Sufficient Excitation Research Articles

Research on lateral vibration coupling mechanism of high-speed train based on singular coefficients

The lateral vibration coupling mechanism is the basic law of the current vibration behavior of high-speed train. The main reason is that there are design defects in the prototype of ICE3 bogie. Therefore, the passenger dedicated line is not a necessary and sufficient technical condition for ensuring the stability and safety of high-speed railway operation. Based on the previous research, this article further develops the theoretical demonstration and experimental verification of the lateral vibration coupling mechanism of high-speed train. According to the concept of system stiffness and the statistical characteristics of Gaussian process, a new concept of singular coefficient is proposed to describe non-stationary and non-Gaussian processes. Combined with the quality vibration tracking test of a high-altitude vehicle, the conclusions are in good agreement through the experimental testing and the dynamic simulation. The application of the singular coefficient further gives three conditions for determining the lateral vibration coupling mechanism of high-speed train, namely sufficient excitation energy, vibration transmission medium and coupling resonance possibility. Aiming at the influence of high-order modal interception and residual force of flexible body, the new concept of singular coefficient is applied. The rigid-flexible coupling simulation reflects the main statistical characteristics of non-stationary and non-Gaussian processes, and reveals the current lateral vibration coupling mechanism of high-speed train.

A Numerical Investigation of the Vibration Effect on Interactions in a Gas Bubble Ensemble and Solid Particles in a Liquid

Abstract—It is known that linearly polarized translational vibrations can multiply the capture cross section of deposited solid particles by a single gas bubble suspended in a liquid. This fact is of great theoretical and practical importance for using vibrations to intensify the flotation process, which, however, involves multiple rising bubbles. The present work studied the capture of solid particles by a gas bubble ensemble in liquid exposed to high-frequency small amplitude vibrations in two-dimensional formulation numerically. The numerical solution was obtained using ANSYS Fluent software. The obtained fields of the averaged and pulsating components of the flow velocity were then processed by our own code in order to calculate the trajectories of small particles from a “cloud” located in the computational domain. The description of forces acting on the particle took the added mass of the fluid, gravity, the Archimedean force, Stokes force, Basset force, and the vibrational force due to the inhomogeneity of the pulsating field into account. The calculations were performed for millimeter air bubbles in water with particle characteristics significant for the flotation process. The influence of vibrations is shown to be significantly attenuated within the bubble ensemble due to the “screening” effect, which peaks at the highest vibration frequency and depends weakly on the distance between the bubbles. However, vibrations of a lesser frequency, at which the gas in bubbles is compressible, are still able to penetrate deep into the liquid volume at a sufficient excitation intensity, and thereby increase the particle capture region.

Development of a Low-Cost, Portable NQR Spectrometer for RDX Explosives Detection

Nuclear quadrupole resonance (NQR) technology is a promising approach to detect so-called “minimum metal” landmines, as it can look directly for their explosive content. Conventional commercially available NQR devices, however, are large and expensive, and they require a transmitter power amplifier with a power generator, which is not suitable for outdoor use and mass production. Here we present a small and inexpensive portable device developed for detecting landmines using NQR. The device uses a field-programmable gate array (FPGA) and low-impedance transmission and reception circuits that include a dual supply class-D power amplifier powered by conventional batteries to ensure sufficient magnetic field excitation for mine detection. The pulse width modulation signals that are fed into the power amplifier have been designed to protect the low impedance transmit-receive switch circuit from the high voltage. The system has been tested successfully in the laboratory with 100g of explosive RDX at a distance of 10cm from the antenna, corresponding to a plausible anti-personal (AP) mine scenario. Detection was achieved with a signal to noise ratio (SNR) ~2 in 2/3 of the time that a previous prototype required (120 sec). Moreover, the new device can detect RDX explosives with a measurement time and SNR comparable to mine detectors built with commercial-off-the-shelf (COTS) components, but at lower cost and smaller form factor.

Wideband Magnetic Excitation System for Atomic Force Microscopy Cantilevers with Megahertz-Order Resonance Frequency

Small cantilevers with a megahertz-order resonance frequency provide excellent sensitivity and speed in liquid-environment atomic force microscopy (AFM). However, stable and accurate oscillation control of a small cantilever requires the photothermal excitation, which has hindered their applications to the studies on photo-sensitive materials. Here, we develop a magnetic excitation system with a bandwidth wider than 4 MHz, enabling a light-free excitation of small cantilevers. In the system, a cantilever with a magnetic bead is driven by a magnetic field generated by a coil. In the coil driver, a differentiation circuit is used for compensating the frequency dependence of the coil impedance and keeping the current constant. By implementing several differentiation circuits with different frequency ranges, we enable to drive various cantilevers having different resonance frequencies with sufficient excitation efficiency. In contrast to the conventional coil driver with a closed-loop circuit, the developed one consists of an open-loop circuit and hence can be stably operated regardless of the coil design. With the developed system, atomic-resolution imaging of mica in liquid using a small cantilever with a megahertz-order resonance frequency is demonstrated. This development should lead to the future applications of AFM with small cantilevers to the studies on various photo-sensitive materials and phenomena.

Variation of elemental concentration in leather during post-tanning operation using energy dispersive X-ray fluorescence spectroscopy: principal component analysis approach

ABSTRACT Leather processing employs a series of heavy chemicals that are detrimental to human health and environment at different stages of tanning. Determination of the elemental concentrations becomes complex especially using the conventional techniques since they involve tracking individual elements at different stages of leather-making processes. The study investigated the effect of crusting operations on the elemental concentrations using energy dispersive X-ray fluorescence (EDXRF) and principal component analysis (PCA). The spectral measurements of the post-tanned samples were carried out in vacuum using EDXRF-Rigaku NEX CG. The concentrations of elements were determined by advanced FP (fundamental Parameter) software-RPF-SQX (Rigaku Profile Fitting-Spectra Quant X) software. The abundance of the elements in the leather crusts was in the order Cr > S > Na > Cl > Al > Si > Ca > V > K > P > Zr > Zn > Fe > Mn > As > Ti > Cu > Pb > Ni > Ga > Br > Hg. The concentration levels for the majority of the elements in the crusts were higher than the recommended safe extractible levels for leather. Combination of EDXRF and principal component analysis in this study has shown potential in the leather industry to monitor the chemical concentrations. A combination of Cu and RX9 secondary targets exhibited sufficient and excellent excitation efficiency for detection of the majority of elements in leather crusts. The valuable tracers for classification in the crusting operations are Cr, S, Cl, P, V, K, Mn and Zn. Retanning process increases S and Cu whereas levels of Cl decrease in the crusts. Dyeing and Fatliquoring processes raise the concentration levels of S while decreasing the Cl level.

Active Fault Diagnosis Method for Vehicles in Platoon Formation

This paper presents an active fault diagnosis (AFD) method with reduced excitation for detection and identification of sensor faults of vehicles in a platoon formation. By introducing a probing signal into the platooning, it will allow an active excitation of the system, reveling a residual component, with the same frequency, that can be explored to obtain a fault identification of specific system faults. A supervisor is introduced to monitor the platoon behavior and activate the auxiliary input whenever the system natural excitation is insufficient for a clear fault diagnosis. This solution will allow the fault diagnosis to behave as active or passive through the adaptive signal provided by the supervisor. A dual Youla-Jabr-Bongiorno-Kucera (YJBK) matrix transfer function, also known as fault signature matrix (FSM) is investigated to get a fault diagnosis. In order to obtain an online identification of specific faults in the system, a Taylor approximation of the FSM is pursued. Computational simulations with a high-fidelity full-vehicle model, provided by CarSim , are carried out to demonstrate the effectiveness of the proposed active approach. A direct comparison between an active and a passive behavior in the same scenario shows that the active fault diagnosis method outperforms the passive approach whenever the dynamic behavior does not provide sufficient excitation. Furthermore, the excitation supervisor is able to significantly reduce the amount of artificial excitation introduced into the system ensuring a more energy efficient active fault diagnosis.

Experimental realization of incipient active failure in sand heap by seismic loading

Sand heaps avalanche when a static angle of repose is exceeded, and freeze at a dynamic angle of repose. The stress distribution in a planar sand heap stabilized at the angle of repose with an incipient active failure condition, by which the material is on the verge of failure everywhere, was examined. At the time of construction, the vertical pressure was initially not in the incipient failure. A slight amount of seismic loading using the sinusoidal motions of a shaking table was set up with just enough energy to rearrange the sand particles, to erase the formation history, and to fluctuate local density inside a sand heap without an excessive loss of mass. During the experiments, laser displacement sensors and pressure gauges were employed to monitor the vertical displacements above and the vertical pressures below the sand heap respectively. The non-linear differential equations, characterizing the stress distribution in an active sand heap, were formulated under the Mohr–Coulomb failure criterion and numerically solved by the Newton method. Bulk solids started flowing after the state of incipient failure; therefore, the experimental vertical pressure profiles were found to gradually conform to the theoretical vertical pressure profiles if the sand heap was subjected to sufficient excitation.

Reliable Online Parameter Identification of Li-Ion Batteries in Battery Management Systems Using the Condition Number of the Error Covariance Matrix

Monitoring the state of health (SOH) for Li-ion batteries is crucial in the battery management system (BMS), for their efficient and safe use. Due to time-varying battery parameters and insufficient computation capability of the BMSs, computationally efficient online parameter identification is practically required. So, a simple equivalent circuit model (ECM) based recursive least squares (RLS) parameter identification algorithm has been widely used. However, it has long been acknowledged that this algorithm suffers from wind-up problem when the input current doesn't provide sufficient excitation. It causes numerical instability and then induces large sensitivity of identified parameter values to the noise or truncation error of sensor data, leading to large parameter identification errors. In this work, a new reliable version of ECM based RLS, called a condition number based recursive least squares (CNRLS) algorithm, is proposed to avoid large errors due to insufficient excitation by monitoring the condition number of the error covariance matrix If the condition number is greater than a certain prescribed value, currently identified parameters are considered unreliable and hence the proposed algorithm uses stored internal variables previously computed with sufficiently exciting input current, leading to small condition number of the error covariance matrix. Accordingly, the forgetting factor is also adjusted to give a larger weight to such stored internal variables in order to overcome the insufficient excitation of the input current. It is shown with a1-RC equivalent circuit model that the proposed CNRLS algorithm is more noise-tolerant and accurate than two benchmarks including the standard RLS and adaptive forgetting factor RLS (AFFRLS) in terms of mean absolute errors, with almost the same computing cost.

Enhanced parameter estimation in adaptive control via online historical data

Parameter convergence is desirable for adaptive control as it enhances the overall stability and robustness properties of the closed-loop system. In existing online historical data (OHD)-driven parameter estimation schemes, all OHD are exploited to update parameter estimates such that exponential parameter convergence is ensured under a condition of sufficient excitation which is strictly weaker than the traditional persistent excitation (PE) condition. However, the exploitation of all OHD not only results in possible unbounded adaptation but also loses the flexibility of handling slow time-varying uncertainties. In this brief, a novel OHD-driven parameter estimation scheme that exploits only partial OHD is presented to improve parameter convergence and is incorporated with direct adaptive control to construct a composite learning control strategy. The proposed approach guarantees exponential parameter convergence under a condition of interval excitation which is also strictly weaker than the PE condition while eliminating the drawbacks of existing OHD-driven parameter estimation schemes. Numerical results have verified the effectiveness and superiority of the proposed approach.

Efficient learning from adaptive control under sufficient excitation

SummaryParameter convergence is desirable in adaptive control as it enhances the overall stability and robustness properties of the closed‐loop system. In existing online historical data (OHD)–driven parameter learning schemes, all OHD are exploited to update parameter estimates such that parameter convergence is guaranteed under a sufficient excitation (SE) condition which is strictly weaker than the classical persistent excitation condition. Nevertheless, the exploitation of all OHD not only results in possible unbounded adaptation but also loses the flexibility of handling slowly time‐varying uncertainties. This paper presents an efficient OHD‐driven parameter learning scheme for adaptive control, where a variable forgetting factor is specifically designed and is equipped with an estimation error feedback such that exponential parameter convergence is achieved under the SE condition without the aforesaid drawbacks. The proposed parameter learning scheme is incorporated with direct adaptive control to construct an OHD‐based composite learning control strategy. Numerical results have verified the effectiveness of the proposed approach.

Rapid Road Friction Estimation using Independent Left/Right Steering Torque Measurements

Sensing the friction coefficient between the tyres of an automobile and the road surface is a key challenge in automotive control. Accurate estimation of this parameter enables higher performance and safer operation of vehicles, whether human-operated, assisted or autonomously controlled. Previous approaches have utilised braking or lateral excitation to generate estimates but are limited by the conflict between generating sufficient excitation and preserving ride comfort and efficient operation. Further complicating the problem is the need to provide the friction estimate without adding costly sensors to the vehicle. This paper presents an approach to friction estimation that leverages separate measurements of the left and right steering torques to generate an estimate of the surface friction through direct model inversion. Uncertainty of the method is explored using experimental sensor properties and analysis of the inverted model. The method is shown to be sensitive to reduced friction coefficients and can identify sudden changes before travelling a full contact patch length. Experimental validation of the approach is demonstrated with calibrated wheel force transducers as well as low-cost sensors suitable for incorporation into production vehicles.

Transmittance Characterization of Objective Lenses Covering all Four Near Infrared Optical Windows and its Application to Three-Photon Microscopy Excited at 1820 nm

Near infrared (NIR) excitation or emission is capable of deep-tissue penetration in various modalities of optical microscopy. Based on the transmittance characterization of biological tissue, such as brains, four NIR optical windows have been demonstrated or suggested: The 800-nm, the 1300-nm, the 1700-nm, and the 2200-nm window. High-numerical aperture objective lenses are needed in optical microscopy to both deliver sufficient excitation light and collect efficient signal light to enable deep-tissue imaging. The transmittance performances of objective lenses are of vital importance. However, there is a lack of experimental characterization of the transmittance, especially at long wavelengths, which poses a dramatic obstacle for lens selection in imaging experiments. Here, we demonstrate detailed measurement results of the transmittance performance of air, water-immersion, and oil-immersion objectives available to us, covering all the four NIR optical windows. These results will provide direct guidelines for objective lens selection in terms of transmittance performance. We further demonstrate three-photon microscopy with 1820-nm excitation, close to the edge of the 1700-nm window, using the objective lens based on our transmittance measurement.

Analysis on two types of internal resonance of a suspended bridge structure with inclined main cables based on its sectional model

Abstract This study investigated the internal resonance phenomenon of a suspended bridge structure with a 6-Degree-of-Freedom sectional model. The primary resonance of the second mode of the system under harmonic excitation is firstly studied. The one-to-two internal resonance between the second and third modes, and one-to-three internal resonance between the second and fourth modes may be induced if the corresponding natural frequency ratios are close to 2.0 and 3.0, respectively. Numerical analysis also shows that the two-to-one internal resonance between the third and second modes can be induced under different scenarios of excitation. The first one happens with the second mode being excited, resulting in two response peaks in the frequency response curves . The second one may occur when the third mode is excited with sufficient large excitation where most of the energy input will be transmitted to the second mode. Hopf bifurcation can also be found in the frequency response curve of the system. Lastly, the three-to-one internal resonance between the fourth and second modes is also found when the second mode is excited. The response of the second mode is slightly reduced with a distinct increase in the response of the fourth mode due to the internal resonance. All these behaviors of this dynamic system indicate the meaningful role played by a variety of internal resonances in the design of mega-scale cable-supported bridge structure.

Highly Sensitive Refractive Index Sensors with Plasmonic Nanoantennas−Utilization of Optimal Spectral Detuning of Fano Resonances

We analyze and optimize the performance of coupled plasmonic nanoantennas for refractive index sensing. The investigated structure supports a sub- and super-radiant mode that originates from the weak coupling of a dipolar and quadrupolar mode, resulting in a Fano-type spectral line shape. In our study, we vary the near-field coupling of the two modes and particularly examine the influence of the spectral detuning between them on the sensing performance. Surprisingly, the case of matched resonance frequencies does not provide the best sensor. Instead, we find that the right amount of coupling strength and spectral detuning allows for achieving the ideal combination of narrow line width and sufficient excitation strength of the subradiant mode, and therefore results in optimized sensor performance. Our findings are confirmed by experimental results and first-order perturbation theory. The latter is based on the resonant state expansion and provides direct access to resonance frequency shifts and line width changes as well as the excitation strength of the modes. Based on these parameters, we define a figure of merit that can be easily calculated for different sensing geometries and agrees well with the numerical and experimental results.

Integrated identification and control for nanosatellites reclaiming failed satellite

Using nanosatellites to reclaim a failed satellite needs nanosatellites to attach to its surface to take over its attitude control function. This is challenging, since parameters including the inertia matrix of the combined spacecraft and the relative attitude information of attached nanosatellites with respect to the given body-fixed frame of the failed satellite are all unknown after the attachment. Besides, if the total control capacity needs to be increased during the reclaiming process by new nanosatellites, real-time parameters updating will be necessary. For these reasons, an integrated identification and control method is proposed in this paper, which enables the real-time parameters identification and attitude takeover control to be conducted concurrently. Identification of the inertia matrix of the combined spacecraft and the relative attitude information of attached nanosatellites are both considered. To guarantee sufficient excitation for the identification of the inertia matrix, a modified identification equation is established by filtering out sample points leading to ill-conditioned identification, and the identification performance of the inertia matrix is improved. Based on the real-time estimated inertia matrix, an attitude takeover controller is designed, the stability of the controller is analysed using Lyapunov method. The commanded control torques are allocated to each nanosatellite while the control saturation constraint being satisfied using the Quadratic Programming (QP) method. Numerical simulations are carried out to demonstrate the feasibility and effectiveness of the proposed integrated identification and control method.

Experimental estimation of transmissibility matrices for industrial multi-axis vibration isolation systems

Abstract Vibration isolation is essential for industrial high-precision systems to suppress external disturbances. The aim of this paper is to develop a general identification approach to estimate the frequency response function (FRF) of the transmissibility matrix, which is a key performance indicator for vibration isolation systems. The major challenge lies in obtaining a good signal-to-noise ratio in view of a large system weight. A non-parametric system identification method is proposed that combines floor and shaker excitations. Furthermore, a method is presented to analyze the input power spectrum of the floor excitations, both in terms of magnitude and direction. In turn, the input design of the shaker excitation signals is investigated to obtain sufficient excitation power in all directions with minimum experiment cost. The proposed methods are shown to provide an accurate FRF of the transmissibility matrix in three relevant directions on an industrial active vibration isolation system over a large frequency range. This demonstrates that, despite their heavy weight, industrial vibration isolation systems can be accurately identified using this approach.

A Framework for Adaptive Predictive Control System Based on Zone Control

In view of the degradation of predictive control performance caused by model mismatch, a multi-variable adaptive predictive control system framework which is composed of zone model predictive control (MPC), identification module and performance monitoring module, is presented. The proposed framework synthesizes the traditional control mode and the test mode to construct a unified form, which is convenient to implement with the MPC software packages. Traditional setpoint control is switched to zone control to ensure that the process constraints remain satisfied in testing, while multi-variable test signals are introduced to guarantee the sufficient excitation of the plant. In addition, in order to maximize the signal-to-noise ratio, an adaptive method of determining the amplitude of test signals is proposed. All the online open-loop identification methods are suitable for this framework, as the testing is treated as “open-loop,” which solves the problem of the correlation between input signals and noises in the closed-loop identification. These characteristics of the proposed framework are illustrated via a simulation.

Partial identification and control of MIMO systems via switching linear reduced-order models under weak stimulations

In closed loop identification of an unknown control system, the stability is a big concern particularly when the system does not proceed with sufficient excitation. In this paper, under insufficient excitation of the system, identification and control are investigated by employing Evolving Linear Models (ELMs). It is explained that under weak stimulation, linear correlations between input and output signals and their derivations are occurred. Removing some correlated variables through the time, an equivalent reduced order model of the original system is appeared, which can be identified as an ELM. Defining control law based on the sliding mode control (SMC) and using appropriate adaptation rules for parameters of the model, the tracking errors converge to zero and the stability of the system is guaranteed. Then, convergence of the parameters to their true values is studied and discussed. Different simulations are given to demonstrate the efficacy of the proposed closed loop identification approach.

Spline-Based Initialization of Monocular Visual–Inertial State Estimators at High Altitude

Due to the lack of direct distance measurements, robust and accurate state estimation at high altitude but GPS-denied environments is a challenging task. A possible solution is monocular visual–inertial estimators, in which visual and inertial measurements are properly fused to recover the metric estimates. However, these estimators suffer from initialization under poor numerical conditioning or even degeneration, due to difficulties in retrieving observations of visual features with sufficient parallax, and the excessive period of inertial measurement integration. In this letter, we introduce the joint formulation into the spline-based high altitude estimator initialization method for monocular visual–inertial navigation system, in which the fitting of the spline and the alignment of visual measurements and inertial measurements are jointly optimized to recover metric estimates. The method ensures that sufficient excitation is contained in the inertial measurements when initialized, which eliminates the numerical issues. Compared with the work of Liu and Shen, the joint formulation makes our initialization method insensitive to the choice of spline parameters. Thus, the adaptivity to various environments and motions is obtained, as well as higher accuracy. The method is applicable for both loosely coupled and tightly coupled visual–inertial estimators. Extensive experiments are conducted to validate our approach.

Gyroscope Calibration via Magnetometer

Magnetometers, gyroscopes, and accelerometers are commonly used sensors in a variety of applications. This paper proposes a novel gyroscope calibration method in the homogeneous magnetic field by the help of magnetometer. It is shown that, with sufficient rotation excitation, the homogeneous magnetic field vector can be exploited to serve as a good reference for calibrating low-cost gyroscopes. The calibration parameters include the gyroscope scale factor, non-orthogonal coefficient and bias for three axes, as well as its misalignment to the magnetometer frame. Simulation and field test results demonstrate the method’s effectiveness.