Input-output Transfer Function Research Articles

Performance analysis of a robust MF-PTC strategy for induction motor drive

The load model-based conventional predictive torque control (PTC) strategy performs as a high-performance controller at transient and steady-state conditions. However, its performance is poor on the issue of parameters variation. A robust model-free PTC (MF-PTC) strategy has been proposed in this paper to overcome the aforementioned drawback. An auto-regressive exogenous (ARX) model instead of a load model has been used to establish a model-free controller. This model is usually formed based on their input-output transfer function. A recursive least square algorithm has been employed to estimate the unknown parameters of the ARX model. Then, an observable canonical state-space model uses those estimated parameters to achieve an accurate prediction of the control variables. The performance of the proposed scheme can be affected by the variation of resistance. A resistance estimator based on the model reference adaptive system observer has been applied to improve the robustness of the system against variation of the stator and rotor resistance, and inductance measurement uncertainty. Simulation results show that the proposed MF-PTC scheme is robust against parameters uncertainty and works well at transient and steady-state conditions.

Structural and functional specializations of human fast-spiking neurons support fast cortical signaling.

Fast-spiking interneurons (FSINs) provide fast inhibition that synchronizes neuronal activity and is critical for cognitive function. Fast synchronization frequencies are evolutionary conserved in the expanded human neocortex despite larger neuron-to-neuron distances that challenge fast input-output transfer functions of FSINs. Here, we test in human neurons from neurosurgery tissue, which mechanistic specializations of human FSINs explain their fast-signaling properties in human cortex. With morphological reconstructions, multipatch recordings, and biophysical modeling, we find that despite threefold longer dendritic path, human FSINs maintain fast inhibition between connected pyramidal neurons through several mechanisms: stronger synapse strength of excitatory inputs, larger dendrite diameter with reduced complexity, faster AP initiation, and faster and larger inhibitory output, while Na+ current activation/inactivation properties are similar. These adaptations underlie short input-output delays in fast inhibition of human pyramidal neurons through FSINs, explaining how cortical synchronization frequencies are conserved despite expanded and sparse network topology of human cortex.

Addressing Load Sensitivity of Rational Macromodels

Behavioral models are effective tools used to relieve the computational burden of large-scale system-level simulations. In electrical and electronic applications, the Vector Fitting (VF) iteration often represents the algorithm of choice for generating low order equivalent circuits for complex multiport components in a data-driven setting. Although accurate and reliable in general, macromodels generated via VF are inherently represented in terms of a rational approximation of one specific input-output transfer function of the structure under modeling, e.g., its scattering matrix. However, accuracy in the scattering representation does not necessarily imply a good accuracy when solving the macromodel in a system-level setting, under different termination conditions. In fact, the sensitivity of the macromodel with respect to its loading conditions may be large and needs to be addressed and controlled. In this work, we present a modified VF scheme that overcomes this issue, by introducing in the rational approximation algorithm the requirement that the macromodel remains accurate when interconnected with a known class of admissible networks. The proposed formulation is based on an augmentation of the cost function minimized at each VF iteration; further, it does not require additional expensive data gathering steps when compared to standard approaches. The effectiveness of the scheme is tested over a set of relevant examples, in particular for Power Integrity applications.

Designing Optimal Linear Detectors: A Bottom-Up Approach

This paper develops a systematic approach to realizing linear detectors with an optimized sensitivity, allowing for the detection of extremely weak signals. First, general constraints are derived on a specific class of input-output transfer functions of a linear detector. Then a physical realization of transfer functions in that class is found using the quantum network synthesis technique, which allows for the inference of the physical setup directly from the input-output transfer function. By exploring a minimal realization which has the minimum number of internal modes, it is shown that the optimal such detectors are internal squeezing schemes. Then, investigating nonminimal realizations, which is motivated by parity-time symmetric systems, a quantum nondemolition measurement is systematically recovered.

An Identification Approach for Descriptor Systems

This letter proposes a novel identification approach for linear time-invariant descriptor systems based on input-state-output data. Under the assumption of regularity, the equivalent inverse form of the descriptor system is firstly introduced. Then two groups of data experiments are designed, and corresponding input, state and output data are collected. Furthermore, the parameters of the inverse form are uniquely identified based on those data sets. Since the inverse form possesses the same state, input, output signals and input-output transfer function as the original descriptor system, the identification is completed. Finally, an electrical circuit system is provided to illustrate the effectiveness of our method.

Online Transfer Function Estimation and Control Design Using Ambient Synchrophasor Measurements

This paper proposes an adaptive control design framework for damping inter-area oscillations in power systems. The design is entirely based on ambient synchrophasor measurements. The proposed framework consists of three components, namely, input-output transfer function estimation, channel selection, and feedback control implementation. For the first part, we propose a simple yet effective novel estimation algorithm in the frequency domain to identify low-order transfer functions in pre-specified frequency ranges between the measured input and output data using ambient synchrophasor measurements. In the second part, based on the identified transfer functions, the joint controllability-observability (JCO) of all identified channels is estimated and most suitable control candidates for damping the target inter-area mode are selected. Finally, an appropriate lead-lag controller is designed using a classical frequency-domain method to improve the damping of a dominant oscillatory inter-area mode. Since ambient data is used in the analysis, the selected channel as well as the designed controller parameters can be updated online whenever the system operating point changes resulting in an efficient adaptive controller. The effectiveness of the proposed framework is illustrated by implementing it on the two-area Kundur test system and the IEEE 39-bus test system.

Mapping of Dynamics Between Mechanical and Electrical Ports in SG-IBR Composite Grids

Power grids are traditionally dominated by synchronous generators (SGs) but are currently undergoing a major transformation due to the increasing integration of inverter-based resources (IBRs). The state space method with transparent apparatus models can be readily used. However, models of IBRs are usually not disclosed by manufacturers. Alternatively, the port-based approach represents dynamics by input-output transfer functions without exposing internal states. These transfer functions at various ports are normally configured with a particular focus: an SG-dominated grid is traditionally analyzed in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mechanical-centric</i> view which ignores fast electrical dynamics and focuses on the torque-speed dynamics, whereas the emergent IBR-dominated grid usually takes an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">electrical-centric</i> view which focuses on the voltage-current interaction. In this article, a new perspective called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">port-mapping method</i> is proposed to combine these two views. Specifically, the mechanical dynamics are mapped to the electrical impedance seen at the electrical port; and the electrical dynamics are also mapped to the torque coefficient seen at the mechanical port. The bidirectional mapping gives additional flexibility and insights to analyze the sub-system interactions in whole-system dynamics and guide the tuning of parameters. Application of the proposed method is illustrated in three cases with increasing scales.

Analysis of Non-Minimum Phase System for AC/DC Battery Charger Power Factor Correction Converter

Electric mobility is nowadays one of the more important trends regarding pollution reduction and global warming due to fuel consumption. Big efforts are done in order to develop efficient and reliable power electronic systems for electric vehicles. In two stage on board-battery chargers, one way of improving efficiency is by means of ensuring the DC-DC isolated converter always operates in the nominal input/output voltage ratio, that could be achieved with a variable DC-link operation. In this paper, a four-switch buck-boost based AC/DC converter is deeply analyzed in order to improve its dynamic performance, the power factor and the total harmonic distortion. The converter suffers from a non-minimum phase characteristic in different input–output transfer functions, which reduces the closed-loop bandwidth of the system. Therefore, after a deep converter analysis has been done, different solutions have been evaluated and tested. Finally, a control to different output transfer functions of the converter become minimum phase, which allows us to increase the system bandwidth and, consequently, high power factor, low harmonics distortion, single control structure and fast dynamics for wide output voltage range are achieved.

Input-output $ L^2 $-well-posedness, regularity and Lyapunov stability of string equations on networks

&lt;p style='text-indent:20px;'&gt;We consider the general networks of elastic strings with Neumann boundary feedbacks and collocated observations in this paper. By selecting an appropriate multiplier, we show that this system is input-output &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ L^2 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-well-posed. Moreover, we verify its regularity by calculating the input-output transfer function of system. In the end, by choosing an appropriate multiplier, we give a method to construct a Lyapunov functional and prove the exponential decay of tree-shaped networks with one fixed root under velocity feedbacks acted on all leaf vertices.&lt;/p&gt;

Mapping the dynamic transfer functions of eukaryotic gene regulation.

Biological information can be encoded within the dynamics of signaling components, which has been implicated in a broad range of physiological processes including stress response, oncogenesis, and stem cell differentiation. To study the complexity of information transfer across the eukaryotic promoter, we screened 119 dynamic conditions-modulating the pulse frequency, amplitude, and pulse width of light-regulating the binding of an epigenome editor to a fluorescent reporter. This system revealed tunable gene expression and filtering behaviors and provided a quantification of the limit to the amount of information that can be reliably transferred across a single promoter as ∼1.7 bits. Using a library of over 100 orthogonal chromatin regulators, we further determined that chromatin state could be used to tune mutual information and expression levels, as well as completely alter the input-output transfer function of the promoter. This system unlocks the information-rich content of eukaryotic gene regulation.

Digital Non-Linearity Calibration for ADCs With Redundancy Using a New LUT Approach

This paper presents a novel Look-up Table (LUT) calibration technique for static non-linearity compensation in analog-to-digital converters (ADCs) with digital redundancy, such as Successive Approximation Register (SAR), Algorithmic, Sub-ranging or Pipeline ADCs. The method compensates the performance limitations of the conventional LUT approach in presence of comparison noise and/or non-monotonicity. In these circumstances, the input-output transfer function of a redundant ADC becomes significantly multivalued - that is, different output codes can be achieved for the same input level at different time instants. This behavior is motivated because from sample to sample, in a design with redundancy, the processing signal path is not unique, causing that the error under calibration becomes time-dependent, something which is not contemplated in the conventional calibration model. To deal with this effect, this work proposes a digital low-cost post-processing of the standardized Integral-Non-linearity (INL), which resolves multivalued situations using a direct access to the internal redundant codes. The method improvements are validated by realistic SAR and Pipeline ADC case studies at behavioral level, and by experimental data from an 11-bit 60Msps Pipeline ADC implemented in a 130nm CMOS process. These experimental results show that the proposed calibration achieves an improvement of approximately 1.6 effective bits at full-scale input amplitude.

Mapping the Dynamic Transfer Functions of Epigenome Editing

Biological information can be encoded in the dynamics of signaling components which has been implicated in a broad range of physiological processes including stress response, oncogenesis, and stem cell differentiation. To study the complexity of information transfer across the eukaryotic promoter, we screened 119 dynamic conditions — modulating the frequency, intensity, and pulse width of light — regulating the binding of an epigenome editor to a fluorescent reporter. This system revealed highly tunable gene expression and filtering behaviors and provided the most comprehensive quantification to date of the maximum amount of information that can be reliably transferred across a promoter as ~1.7 bits. Using a library of over 100 orthogonal epigenome editors, we further determined that chromatin state could be used to tune mutual information and expression levels, as well as completely alter the input-output transfer function of the promoter. This system unlocks the information-rich content of eukaryotic epigenome editing.

A Spurless and Wideband Continuous-Time Electro-Optical Phase Locked Loop (CT-EOPLL) for High Performance LiDAR

Frequency-modulated continuous-wave (FMCW) LiDAR systems are drawing increasing interest due to their potential applications in autonomous driving, machine perception, rapid prototyping, and medical diagnostics. The nonlinearity of a laser’s input-output transfer function can degrade the performance of an FMCW LiDAR. However, traditional discrete-time electro-optical phase-locked loops (DT-EOPLLs) face an unfavorable trade-off between chirp bandwidth and Mach-Zehnder delay. We present an integrated continuous-time electro-optic phase-locked loop (CT-EOPLL) to address this problem. The proposed EOPLL is very wideband, with its loop bandwidth equal to its reference frequency. This feature enables it to relax the trade-off between chirp bandwidth and Mach-Zehnder (MZ) delay by 10× in dB scale, which consequently reduces the area and loss associated with the silicon photonic delay implementation. It also does not suffer from the challenging issue of spurs in wideband PLLs because it features image and harmonic spur suppression in the loop using single-sideband (SSB) and harmonic-reject (HR) mixing techniques. The electrical part of this EOPLL is implemented in 65nm CMOS technology, and its optical integrated circuit is fabricated using a silicon photonic process. Featuring more than 25dB of suppression of the highest spur, this EOPLL is utilized in a high precision LiDAR sensor that shows an RMS depth precision of 558μm at 2m distance, and a 9.4mm RMS depth resolution at ranges exceeding 3.3m.

S100β-mediated astroglial control of firing and input processing in layer 5 pyramidal neurons of the mouse visual cortex.

Inputs impinging on layer 5 pyramidal neurons perform essential operations as these cells represent one of the most important output carriers of the cerebral cortex. However, the contribution of astrocytes, a type of glial cell, to these operations is poorly documented. Here we found that optogenetic activation of astrocytes in the vicinity of layer 5 in the mouse primary visual cortex induces spiking in local pyramidal neurons through Nav1.6 ion channels and prolongs the responses elicited in these neurons by stimulation of their distal inputs in cortical layer 1. This effect partially involved glutamatergic signalling but relied mostly on the astrocytic calcium-binding protein S100β, which regulates the concentration of calcium in the extracellular space around neurons. These findings show that astrocytes contribute to the fundamental computational operations of the cortex by acting on the ionic environment of neurons. The most complex cerebral functions are performed by the cortex, whose most important output is carried out by its layer 5 pyramidal neurons. Their firing reflects integration of the sensory and contextual information that they receive. There is evidence that astrocytes influence cortical neuron firing through the release of gliotransmitters such as ATP, glutamate or GABA. These effects have been described at the network and at the synaptic levels, but it is still unclear how astrocytes influence neuron input-output transfer function at the cellular level. Here, we used optogenetic tools coupled with electrophysiological, imaging and anatomical approaches to test whether and how astrocytic activation affected processing of distal inputs to layer 5 pyramidal neurons (L5PNs). We show that optogenetic activation of astrocytes near L5PN cell body prolonged firing induced by distal inputs to L5PNs and potentiated their ability to trigger spikes. The observed astrocytic effects on L5PN firing involved glutamatergic transmission to some extent but relied mostly on release of S100β, an astrocytic Ca2+ -binding protein that decreases extracellular Ca2+ once released. This astrocyte-evoked decrease in extracellular Ca2+ elicited firing mediated by activation of Nav1.6 channels. Our findings suggest that astrocytes contribute to the cortical fundamental computational operations by controlling the extracellular ionic environment.

Larin Parameterization to Solve the Problem of Analytical Construction of the Optimal Regulator of Oscillatory Systems with Liquid Dampers

The problem of the analytical construction of the optimal regulator of oscillatory systems with liquid ‎dampers on the complex plane is considered. Since the fractional derivative is included in the differential ‎equation describing the oscillatory systems with liquid dampers movement, the corresponding input-output ‎transfer function also contains fractional rational orders, the general Larin parameterization scheme is ‎modifying for this case. The results are illustrated by numerical examples and it is shown that they coincide ‎with Letov’s А.М. analytical construction of the optimal regulator.

Advanced Impedance-Based Control Design for Decoupling Multi-Vendor Converter HVDC Grids

A progressive interconnection of existing HVDC links to form grids and the development of completely new HVDC grids from different vendors are expected shortly. One of the current challenges of such endeavour is unintended interactions due to independently designed controllers. This article proposes a design methodology for decentralized controllers to mitigate such interactions in multi-vendor voltage source converter (VSC)-HVDC grids. The approach presented relies on the unique stand-alone input-output impedance transfer function of each VSC, and the global impedance transfer function as seen from each terminal after interconnection with other VSCs. Subsequently, network-level controllers are designed by attempting to match the global responses at selected locations based on a novel interaction analysis, to the unique transfer function model of the vendors at the corresponding location. This approach reduces the entire problem to an impedance matching problem. We demonstrate the efficacy and flexibility of both the methodology and the designed controllers in mitigating interactions due to the independent design of VSC controllers through nonlinear simulations on a four-terminal droop controlled HVDC grid.

Investigation on Dynamic Response of Grid-Tied VSC During Electromechanical Oscillations of Power Systems

This study focuses on the dynamics of a grid-tied voltage source converter (GVSC) during electromechanical oscillations. A small-signal model with GVSC port variables (DC voltage and AC power) as the outputs and a terminal voltage vector as the input is derived to reveal the passive response of the GVSC on the basis of the power equation in the d–q coordinate system. An input–output transfer function matrix is constructed according to the proposed model. The frequency response of this matrix in the electromechanical bandwidth is described to reflect the dynamic behavior of the GVSC. The effects of the operation parameters, i.e., the grid strength, reference value of the control system, and grid voltage, on the dynamic behavior of the GVSC in the electromechanical bandwidth, are investigated using frequency domain sensitivity. Analysis results show that the GVSC generates responses with respect to the electromechanical mode. These responses have different sensitivities to the operation parameters. The IEEE 10-machine power system simulation is performed, and the power hardware-in-the-loop platform with the GVSC was applied to validate the analysis.

Data-driven inference of physical devices: theory and implementation

Given a physical device as a black box, one can in principle fully reconstruct its input–output transfer function by repeatedly feeding different input probes through the device and performing different measurements on the corresponding outputs. However, for such a complete tomographic reconstruction to work, full knowledge of both input probes and output measurements is required. Such an assumption is not only experimentally demanding, but also logically questionable, as it produces a circular argument in which the characterization of unknown devices appears to require other devices to have been already characterized beforehand. Here, we introduce a method to overcome such limitations present in usual tomographic techniques. We show that, even without any knowledge about the tomographic apparatus, it is still possible to infer the unknown device to a high degree of precision, solely relying on the observed data. This is achieved by employing a criterion that singles out the minimal explanation compatible with the observed data. Our method, that can be seen as a data-driven analog of tomography, is solved analytically and implemented as an algorithm for the learning of qubit channels.

Large-scale dynamic modeling of task-fMRI signals via subspace system identification

Objective. We analyze task-based fMRI time series to produce large-scale dynamical models that are capable of approximating the observed signal with good accuracy. Approach. We extend subspace system identification methods for deterministic and stochastic state-space models with external inputs. The dynamic behavior of the generated models is characterized using control-theoretic analysis tools. To validate their effectiveness, we perform a probabilistic inversion of the identified input–output relationships via joint state-input maximum likelihood estimation. Our experimental setup explores a large dataset generated using state-of-the-art acquisition and pre-processing methods from the Human Connectome Project. Main results. We analyze both anatomically parcellated and spatially dense time series, and propose an efficient algorithm to address the high-dimensional optimization problem resulting from the latter. Our results enable the quantification of input–output transfer functions between each task condition and each region of the cortex, as exemplified by a motor task. Further, the identified models produce impulse response functions between task conditions and cortical regions that are compatible with typical hemodynamic response functions. We then extend subspace methods to account for multi-subject experimental configurations, identifying models that capture common dynamical characteristics across subjects. Finally, we show that system inversion via maximum-likelihood allows the time-of-occurrence of the task stimuli to be estimated from the observed outputs. Significance. The ability to produce dynamical input–output models might have an impact in the expanding field of neurofeedback. In particular, the models we produce allow the partial quantification of the effect of external task-related inputs on the metabolic response of the brain, conditioned on its current state. Such a notion provides a basis for leveraging control-theoretic approaches to neuromodulation and self-regulation in therapeutic applications.

Tune it in: mechanisms and computational significance of neuron-autonomous plasticity.

The activity of a neural network is a result of synaptic signals that convey the communication between neurons and neuron-based intrinsic currents that determine the neuron's input-output transfer function. Ample studies have demonstrated that cell-based excitability, and in particular intrinsic excitability, is modulated by learning and that these modifications play a key role in learning-related behavioral changes. The field of cell-based plasticity is largely growing, and it entails numerous experimental findings that demonstrate a large diversity of currents that are affected by learning. The diverse effect of learning on the neuron's excitability emphasizes the need for a framework under which cell-based plasticity can be categorized to enable the assessment of the computational roles of the intrinsic modifications. We divide the domain of cell-based plasticity into three main categories, where the first category entails the currents that mediate the passive properties and single-spike generation, the second category entails the currents that mediate spike frequency adaptation, and the third category entails a novel learning-induced mechanism where all excitatory and inhibitory synapses double their strength. Curiously, this elementary division enables a natural categorization of the computational roles of these learning-induced plasticities. The computational roles are diverse and include modification of the neuronal mode of action, such as bursting, prolonged, and fast responsive; attention-like effect where the signal detection is improved; transfer of the network into an active state; biasing the competition for memory allocation; and transforming an environmental cue into a dominant cue and enabling a quicker formation of new memories.