together in an array configuration. The wave energy community (research and industrial) has identified the urgent need for available realistic and reliable data on WEC array tests in order to perform a better WEC array optimization approach and in order to validate recently developed (non-linear) numerical models. The ‘WECfarm’ project is initiated to cover this scientific gap on necessary experimental data. The ‘WECfarm’ experimental setup consists of an array of five generic heaving point-absorber WECs. The WECs are equipped with a permanent magnet synchronous motor (PMSM), addressing the need for WEC array tests with an accurate and actively controllable power take-off (PTO). The WEC array control and data acquisition are realized with a Speedgoat Performance real-time target machine, offering the possibility to implement advanced WEC array control strategies in the MATLAB-Simulink environment. The presented article describes the experimental setup, the performed tests and the results of the test campaign using a single, isolated ‘WECfarm’ WEC in April 2021 at the wave basin of Aalborg University (AAU), Denmark. A Coulomb and viscous friction model is determined to partly compensate for the drivetrain (motor, gearbox, rack and pinion) friction. A system identification (SID) approach is adopted considering the WEC system to be composed of two single input single output (SISO) models, the radiation and the excitation model. Radiation tests yield the intrinsic impedance. Excitation tests yield the excitation frequency response function. Adopting an impedance matching approach, the control parameters for the resistive and reactive controller are determined from the complex conjugate of the intrinsic impedance. Both controllers are tested for a selection of regular wave conditions. The performed experimental test campaign using an isolated ‘WECfarm’ WEC allows a full evaluation of the WEC design prior to extending the setup to five WECs. Within the ‘WECfarm’ project, an experimental campaign with a five-WEC array in the Coastal and Ocean Basin (COB) in Ostend, Belgium, is under preparation.

Prediction of flavor of Maillard reaction product of beef tallow residue based on artificial neural network.

The beef flavor of beef tallow residue was improved by enzymatic hydrolysis followed by the Maillard reaction, and the flavor could be predicted using an artificial neural network. Five beef tallow residue hydrolysates were prepared using different enzymes. The Flavourzyme and Papain (FP) hydrolysate had low molecular weight peptides and high degree of hydrolysis and free amino acid content. We identified 49 main compounds, including aldehydes, pyrazines, and furan. Furan and pyrazine were the dominant volatile compounds in the five beef tallow residue-derived Maillard reaction products (MRPs), and their profiles and levels in the FP MRPs were high. The FP MRPs had the best sensory characteristics. The artificial neural network analysis revealed that the multiple input single output model had a better performance than the single input single output model, and the prediction accuracy was>90%, indicating that the MRPs sensory evaluation scores could be accurately predicted.

A Multi-Frequency Input-Admittance Model of Locomotive Rectifier Considering PWM Sideband Harmonic Coupling in Electrical Railways

Electrical railway harmonic instability issues are common in the high-frequency range. The effective frequency of the traditional converter’s small-signal averaging model is below 1/2 switching frequency since the pulse width modulation (PWM) sideband harmonic components are ignored. In this article, the dynamic propagations of perturbation frequency and the generated PWM sideband components are constructed first. Then the locomotive rectifier’s multi-frequency input-admittance model is derived appropriately. Afterward, an admittance conversion approach is used to convert the multi-frequency model into the single-input–single-output (SISO) model whereas retaining the sideband frequency couplings. The proposed SISO model is more accurate than the traditional small-signal averaging model in the frequency range higher than 1/2 switching frequency. It is found that PWM sideband harmonics dominate the locomotive rectifier’s input-admittance characteristic higher than 1/2 switching frequency. Finally, based on the proposed model, the influence of different switching frequencies, control bandwidths, and traction network impedance on system harmonic stability is revealed by the hardware-in-the-loop (HIL) results.

Interactions analysis and quadrature voltage compensation control for stabilizing LCC-HVDC system with multiple STATCOMs

Instability may occur in LCC-HVDC system when multiple STATCOMs operate simultaneously at the inverter station. To deeply investigate this problem, firstly a structured linear model of LCC-HVDC system with STATCOMs is presented. Then, a single-input single-output (SISO) form of the model is obtained. Based on the movement of dominant poles (MODPs) of the SISO model, the interactions at the receiving end of the system are investigated. The results reveal that the interactions between LCC and STATCOMs mainly affect the high-frequency (HF) characteristics of the system. With the increase in the number of online STATCOMs, the negative effects of the interactions on the system HF stability gradually increase, while that on low-frequency (LF) stability become weaker. In order to improve such negative interactions, a quadrature voltage compensation control (QVCC) of STATCOM is proposed in this paper. This method introduces the q-axis AC voltage into the constant AC voltage control (CAVC). The theoretical analysis indicates that the QVCC can enhance the stability of the LCC-HVDC system with multiple STATCOMs, which is verified by the simulation results conducted in PSCAD/EMTDC. Finally, the intuitive and physical explanations on the mechanism of instability caused by increasing number of online STATCOMs are given from two perspectives.

Cooperative Spectrum Sensing under SISO and MIMO Environment for Cognitive Radio Networks

Background: Increasing demand for wireless devices leads to the efficient use of spectrum resources. Despite the limited availability of spectrum resources, all licensed users do not utilize their spectrum all the time. Objective: Cognitive Radio (CR) is used to detect free channels in the available spectrum resources and access them dynamically without interfering with licensed users. Various spectrum sensing (SS) techniques are available to sense the spectrum of licensed users. Methods: The spectrum sensing technique, energy detector (ED), is analysed under a Rayleigh fading channel. Further, Single Input Single Output (SISO) and Multiple Input Multiple Output (MIMO) channel models are developed in the consumer environment. The ED technique is further analysed with cooperative spectrum sensing (CSS) to overcome detection problems for a single CR user. Results: The ED achieves a probability of detection (PD) of 0.9 and a probability of false alarm (PFA) of 0.1 at the lowest SNR of −20 dB, which is the requirement for a standard IEEE 802.22 Wireless Regional Area Network (WRAN). Conclusion: Various receiver operating characteristics are determined by conducting practical simulations and theoretical calculations for each SISO and MIMO channel model with and without the CSS scenario to achieve the target PD and PFA .

Improved MIMO Modeling and Enhanced Transient Performance of Phase-Locked Loop During Grid Fault

A phase-locked loop (PLL) is commonly used in grid-connected power converter for synchronization. A single input single output (SISO) linear model of PLL with the phase angle of the point of common coupling (PCC) voltage as input and the estimated phase angle as the output is generally used in the PLL analysis and design. However, an undesirable coupling between the magnitude and phase of the input voltage is present when a filtering stage is incorporated before the control loop, which a SISO model cannot capture, and could result in a significant disturbance in the PLL estimated phase during grid faults. This paper proposes a generalized multi-input multi-output (MIMO) linear model that captures the complete PLL dynamics during symmetrical faults for a PLL equipped with any prefilter. Furthermore, a compensation method that decouples the magnitude dynamics of the input voltage from the phase dynamics is proposed in this paper. The proposed compensation improves the PLL's phase tracking performance by ensuring that the prefiltered PLL acts only on the PCC voltage phase changes. A vital application of the proposed compensation method for PLL is during a grid fault case, wherein the converters are expected to contribute to the fault current, which requires an accurate measurement of the PCC voltage phase. The compensation method's effectiveness is demonstrated using power hardware in the loop simulation of a hardware voltage source converter interfaced to a distribution system simulated in real-time.

Increased Robustness to Delay in Incremental Controllers Using Input Scaling Gain

The main advantage of the incremental dynamics formulation is its property of replacing state-dependent aspects of the system dynamics by measurements, solely requiring modeling the input-dependent dynamics. However, incremental controllers assume that state derivative measurements are available. The lack of accurate sensors and communication delay may appear as limitations for the use of incremental techniques. This article applies a second-order differentiator (SOD) for estimating state derivatives and attenuate measurement noise, and an input scaling gain (ISG) for attenuating the effects of high-frequency noise and delay in the feedback loop. A complete analysis of the combined solution is carried out using an incremental nonlinear dynamic inversion (INDI) strategy. First, a simple linear single-input single-output model is used to evaluate the influence of design parameters in the closed-loop response and stability. Then, the combined SOD and ISG approach is illustrated in a more complex case application, where INDI is used to control the lateral motion of an autonomous airship, results of which corroborate the ISG as an asset to increase the maximum delay allowed in the feedback loop.

Low-Frequency Oscillation Analysis in Train-Traction Power Supply System Using an SISO Voltage Loop Model

Low-frequency oscillation (LFO) in the train-grid coupling system severely endangers the stability and safety of railway operation. It is well known that this phenomenon is caused by impedance mismatch among the trains and the traction power supply system (TPSS). A novel voltage loop model of the single-phase pulsewidth modulation rectifier is proposed in this article. Unlike the commonly adopted multi-input multi-output (MIMO) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> impedance model in the literature, this is a single-input single-output (SISO) model that enjoys some advantages, such as easier calculation of the system poles and quantitative analysis of the stability margin. The most attractive character of this model in comparison to the MIMO one is perhaps the decoupling of the controller and the voltage loop. This paves the way toward the analytical design of the dc-link voltage controller (DVC), whereas the impedance model is mainly useful for analysis. Furthermore, the maximum amount of trains allowed for keeping stability is derived from the steady-state equation. Robust stability margin (RSM) and sensitivity analysis are adopted to quantitatively investigate the influence of the control parameters and the circuit element parameters. LFO is guaranteed to be eliminated permanently by adjusting the controller parameters.

Unified SISO Loop Gain Modeling, Measurement, and Stability Analysis of Three-Phase Voltage Source Converters

Frequency-domain modeling is an effective technique in the dynamic analysis of power electronic converters-based power systems. In this paper, a unified single-input single-output (SISO) loop gain modeling for the three-phase grid-tied voltage source converters (VSCs) under both symmetric and asymmetric AC grids is presented, which facilitates the physical measurement and stability analysis. Based on the linear-time-periodic (LTP) modeling technique, the harmonic admittance model of the three-phase grid-tied VSC is developed in the stationary ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">αβ</i> )-frame, which accurately captures the coupling dynamics. This model can be transformed into other existing models through coordinate transformations. According to the idea of mathematical induction, a two-by-two recursive admittance matrix (RAM) model is derived, which is easy to include frequency coupling components of arbitrary order. It is illustrated that the VSC admittance is coupled with the grid admittance due to the frequency coupling effect (FCE). Furthermore, the RAM is converted to its equivalent SISO models following the concept of loop gain. The system stability is thus assessed by the SISO stability criteria (e.g., Nyquist stability criterion). In addition, the loop gain allows the traditional SISO perturbation and measurement scheme to be used for detecting the stability margin information. Finally, simulation results verify the feasibility and correctness of the theoretical analysis presented above.

Evaluation of engine performance and emission of African pear seed oil (APO) biodiesel and its prediction via multi‐input‐multi‐output artificial neural network (ANN) and sensitivity analysis

AbstractThe evaluation of engine performance and emission of biodiesel produced from African pear seed oil (APO) and its prediction using multi‐input‐multi‐output (MIMO) artificial neural network (ANN) technique was studied. A standard diesel test bed was used to carry out the evaluation using petrol‐diesel, biodiesel, and its blends, operating at various engine revolutions per minute. Sensitivity analysis used the connection weight method. The performance results showed that petro‐diesel blended with a small amount of APO methyl ester (B20) improved the engine efficiency and emissions better than only petrol‐diesel. The engine performance was predicted well, with the MIMO model having better correlation coefficients of 0.99779, 0.99993, and 0.99774 on training, validation, and testing, respectively, than the Multiple input Single Output (MISO) model. The desired outputs compared between their measured and simulated values exhibited a very low mean square error of 0.0000014488. The blend had the highest relative impact (62.08%) on the output variables. The MIMO model has therefore shown the potential to adopt two input variables equally in predicting the engine performance of biodiesel rather than only complex variables. © 2021 Society of Chemical Industry and John Wiley &amp; Sons, Ltd

A Systematic Optimization Design of New Constrained Robust Model Predictive Fault-Tolerant Control for Industrial Processes

A comprehensive optimization design method of new constrained robust model predictive fault-tolerant control is proposed for industrial processes with partial actuator failures and unknown disturbances. A new state space model which is composed of differential state variables, output tracking errors, and new state variables related to output tracking errors is first constructed according to the multiple single-input single-output (SISO) models. Considering the uncertainties existing in the system, the reproduced model is represented by a discrete switched model with polytopic uncertainties. Subsequently, the model predictive controller with min-max optimization is designed to ensure the performance improvement and solve the drawback that the controller gain is not adjustable. To further enhance the system performance, the designed controller is optimized in two steps based on the switching strategy. In addition, the corresponding switching strategy is designed to realize the mutual switching between different models of the system. Finally, taking continuous stirred tank and injection molding processes as examples, the effectiveness of the proposed method is verified by comparing with the traditional control method.

Nonlinear Gain Position Control Using Only Position Feedback for Permanent Magnet Stepper Motors

This article proposes a robust nonlinear position control for permanent magnet stepper motors. We develop a new single-input single-output model that consists of position, velocity, and acceleration using a commutation scheme to lump a system function and an external disturbance into a disturbance. An augmented observer is designed to estimate the position, velocity, acceleration, and disturbance. Because the disturbance refers to external disturbance, acceleration dynamics, and parameter uncertainties, it is difficult to accurately estimate disturbance without the high observer gain. It may result in the degradation of the position tracking performance. The nonlinear controller is then developed using backstepping to suppress the position tracking error using the input-to-state stability property. The key innovation of the proposed method is the design of the nonlinear gain to suppress the position tracking error according to the disturbance estimation error. Thus, the use of the high observer gain to accurately estimate the disturbance can be avoided. The proposed control algorithm was experimentally verified using the PM stepper motor control system and can be easily implemented in real-time using position measurements only.