Slow Sampling Rate Research Articles

A fast dynamic pose estimation method for vision-based trajectory tracking control of industrial robots

Vision-based robotic trajectory tracking control is considered a promising technology. However, the slow sampling rate and latency of the vision sensor enormously limit the tracking performance. To conquer the issues, this paper proposes a dual-space error-state Kalman filter (DS-ESKF). By combining the encoder measurement with the vision measurement, the end-effector’s pose between adjacent vision measurements is restored, and the pose estimation cycle is synchronized with the control cycle. The critical distinguishing of DS-ESKF is that the encoder-driven error-state kinematics for the industrial robot is reconstructed. The experimental results on the Staubli TX60 industrial robot show that compared with the existing dual-rate Kalman filters (DR-KF), DS-ESKF can reduce estimation errors by about 50 % and have robust estimation performance. By applying the trajectory tracking control scheme combining DS-ESKF with a simple PID controller to Staubli TX60, the tracking accuracy is significantly improved (±0.11 mm for position and ± 0.05°for orientation).

DFT-based controller for sag and harmonic mitigation with a series-connected compensator

Voltage-quality problems in power distributionnetworks include harmonics, voltage sags and swells andunbalances. These problems may seriously affect sensitiveindustrial loads causing production interruption or equipmentdamage. Now-a-days series voltage compensation using powerelectronics devices is a promising solution for these problemsand the design, control and application of this type of deviceshave drawn much attention in the literature. In fact, compre-hensive controllers for power electronics series compensatorhave already been proposed in the literature to tackle all thoseproblems simultaneously. For example, repetitive controllersshow very promising performance although several aspectsstill need closer attention. This paper proposes a differenttype of controller based on a Discrete Fourier Transform tominimise voltage harmonic pollution using power electronicsseries devices. The design process of this type of controlleris straightforward regardless of the number of harmonics tobe tackled. In addition, the proposed controller uses a slowsampling rate and its computational effort is relatively low. Themain contributions of this paper are illustrated by simulation.

High bandwidth control of slow sampling rate nonlinear systems using alternating predictive GPIOs

This paper investigates the problem of robust output feedback control tracking for a class of nonlinear dual-rate sample-data systems subject to time-varying disturbances. These dual-rate systems, known as slow measurement fast control (SMFC) systems, are characterised by a slow sampling rate of the sensor and a fast updating rate of the controller. To facilitate high-frequency signal tracking, this paper introduces an alternating predictive generalised proportional integral observer (APGPIO). The proposed control method not only effectively restores the system state and captures disturbance changes between samples but also enhances the control bandwidth and anti-disturbance ability, enabling effective tracking of high-frequency reference outputs. By combining it with a fast updating sample-data feedback controller, APGPIO utilises predictive methods to increase the sampling rate of the system's sensors, matching the update frequency of the controller. Stability analysis demonstrates that the tracking error of this hybrid nonlinear system converges to a bounded region centred at the origin. Finally, the designed method's high control bandwidth and anti-disturbance ability are demonstrated through multiple sets of simulations using a real system.

Formula omitted] control of singularly perturbed systems using deficient hidden semi-Markov model

This paper deals with the H∞ control of a class of stochastic multi-timescale systems, called Markov jump singularly perturbed systems. The hidden semi-Markov model is introduced to handle the situation when system modes are unavailable in semi-Markov systems. Such a model is assumed deficient, that is, it lacks knowledge about the emission probability, transition probability, and probability density function of the sojourn time. It is a more general case compared with works conducted with perfect transition information. Depending on whether a fast or slow sampling rate is used, the resulting discrete-time singularly perturbed system is modeled differently, for both of which the controller design is conducted. Furthermore, criteria expressed in terms of linear matrix inequalities (LMIs) are developed that guarantee the δ-error mean-square stability. An approach to estimate the upper bound on δ-error with incomplete information is provided, meanwhile, the relationship between system performance and the upper of singular perturbation parameter is also presented. Finally, two simulation examples using real-world systems are provided to corroborate the validity as well as the practical merits of the results.

A New Alternating Predictive Observer Approach for Higher Bandwidth Control of Dual-Rate Dynamic Systems

Dual-rate dynamic systems consisting of a sensor with a relatively slow sampling rate and a controller/actuator with a fast updating rate widely exist in control systems. The control bandwidth of these dual-rate dynamic systems is severely restricted by the slow sampling rate of the sensors, resulting in various issues like sluggish dynamics of the closed-loop systems, poor robustness performance. A novel alternating predictive observer (APO) is proposed to significantly enhance the control bandwidth of a generic dual-rate dynamic systems. Specifically, at each fast controller/actuator updating period, we will first implement the prediction step by using the system model to predict the system output, generating a so-called virtual measurement, when there is no output measurement during the slow sampling period. Subsequently, the observation step is carried out by exploiting this virtual measurement as informative update. An APO-based output feedback controller with a fast updating rate is developed and rigorous stability of the closed-loop system is established. The superiority of the proposed method is demonstrated by applying it to control a permanent magnet synchronous motor system.

Vision-based particle filtering for quad-copter attitude estimation using multirate delayed measurements.

In this paper, the problem of attitude estimation of a quad-copter system equipped with a multi-rate camera and gyroscope sensors is addressed through extension of a sampling importance re-sampling (SIR) particle filter (PF). Attitude measurement sensors, such as cameras, usually suffer from a slow sampling rate and processing time delay compared to inertial sensors, such as gyroscopes. A discretized attitude kinematics in Euler angles is employed where the gyroscope noisy measurements are considered the model input, leading to a stochastic uncertain system model. Then, a multi-rate delayed PF is proposed so that when no camera measurement is available, the sampling part is performed only. In this case, the delayed camera measurements are used for weight computation and re-sampling. Finally, the efficiency of the proposed method is demonstrated through both numerical simulation and experimental work on the DJI Tello quad-copter system. The images captured by the camera are processed using the ORB feature extraction method and the homography method in Python-OpenCV, which is used to calculate the rotation matrix from the Tello's image frames.

Power Gain from Energy Harvesting Sources at High MPPT Sampling Rates.

Energy harvesting (EH) sources require the tracking of their maximum power point (MPP) to ensure that maximum energy is captured. This tracking process, performed by an MPP tracker (MPPT), is performed by periodically measuring the EH transducer's output at a given sampling rate. The harvested power as a function of the sampling parameters has been analyzed in a few works, but the power gain achieved with respect to the case of a much slower sampling rate than the EH source's frequency has not been assessed so far. In this work, simple expressions are obtained that predict this gain assuming a Thévenin equivalent for the EH transducer. It is shown that the power gain depends on the relationship between the square of AC to DC open circuit voltage of the EH transducer. On the other hand, it is proven that harvested power increases, using a suitable constant signal for the MPP voltage instead of tracking the MPP at a low sampling rate. Experimental results confirmed the theoretical predictions. First, a function generator with a series resistor of 1 kΩ was used, emulating a generic Thévenin equivalent EH. Three waveform types were used (sinus, square, and triangular) with a DC voltage of 2.5 V and AC rms voltage of 0.83 V. A commercial MPPT with a fixed sampling rate of 3 Hz was used and the frequency of the waveforms was changed from 50 mHz to 50 Hz, thus effectively emulating different sampling rates. Experimental power gains of 11.1%, 20.7%, and 7.43% were, respectively, achieved for the sinus, square, and triangular waves, mainly agreeing with the theoretical predicted ones. Then, experimental tests were carried out with a wave energy converter (WEC) embedded into a drifter and attached to a linear shaker, with a sinus excitation frequency of 2 Hz and peak-to-peak amplitude of 0.4 g, in order to emulate the drifter's movement under a sea environment. The WEC provided a sinus-like waveform. In this case, another commercial MPPT with a sampling period of 16 s was used for generating a slow sampling rate, whereas a custom MPPT with a sampling rate of 60 Hz was used for generating a high sampling rate. A power gain around 20% was achieved in this case, also agreeing with the predicted gain.

Kalman Filter Fusion With Smoothing for a Process With Continuous-Time Integrated Sensor

Integrated Sensor is a well-known methodology of slow sampling rates in chemical processes, which gradually collects material samples within a finite period. This type of infrequent measurement specified for quality variables is a function of states over sampling time. Our objective is to solve the problem of estimating states in fast sampling rates for a continuous-time system in the presence of a slow-rate integrated sensor. For this purpose, two reformulated models are suggested for describing the system, while they do not explicitly include the integral term. Integrated Measurement Kalman Filter (IMKF) for these two models are presented by the extension of the classical Continuous-Discrete (CD) Kalman filter to the integrated systems. Then, a novel sliding window smoothing algorithm is proposed for integrated systems using pseudo-estimations of the output, past time information of the fast-rate estimates and the last available slow-rate measurement. Also, the optimal fusion algorithm employing the estimation results of two reformulated models is extended for the integrated system. Eventually, the observability condition and exponential convergence of observation error are proved for the proposed algorithm. Simulation and experimental implementations are employed to demonstrate the effectiveness of the proposed method through a drum boiler model and a laboratory-scale pressure control process, respectively.

Shape detection beyond the visual field using a visual-to-auditory sensory augmentation device.

Current advancements in both technology and science allow us to manipulate our sensory modalities in new and unexpected ways. In the present study, we explore the potential of expanding what we perceive through our natural senses by utilizing a visual-to-auditory sensory substitution device (SSD), the EyeMusic, an algorithm that converts images to sound. The EyeMusic was initially developed to allow blind individuals to create a spatial representation of information arriving from a video feed at a slow sampling rate. In this study, we aimed to use the EyeMusic for the blind areas of sighted individuals. We use it in this initial proof-of-concept study to test the ability of sighted subjects to combine visual information with surrounding auditory sonification representing visual information. Participants in this study were tasked with recognizing and adequately placing the stimuli, using sound to represent the areas outside the standard human visual field. As such, the participants were asked to report shapes' identities as well as their spatial orientation (front/right/back/left), requiring combined visual (90° frontal) and auditory input (the remaining 270°) for the successful performance of the task (content in both vision and audition was presented in a sweeping clockwise motion around the participant). We found that participants were successful at a highly above chance level after a brief 1-h-long session of online training and one on-site training session of an average of 20 min. They could even draw a 2D representation of this image in some cases. Participants could also generalize, recognizing new shapes they were not explicitly trained on. Our findings provide an initial proof of concept indicating that sensory augmentation devices and techniques can potentially be used in combination with natural sensory information in order to expand the natural fields of sensory perception.

A hybrid random parameters modification to MPPT algorithm to mitigate interharmonics from single-phase grid-connected PV systems

As the on-grid photovoltaic (PV) system’s penetration level increases, the utility grid power quality (PQ) becomes a vital emerging issue to spot the lights on. The recent studies confirmed that one source of interharmonics emission from PV inverters is the maximum power point tracking (MPPT) algorithms. Following this issue, a strong relationship has been found between the interharmonics generating characteristics and the MPPT parameters, such as the sampling rate and perturbation step size. Utilizing a big step size and fast sampling rate will enhance the tracking performance but will raise the level of interharmonics injected into the grid and lower the overall efficiency. Therefore, there is a trade-off between MPPT parameters, efficiency, and interharmonics characteristics in PV systems. To alleviate mentioned issue, this paper investigates the mechanism of interharmonics generation and emission under different power operating conditions. Accordingly, a new mitigation technique is presented for interharmonics generation in grid-connected PV systems. The proposed method is based on modifying the MPPT algorithm in a way that maintains its performance characteristic while effectively minimizing the generated interharmonics by a random selection of big or small perturbation step sizes and fast or slow sampling rate, respectively. Thereby, the frequency spectrum distribution is modified, and interharmonics peaks are reduced in the output injected current to the grid by 27 % compared to the other conventional and modified perturb and observe (P&O) MPPT algorithms found in the literature. The effectiveness of the proposed method is verified by simulation studies on a single-phase grid-connected PV system.

Imaging a periodic moving/state-changed object with Hadamard-based computational ghost imaging

We propose a method for imaging a periodic moving/state-changed object based on computational ghost imaging with Hadamard speckle patterns and a slow bucket detector, named as PO-HCGI. In the scheme, speckle patterns are produced from a part of each row of a Hadamard matrix. Then, in each cycle, multiple speckle patterns are projected onto the periodic moving/state-changed object, and a bucket detector with a slow sampling rate records the total intensities reflected from the object as one measurement. With a series of measurements, the frames of the moving/state-changed object can be obtained directly by the second-order correlation function based on the Hadamard matrix and the corresponding bucket detector measurement results. The experimental and simulation results demonstrate the validity of the PO-HCGI. To the best of our knowledge, PO-HCGI is the first scheme that can image a fast periodic moving/state-changed object by computational ghost imaging with a slow bucket detector.

Observer-Based Control for the Two-Time-Scale Cyber-Physical Systems: The Dual-Scale DoS Attacks Case

This paper concerns the observer-based control problem for a class of two-time-scale cyber-physical systems (TTSCPSs) under dual-scale denial-of-service (DoS) attacks. A TTSCPSs model is first proposed with slow and fast transmission channels, because that slow and fast dynamics are operating and communicating with slow and fast sampling rates, respectively. Due to the time scale difference between slow and fast transmission channels, the model of dual-scale DoS attacks is constructed by characterizing the attack frequency and duration on different time scales. A novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -dependent switched observer is formulated, and corresponding security controller is designed to stabilize the TTSCPSs under dual-scale DoS attacks. By constructing an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -dependent Lyapunov function, sufficient conditions for the desired observer-based controller are derived such that the closed-loop TTSCPSs can be input-to-state stable. Moreover, ill-conditioned numerical issue caused by the two-time-scale feature can be avoided in the proposed design methods. Finally, examples are given to verify the effectiveness of the proposed algorithm.

Defining speckle-based spectral metric from a linear array detector for characterizing the laser focusing spot

Speckle modulation in the laser beam projection scenarios such as adaptive optics, remote sensing, and active imaging has been a long-standing challenge that causes intensity fluctuation and further degrades the performance of the system. Speckle statistical characteristics coupled with the feature of the laser beam intensity distribution can be obtained by analyzing the speckle variation through a single point detector or an image sensor. A single point detector has the advantage in high sampling rate but may lose speckle spatial information, while the image sensor has a high spatial resolution but slow sampling rate. In this paper, we defined a dynamic speckle metric based on a linear array detector to estimate the encircled energy of the far-field spot on the diffuse target, which well balanced the trade-off between sampling rate and spatial resolution. The potential of the metric is analyzed using a physics-based speckle simulation and experimental verification. Both results agree with each other, showing that the metrics based on the speckle-field spatiotemporal spectrum analysis are monotonically dependent on the target focused-spot size.

Discrete-Time Sliding Mode Control Coupled with Asynchronous Sensor Fusion for Rigid-Link Flexible-Joint Manipulators

This paper proposes a novel discrete-time terminal sliding mode controller (DTSMC) coupled with an asynchronous multirate sensor fusion estimator for rigid-link flexible-joint (RLFJ) manipulator tracking control. A camera is employed as external sensors to observe the RLFJ manipulator’s state which cannot be directly obtained from the encoders since gear mechanisms or flexible joints exist. The extended Kalman filter- (EKF-) based asynchronous multirate sensor fusion method deals with the slow sampling rate and the latency of camera by using motor encoders to cover the missing information between two visual samples. In the proposed control scheme, a novel sliding mode surface is presented by taking advantage of both the estimation error and tracking error. It is proved that the proposed controller achieves convergence results for tracking control in the theoretical derivation. Simulation and experimental studies are included to validate the effectiveness of the proposed approach.

Process variability reduction in flow control and wedge flowmeter performance evaluation

In modern flow control settings, a slow controller sampling rate is often much slower than a flowmeter response, and flowmeters are often selected without consideration of the dynamic response characteristics. However, for fast loops which necessitate a high control sampling frequency, negligence in flowmeter selection might result in an undesirable control performance degradation. Such associated research is expected to be critical to design and evaluation of new flowmeters, i.e., wedge flowmeters in this study, but little work has been found in the public domain to address this issue. In this paper, a new way of calculating process variability is proposed to fast-track the evaluation of flow control performance and cost saving. In addition, based on frequency response analysis, a systematic study of how dynamic response characteristics of flowmeters impact flowmeter accuracy and process variability is presented. Moreover, the impact of controller sampling rate on process variability is researched. Last, the closed-loop stability of the flow control loop with new wedge flowmeters is evaluated for a wide range of its targeted applications. The results demonstrate that a flowmeter with a faster response will always lead to process variability reduction for a given controller sampling rate, and that with careful controller set up, a flow loop with wedge flowmeter is still stable despite relatively slow dynamics.

Neurotransmitter Release of Reprogrammed Cells Using Electrochemical Detection Methods.

The detection of neurotransmitter release from reprogrammed human cell is an important demonstration of their functionality. Electrochemistry has the distinct advantages over alternative methods that it allows for the measuring of the analyte of interest at a high temporal resolution. This is necessary for fast events, such as neurotransmitter release and reuptake, which happen in the order of milliseconds to seconds. The precise description of these kinetic events can lead to insights into the function of cells in health and disease and allows for the exploration of events that might be missed using methods that look at absolute concentration values or methods that have a slower sampling rate. In the present chapter, we describe the use of constant potential amperometry and enzyme-coated multielectrode arrays for the detection of glutamate in vitro. These biosensors have the distinct advantage of "self-referencing," a method providing high selectivity while retaining outstanding temporal resolution. Here, we provide a step-by-step user guide for a commercially available system and its application for in vitro systems such as reprogrammed cells.

State Estimation for Multirate Measurements in the Presence of Integral Term and Variable Delay

Measurements in the process industry can arrive with fast or slow sampling rates. Fast measurements, such as flowrate and temperature, are sampled frequently and are obtained instantly after sampling. The slow measurements, which are usually related to chemical quality variables such as product composition, are sampled infrequently and have some delay due to laboratory analysis. Moreover, sample collection for laboratory analysis may extend over a significant time interval, and the slow measurements are actually functions of all the states during the sampling period. Our objective is to develop a multirate state estimation method for this situation. We propose two methods to solve the problem: the exact Bayesian approach and the augmented state approach. In the exact Bayesian approach, the algorithm is developed by implementing the Bayes rule. In the augmented state approach, the system is reformulated by augmenting the current state with past information required for fusing the slow measurements and then applying general state estimation procedures. The performance of the proposed methods for state estimation is demonstrated through simulation and experimental studies and by comparison with methods that only use the fast measurements.

Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring

Concurrent process-quality monitoring helps discover quality-relevant process anomalies and quality-irrelevant process anomalies. It especially works well in chemical plants with faults that cause quality problems. Traditional monitoring strategies are limitedly applied in chemical plants because quality targets in training data are insufficient. It is hard for inflexible models to fully capture the strongly nonlinear process-quality correlations. Also, deterministic models are mapped from process variables to qualities without any consideration of uncertainties. Simultaneously, a slow sampling rate for quality variables is ubiquitous in chemical plants since a product quality test is often time-consuming and expensive. Motivated by these limitations, this paper proposes a new concurrent process-quality monitoring scheme based on a probabilistic generative deep learning model developed from variational autoencoder. The supervised model is firstly developed and then the semi-supervised version is extended to solve the issue of missing targets. Especially, the semi-supervised learning algorithm is accomplished with an optimal parameter estimation in the light of maximum likelihood principle and no any hyperparameters are introduced. Two case studies validate that the proposed method effectively outperforms the other comparative methods in concurrent process-quality monitoring.

A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems.

With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.

Accelerating photonic computing by bandwidth enhancement of a time-delay reservoir

AbstractSemiconductor lasers (SLs) that are subject to delayed optical feedback and external optical injection have been demonstrated to perform information processing using the photonic reservoir computing paradigm. Optical injection or optical feedback can under some conditions induce bandwidth-enhanced operation, expanding their modulation response up to several tens of GHz. However, these conditions may not always result in the best performance for computational tasks, since the dynamical and nonlinear properties of the reservoir might change as well. Here we show that by using strong optical injection we can obtain an increased frequency response and a significant acceleration in the information processing capability of this nonlinear system, without loss of performance. Specifically, we demonstrate numerically that the sampling time of the photonic reservoir can be as small as 12 ps while preserving the same computational performance when compared to a much slower sampling rate. We also show that strong optical injection expands the reservoir’s operating conditions for which we obtain improved task performance. The latter is validated experimentally for larger sampling times of 100 ps. The above attributes are demonstrated in a coherent optical communication decoding task.