Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network

Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as a predictive model to construct a SUGV model and back propagation (BP) is taken as its learning algorithm. Second, the predictive control law is designed and the traditional objective function is improved to obtain a predictive objective function with a differential term. The optimal control quantity is given in real time through iterative optimization. Meanwhile, the stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, a tracking control experiment on the SUGV model is used to verify the effectiveness of the proposed scheme. For comparison, traditional MTN and Radial Basis Function (RBF) predictive control schemes are introduced. Moreover, a noise disturbance is considered. Experimental results show that the proposed scheme is effective, which ensures that the vehicle can quickly and accurately track the desired yaw velocity signal with good real-time, robustness, and convergence performance, and is superior to other comparison schemes.

Kalman Filter Implementation of Subglottal Impedance-Based Inverse Filtering to Estimate Glottal Airflow during Phonation

Subglottal Impedance-Based Inverse Filtering (IBIF) allows for the continuous, non-invasive estimation of glottal airflow from a surface accelerometer placed over the anterior neck skin below the larynx. It has been shown to be advantageous for the ambulatory monitoring of vocal function, specifically in the use of high-order statistics to understand long-term vocal behavior. However, during long-term ambulatory recordings over several days, conditions may drift from the laboratory environment where the IBIF parameters were initially estimated due to sensor positioning, skin attachment, or temperature, among other factors. Observation uncertainties and model mismatch may result in significant deviations in the glottal airflow estimates; unfortunately, they are very difficult to quantify in ambulatory conditions due to a lack of a reference signal. To address this issue, we propose a Kalman filter implementation of the IBIF filter, which allows for both estimating the model uncertainty and adapting the airflow estimates to correct for signal deviations. One-way analysis of variance (ANOVA) results from laboratory experiments using the Rainbow Passage indicate an improvement using the modified Kalman filter on amplitude-based measures for phonotraumatic vocal hyperfunction (PVH) subjects compared to the standard IBIF; the latter showing a statistically difference (p-value =0.02, F=4.1) with respect to a reference glottal volume velocity signal estimated from a single notch filter used here as ground-truth in this work. In contrast, maximum flow declination rates from subjects with vocal phonotrauma exhibit a small but statistically difference between the ground-truth signal and the modified Kalman filter when using one-way ANOVA (p-value =0.04, F=3.3). Other measures did not have significant differences with either the modified Kalman filter or IBIF compared to ground-truth, with the exception of H1-H2, whose performance deteriorates for both methods. Overall, both methods (modified Kalman filter and IBIF) show similar glottal airflow measures, with the advantage of the modified Kalman filter to improve amplitude estimation. Moreover, Kalman filter deviations from the IBIF output airflow might suggest a better representation of some fine details in the ground-truth glottal airflow signal. Other applications may take more advantage from the adaptation offered by the modified Kalman filter implementation.

Surf Zone Turbulence and Suspended Sediment Dynamics—A Review

The existence of sandy beaches relies on the onshore transport of sand by waves during post-storm conditions. Most operational sediment transport models employ wave-averaged terms, and/or the instantaneous cross-shore velocity signal, but the models often fail in predictions of the onshore-directed transport rates. An important reason is that they rarely consider the phase relationships between wave orbital velocity and the suspended sediment concentration. This relationship depends on the intra-wave structure of the bed shear stress and hence on the timing and magnitude of turbulence production in the water column. This paper provides an up-to-date review of recent experimental advances on intra-wave turbulence characteristics, sediment mobilization, and suspended sediment transport in laboratory and natural surf zones. Experimental results generally show that peaks in the suspended sediment concentration are shifted forward on the wave phase with increasing turbulence levels and instantaneous near-bed sediment concentration scales with instantaneous turbulent kinetic energy. The magnitude and intra-wave phase of turbulence production and sediment concentration are shown to depend on wave (breaker) type, seabed configuration, and relative wave height, which opens up the possibility of more robust predictions of transport rates for different wave and beach conditions.

Transient Stability Control Based on Kinetic Energy Changes Measured by Synchronized Angular Velocity

Real-time transient stability studies are based on voltage angle measures obtained with phasor measurement units (PMUs). A more precise calculation to address transient stability is obtained when using the rotor angles. However, these values are commonly estimated, which leads to possible errors. In this work, the kinetic energy changes in electric machines are used as a criterion for evaluating and correcting transient stability, and to determine the precise time of insertion of a special protection system (SPS). Data from the PMU of the wide-area measurement system (WAMS) are used to construct the SPS. Furthermore, it is assumed that a microcontroller can be located in each generation unit to obtain the synchronized angular velocity. Based on these measurements, the kinetic energy of the system and the respective control action are performed at the appropriate time. The results show that the proposed SPS effectively corrects the oscillations fast enough during the transient stability event. In addition, the proposed method has the advantage that it does not depend on commonly proposed methods, such as system models, the identification of coherent machine groups, or the structure of the network. Moreover, the synchronized angular velocity signal is used, which is not commonly measured in power systems. Validation of the method is carried out in the New England power system, and the findings show that the method is helpful for real-time operation on large power systems.

Dynamic Radiation Effects Induced by Short-Pulsed GeV U-Ion Beams in Graphite and h-BN Targets

Targets of isotropic graphite and hexagonal boron nitride were exposed to short pulses of uranium ions with ∼1 GeV kinetic energy. The deposited power density of ∼3 MW/cm³ generates thermal stress in the samples leading to pressure waves. The velocity of the respective motion of the target surface was measured by laser Doppler vibrometry. The bending modes are identified as the dominant components in the velocity signal recorded as a function of time. With accumulated radiation damage, the bending mode frequency shifts towards higher values. Based on this shift, Young’s modulus of irradiated isotropic graphite is determined by comparison with ANSYS simulations. The increase of Young’s modulus up to 3 times the pristine value for the highest accumulated fluence of 3 × 1013 ions/cm2 is attributed to the beam-induced microstructural evolution into a disordered structure similar to glassy carbon. Young’s modulus values deduced from microindentation measurements are similar, confirming the validity of the method. Beam-induced stress waves remain in the elastic regime, and no large-scale damage can be observed in graphite. Hexagonal boron nitride shows lower radiation resistance. Circular cracks are generated already at low fluences, risking material failure when applied in high-dose environment.

Assessing the validity of dual-minima algorithm for heel-strike and toe-off prediction for the amputee population

Assessment of gait deficits relies on accurate gait segmentation based on the key gait events of heel strike (HS) and toe-off (TO). Kinematics-based estimation of gait events has shown promise in this regard especially using the leg velocity signal and gyroscopic sensors. However, its validation for the amputee population is not established in the literature. The goal of this study is to assess the accuracy of lower-leg angular velocity signal in determining the TO and HS instants for the amputee population. An open data set containing marker data of 10 subjects with unilateral transfemoral amputation during treadmill walking was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating TO and HS events. The predictions were compared against the force platform data for 2595 walking cycles from 239 walking trials. Results showed considerable accuracy for the HS with a median error of -1ms. The TO prediction error was larger with the median ranging from 35-84ms. The algorithm consistently predicted the TO earlier than the actual event. Significant differences were found between the prediction accuracy for the sound and prosthetic legs. The prediction accuracy was also affected by the subjects’ mobility level (K-level) but was largely unaffected by gait speed. In conclusion, the leg velocity profile during walking can predict the heel-strike and toe-off events for the transfemoral amputee population with varying degrees of accuracy depending upon the leg side and amputee’s functional ability level.

Proper orthogonal decomposition of ice velocity identifies drivers of flow variability at Sermeq Kujalleq (Jakobshavn Isbræ)

The increasing volume and spatio-temporal resolution of satellite-derived ice velocity data has created new exploratory opportunities for the quantitative analysis of glacier dynamics. One potential technique, Proper Orthogonal Decomposition (POD), also known as Empirical Orthogonal Functions, has proven to be a powerful and flexible technique for revealing coherent structures in a wide variety of environmental flows. In this study we investigate the applicability of POD to an openly available TanDEM-X/TerraSAR-X derived ice velocity dataset from Sermeq Kujalleq (Jakobshavn Isbræ), Greenland. We find three dominant modes with annual periodicity that we argue are explained by glaciological processes. Mode 1 is interpreted as relating to the stress-reconfiguration at the glacier terminus, known to be an important control on the glacier’s dynamics. Modes 2 and 3 together relate to the development of the spatially heterogenous glacier hydrological system and are primarily driven by the pressurisation and efficiency of the subglacial hydrological system. During the melt season, variations in the velocity shown in Modes 2 and 3 are explained by the drainage of nearby supraglacial melt ponds, as identified with a Google Earth Engine MODIS dynamic thresholding technique. By isolating statistical structures within velocity datasets, and through their comparison to glaciological theory and complementary datasets POD indicates which glaciological processes are responsible for the changing bulk velocity signal, as observed from space. With the proliferation of optical and radar derived velocity products (e.g. MEaSUREs/ESA CCI/PROMICE) we suggest POD, and potentially other modal decomposition techniques, will become increasingly useful in future studies of ice dynamics.

A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking

Magnetic particle tracking is a recently developed technology that can measure the translation and rotation of a particle in an opaque environment like a turbidity flow and fluidized-bed flow. The trajectory reconstruction usually relies on numerical optimization or filtering, which involve artificial parameters or thresholds. Existing analytical reconstruction algorithms have certain limitations and usually depend on the gradient of the magnetic field, which is not easy to measure accurately in many applications. This paper discusses a new semi-analytical solution and the related reconstruction algorithm. The new method can be used for an arbitrary sensor arrangement. To reduce the measurement uncertainty in practical applications, deep neural network (DNN)-based models are developed to denoise the reconstructed trajectory. Compared to traditional approaches such as wavelet-based filtering, the DNN-based denoisers are more accurate in the position reconstruction. However, they often over-smooth the velocity signal, and a hybrid method that combines the wavelet and DNN model provides a more accurate velocity reconstruction. All the DNN-based and wavelet methods perform well in the orientation reconstruction.

Analysis of LES and 1D Hot-Wire Data to Determine Actively Generated Main Flow Turbulence in a Film Cooling Test Rig

A vortex generator was incorporated into a wind tunnel to investigate more realistic inflow conditions for film cooling test rigs. The flow field signals are sampled numerically by probes in LES simulations and experimentally by using 1D hot-wire measurements to determine turbulence quantities. The LES shows that the turbulence is anisotropic which cannot be detected by the 1D hot-wire. Furthermore, the integral length scale which shall provide insight into the sizes of the turbulent eddies is determined using two approaches. The first uses the one probe at two times correlation method and could be evaluated from the numerical and experimental probes. The second correlation method exploits the spatial resolution in the LES domain by using the two probes at one time approach. Both methods show combustor-like turbulence length scales downstream of the vortex generator if the triple decomposition is applied onto the velocity signal.

Vehicle attitude compensation control of magneto-rheological semi-active suspension based on state observer

This paper presents a vehicle attitude compensation algorithm based on state observer for vehicle semi-active suspension system equipped with four magneto-rheological dampers (MR dampers). The proposed algorithm including magneto-rheological damper control algorithm, attitude compensation control algorithm, and design method of state observer is to effectively improve ride comfort and control vehicle body attitude. First, the actual equivalent damping of magneto-rheological damper is introduced into state observer, and the parameter matrix of suspension system is updated in real time via precise discretization method to enhance the estimation accuracy of state observer. Then, the velocity signal estimated by state observer is employed as the evidence to realize attitude compensation control for vehicle body. Finally, relevant co-simulations and hardware-in-the-loop test are conducted to verify the validity of the proposed control algorithm. Results of simulations and tests demonstrate that the application of the control algorithm proposed in this paper can significantly improve ride comfort of magneto-rheological suspension and optimize vehicle body attitude.