Real-time Feedback Control Research Articles

Image-Based Feedback Control for a Coaxial Spray

Abstract Optimization of jet engine sprays has the potential to improve efficiency and reduce environmental impact. Sprays can be continually optimized in multivariate scenarios using real-time feedback control, but a method of controlling the sprays based on physical properties must first be established. In this study, a spray controller was developed to optimize the spray angle obtained from shadowgraphs, with the assumption that the largest angle is desired. The spray angle was used as an example, as it is a physically important parameter which is easily found through shadowgraph imaging. Varying ratios of swirled air to straight air, determined by the image-based feedback controller were introduced into the air portion of a coaxial airblast nozzle while keeping the total air flow rate constant. A golden section search converged on the swirled air ratio that provided the largest angle and was validated from the distribution of spray angle versus swirled air ratio. The ratio that produced a spray with the greatest angle of 25.8 ± 2 deg was found at a swirled air ratio of 0.66 ± 0.03 for a spray with a momentum ratio of 6. The successful design and implementation of this image-based feedback controller is intended to provide a foundation for developing real-time active feedback controllers for sprays.

A rule-based expert system for real-time feedback-control in deep brain stimulation

Abstract Programming in deep brain stimulation (DBS) is often a labour-intensive process. Although automatic closed-loop stimulation has recently been receiving considerable attention, it is still far from clinical settings. Testing in-loop stimulation in a clinical setting is extremely challenging due to manual programming and the lack of synchronisation between stimulation and monitoring devices. In this work, we present a simple rulebased expert system to test feedback-controlled DBS in a clinical setting. The new application operates in closed-loop with the physician as acting person and real-time feedback from an accelerometer. Patients with movement disorders such as in essential tremor announce an individually acceptable level of tremor as a boundary condition for control. As a proof-of-concept, the expert system provides continuous recommendations of stimulation parameters and guides the physician to increase or decrease DBS amplitude by capturing tremor acceleration power on the patients’ forearms. The introduced application considers the technical and practical aspects in a clinical setting. Data obtained from test subjects provide insight into tremor dynamics. We demonstrate the clinical applicability of the rule-based control system for future research focusing on tremor dynamics and inloop stimulation. Finally, a telemetry streaming system could provide the interface for the application of automatic tremor control without the physician as acting person.

Imaging and control of critical fluctuations in two-dimensional magnets.

Strong magnetization fluctuations are expected near the thermodynamic critical point of a continuous magnetic phase transition. Such critical fluctuations are highly correlated and in principle can occur at any time and length scales1; they govern critical phenomena and potentially can drive new phases2,3. Although critical phenomena in magnetic materials have been studied using neutron scattering, magnetic a.c. susceptibility and other techniques4-6, direct real-time imaging of critical magnetization fluctuations remains elusive. Here we develop a fast and sensitive magneto-optical imaging microscope to achieve wide-field, real-time monitoring of critical magnetization fluctuations in single-layer ferromagnetic insulator CrBr3. We track the critical phenomena directly from the fluctuation correlations and observe both slowing-down dynamics and enhanced correlation length. Through real-time feedback control of the critical fluctuations, we further achieve switching of magnetic states solely by electrostatic gating. The ability to directly image and control critical fluctuations in 2D magnets opens up exciting opportunities to explore critical phenomena and develop applications in nanoscale engines and information science.

Optimization of the POlarimeter-INTerferometer diagnostics system on EAST

A double-pass, radial-view, 11-chord POlarimeter-INTerferometer (POINT) system has been routinely operated on the Experimental Advanced Superconducting Tokamak (EAST) to provide electron density and current density information for physics research. Stray light occurs due to spurious reflections from optical elements, including lenses, waveplates, windows, and even detectors, along the optical path and can seriously deteriorate the accuracy of the Faraday rotation measurements. As a result of stray light contamination, periodic oscillations can be observed in the Faraday rotation data during the density ramp-up phase. Aiming to realize real-time current profile feedback control using Faraday effect measurements, the contamination from stray light must be removed. Some optimization methods have been explored to decrease the error induced by stray light. The results show that the Faraday angle oscillation during the density ramp-up phase is reduced from 3̂–5̂ to 0.5̂–1̂ (double-pass). Measurement of the noncollinearity error is also performed for the POINT system on EAST. This optimization allows for more accurate current profile measurements for physics research.

Applying a 6-axis Mechanical Arm Combine with Computer Vision to the Research of Object Recognition in Plane Inspection

The study of a robotic arm copied with 3D-printer combines computer vision system with tracking algorithm is proposed in the paper. Moreover, the designing to the intelligent vehicle system with the integration of electromechanical for planning to apply it to the operations in various fields is presented too. The main purpose of this work tries to avoid the complicated process with traditional manual adjustment or teaching. It is expected to achieve the purpose that the robotic arm can grab the target automatically, classify the target and place it in the specified area, and even accurately realize the classification through training to distinguish the characteristics of the target. Eventually, the mechanical arm's movement behavior is able to be corrected through a real-time image data feedback control system. In words, with the experiment that the computer vision system is used to assist the robotic arm to detect the color and position of the target. By adding color features for algorithm training as well as through human-machine collaboration, which approves that the proposed algorithm has well known that the accuracy of target tracking definitely depends on both of two parameters include “object locations” and the “illustration direction” of light source. The difference will far from 75.2% to 89.0%.

Classification of Reflection High-Energy Electron Diffraction Pattern Using Machine Learning

Reflection high-energy electron diffraction (RHEED) has wide application because it allows in situ observation of the sample surface behavior during molecular beam epitaxy growth. In particular, the RHEED pattern has been used as a milestone for growth condition calibration because it dynamically changes depending on the sample temperature, material supply rate, and supply ratio. However, RHEED pattern analysis depends on the accumulated know-how of the operator and has a time limitation; thus, its application to real-time feedback control is difficult. Moreover, with the conventional computerization method, it is difficult to correctly reflect and recognize the changes in RHEED due to changes in the observation conditions. On the other hand, the machine learning method using the convolutional neural network (CNN) recognizes feature points in the input database and is suitable for the classification of images with variability. In this study, we propose a measurement method for identifying the RHEED pattern of GaAs substrates during continuous rotation and build a data set of the growth conditions. A classification model is established by training the deep learning model using CNN, and is found to be more than 99% accurate. This is expected to be useful in the field of highquality III–V growth on GaAs.

Cell manufacturing innovation facility: use of GMP-compliant space to accelerate advances in therapeutic cell production

Background & Aim Production space for cell therapy products is in high demand, with the recent FDA approvals of the first cell therapy products in the U.S. Hospitals are expanding their qualified production spaces to support the growing number of cell and gene therapy clinical trials. Simultaneously, a pronounced need to improve engineering and controlled manufacturing principles has been made clear. Initiatives have emerged to accelerate cell therapy manufacturing innovations at academic institutions, including the NSF Engineering Research Center for Cell Manufacturing Technologies (CMaT) and the Marcus Center for Therapeutic Cell Characterization and Manufacturing (MC3M). In order for researchers, particularly in engineering and manufacturing disciplines, to have an understanding of restrictions placed on therapeutic cell production, exposure to compliant environments without disrupting production is necessary. The MC3M facility at Georgia Tech provides the community access to facilities operating under GMP-compliant conditions and the ability to develop next generation technologies. Methods, Results & Conclusion The MC3M facility contains two isolated ISO 7/GMP suites for viral and viral-free work, and a large ISO 8 research lab. GMP rooms are adjacent to analytical suites, with pass-thrus to enable real-time analytics and feedback control. MC3M houses various bioreactors, including hollow fiber, vertical wheel, and stirred batch systems, as well as analytics equipment to enable at-line and in-line monitoring within minutes from sampling directly from culture vessels. Additionally, automation technologies that measure and control process parameters such as glucose and lactate are being developed and employed. Faculty, students, and industry can pilot new process technologies for cell manufacturing needs. Users learn proper gowning procedures and operational requirements like material quarantine and environmental monitoring. These experiences serve to better inform researchers in the practical applications of their early technology or culture process improvements, and advance the translation of discoveries to products. The facility is utilized for training purposes beyond the university's community to inform and equip the future workforce of the growing cell manufacturing industry. Facilities like MC3M are imperative to meet the demands of the cell manufacturing industry, and ensure clinically and industry relevant, impactful research is conducted, even at the earliest stages of discovery.

Model and control method of a downhole electromagnetic transmitter for EM-MWD system

Downhole electromagnetic transmitter is an important part of EM-MWD system, which can send low-frequency EM waves data from a downhole tool to surface in real time for oil and gas exploration. In order to reduce the volume and weight of the downhole electromagnetic transmitter and improve the depth and accuracy of petroleum exploration, a novel downhole electromagnetic transmitter topology for electromagnetic MWD system is designed. Aiming at the complex and changeable characteristics of load impedance of transmitter, an equivalent load model for downhole electromagnetic transmitter is established with drill string, casing, drill-bit, drilling fluid and formation being taken into account. Because the attenuation characteristic of electromagnetic signal in the wellbore is similar to that of capacitor discharge, the characteristic of electromagnetic channel can be equivalent to the combination of capacitance and resistance. Then, in order to ensure that the downhole electromagnetic transmitter will produce the nominal load voltage, irrespective of disturbances such as variations in the lithium batteries voltage, perturbations in the switching times, and the load, a dual-loop real-time feedback control scheme with an outer voltage loop and an inner current loop is proposed. Simulation and experimental results with laboratory prototype demonstrate the effectiveness of the control method for downhole electromagnetic transmitter. It also ensures the downhole electromagnetic transmitter's excellent stability and the accuracy of the established equivalent load model as well as its ability to meet the requirements of practical field deep exploration. The findings of our work can help for better understanding of approximate load model of downhole electromagnetic transmitter. Furthermore, it will be of great significance for designing control algorithm of downhole electromagnetic transmitter for different areas or boreholes, achieving the purpose of increasing transmission depth. The research results can be used to improve the design and optimization of electromagnetic MWD measurement system in the aspects of improving the control accuracy of the transmitter, saving energy and increasing the transmission depth.

Feasibility of Robot-Assisted Ultrasound Imaging with Force Feedback for Assessment of Thyroid Diseases.

Medical ultrasound is extensively used to define tissue textures and to characterize lesions, and it is the modality of choice for detection and follow-up assessment of thyroid diseases. Classical medical ultrasound procedures are performed manually by an occupational operator with a hand-held ultrasound probe. These procedures require high physical and cognitive burden and yield clinical results that are highly operator-dependent, therefore frequently diminishing trust in ultrasound imaging data accuracy in repetitive assessment. A robotic ultrasound procedure, on the other hand, is an emerging paradigm integrating a robotic arm with an ultrasound probe. It achieves an automated or semi-automated ultrasound scanning by controlling the scanning trajectory, region of interest, and the contact force. Therefore, the scanning becomes more informative and comparable in subsequent examinations over a long-time span. In this work, we present a technique for allowing operators to reproduce reliably comparable ultrasound images with the combination of predefined trajectory execution and real-time force feedback control. The platform utilized features a 7-axis robotic arm capable of 6-DoF force-torque sensing and a linear-array ultrasound probe. The measured forces and torques affecting the probe are used to adaptively modify the predefined trajectory during autonomously performed examinations and probe-phantom interaction force accuracy is evaluated. In parallel, by processing and combining ultrasound B-Mode images with probe spatial information, structural features can be extracted from the scanning volume through a 3D scan. The validation was performed on a tissue-mimicking phantom containing thyroid features, and we successfully demonstrated high image registration accuracy between multiple trials.

Deep model predictive flow control with limited sensor data and online learning

The control of complex systems is of critical importance in many branches of science, engineering, and industry, many of which are governed by nonlinear partial differential equations. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration). However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible. Fortunately, these high-dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required. Advances in machine learning have the potential to revolutionize flow control given its ability to extract principled, low-rank feature spaces characterizing such complex systems. We present a novel deep learning model predictive control framework that exploits low-rank features of the flow in order to achieve considerable improvements to control performance. Instead of predicting the entire fluid state, we use a recurrent neural network (RNN) to accurately predict the control relevant quantities of the system, which are then embedded into an MPC framework to construct a feedback loop. In order to lower the data requirements and to improve the prediction accuracy and thus the control performance, incoming sensor data are used to update the RNN online. The results are validated using varying fluid flow examples of increasing complexity.

Videometric mass flow control: A new method for real-time measurement and feedback control of powder micro-feeding based on image analysis.

The present paper reports the first monitoring and control of ultra-low dose powder feeding using a camera image-based mass flow measurement system. Caffeine was fed via a single-screw microfeeder as a model active pharmaceutical ingredient (API). The mass, mass flow and sizes of the particles were successfully monitored in real-time by the developed videometric system consisting of a high-speed process camera coupled with an image analysis software. The system was also tested in feedback control mode to automatically reach the desired mass flow values by adjusting the feeder speed based on the mass flow measured by the image analysis system. Based on these features, the developed videometric system can serve as a multi-purpose PAT-tool and can provide valuable real-time information about the process which is indispensable for modern continuous pharmaceutical manufacturing.

Machine learning control for disruption and tearing mode avoidance

Real-time feedback control based on machine learning algorithms (MLA) was successfully developed and tested on DIII-D plasmas to avoid tearing modes and disruptions while maximizing the plasma performance, which is measured by normalized plasma beta. The control uses MLAs that were trained with ensemble learning methods using only the data available to the real-time Plasma Control System (PCS) from several thousand DIII-D discharges. A “tearability” metric that quantifies the likelihood of the onset of 2/1 tearing modes in a given time window, and a “disruptivity” metric that quantifies the likelihood of the onset of plasma disruptions were first tested off-line and then implemented on the PCS. A real-time control system based on these MLAs was successfully tested on DIII-D discharges, using feedback algorithms to maximize βN while avoiding tearing modes and to dynamically adjust ramp down to avoid high-current disruptions in ramp down.

High-quality quantum process tomography of time-bin qubit’s transmission over a metropolitan fiber network and its application

We employ quantum state and process tomography with time-bin qubits to benchmark a city-wide metropolitan quantum communication system. Over this network, we implement real-time feedback control systems for stabilizing the phase of the time-bin qubits, and obtain a 99.3% quantum process fidelity to the ideal channel, indicating the high quality of the whole quantum communication system. This allows us to implement field trial of high performance quantum key distribution using coherent one way protocol with average quantum bit error rate and visibility of 0.25% and 99.2% during 12 hours over 61 km. Our results pave the way for the high-performance quantum network with metropolitan fibers.

Reliable Guaranteed Cost Control of Uncertain Systems with Nonlinear Perturbations

Network control system (NCS) is a distributed real-time feedback control system with the continuous development of network technology. It is generally composed of network, controller, actuator and sensor. It has brought great convenience to people in many fields, but it also has many problems, which makes the research of control system more complicated. Recently, there have been some efforts to tackle the reliable guaranteed cost controller design problem, and some good results have also been obtained for the continuous-time and for the discrete-time. However, there have been few results in the literature of an investigation for the reliable guaranteed cost controller design of nonlinear uncertain systems with time-varying state delay and actuator failure. This paper concerns the reliable guaranteed cost control problem of uncertain systems with time-varying state delay and nonlinear perturbations for a given quadratic cost function. The problem is to design a reliable guaranteed cost state feedback control law which can tolerate actuator failures, such that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound. Firstly, the existence condition of reliable guaranteed cost control law is given by constructing Lyapunov stability function and using linear matrix inequality (LMI). Secondly, the design method of the optimal reliable guaranteed cost controller is given by solving the convex optimization problem with LMI constraints, which minimizes the upper bound of guaranteed cost for closed-loop systems. In the end, the numerical simulation result illustrates the effectiveness of the proposed method.

Design of a Real Time Feedback Automation Irrigation System

This paper presents the design of a real time feedback control automated irrigation systems that consists of monitoring units, control units, irrigation pipeline valves, and a network of irrigation pipeline. The monitoring units continuously measure the soil moisture content in the irrigation blocks, and if the moisture content drops below a predetermined threshold for the particular crop under production, it sends a wireless message to the control units controlling the pipeline valves along the water flow channel, causing the control units to open the valves leading to the water source and commencing automatic irrigation. When the moisture content rises above a predetermined threshold for the crop, the monitoring units sends a wireless message to the control units, causing them to close the pipeline valves and cease automatic irrigation. An automatic irrigation system pipeline network optimization software has also been designed to plan, cost, and design the automatic irrigation system for a piece of land prior to installation.

Design of a Real Time Feedback Automation Irrigation System

This paper presents the design of a real time feedback control automated irrigation systems that consists of monitoring units, control units, irrigation pipeline valves, and a network of irrigation pipeline. The monitoring units continuously measure the soil moisture content in the irrigation blocks, and if the moisture content drops below a predetermined threshold for the particular crop under production, it sends a wireless message to the control units controlling the pipeline valves along the water flow channel, causing the control units to open the valves leading to the water source and commencing automatic irrigation. When the moisture content rises above a predetermined threshold for the crop, the monitoring units sends a wireless message to the control units, causing them to close the pipeline valves and cease automatic irrigation. An automatic irrigation system pipeline network optimization software has also been designed to plan, cost, and design the automatic irrigation system for a piece of land prior to installation.

The effect of transcranial focused ultrasound target location on the acoustic feedback control performance during blood-brain barrier opening with nanobubbles

Real-time acoustic feedback control based on harmonic emissions of stimulated microbubbles may be important for facilitating the clinical adoption of focused ultrasound (FUS)-induced blood-brain barrier (BBB) opening, both to ensure safe acoustic exposures, and to achieve repeatable and consistent opening. Previously our group demonstrated that successful BBB opening was achievable with both commercially available microbubbles and custom-made nanobubbles under acoustic feedback control. In a recent study, we demonstrated the acoustic control performance was not sensitive to the nanobubble concentration within 109–1011 bubbles/ml. The goal of this study was to examine the effect of the ultrasound target location in the rat brain on the acoustic control quality during BBB opening with nanobubbles. Temporal analysis of the received acoustic signals during each ultrasound pulse indicated that stable nanobubble oscillation was present throughout the entire 10 ms ultrasound exposure. The acoustic feedback control signals were very sensitive to the brain spatial location in rats. There appears to be a shared pattern of acoustic control stability in the brain across different animals, suggesting anatomical features are an underlying cause. The findings emphasize the importance of tuning acoustic feedback control algorithms for specific rodent brain regions of interest to ensure optimal performance.

Progress of the synthetic HCOOH laser diagnostic system on HL-2A tokamak

A synthetic formic-acid laser (HCOOH, λ=432.5 μm) diagnostic system, which includes a Michelson Interferometer, a Faraday-effect Polarimeter and a Far-forward collective scattering diagnostic, has been successfully developed on HL-2A tokamak for plasma operation and physics research. This paper describes the progress made to improve the diagnostic system: (1) For the Interferometer, the data processing/acquisition system was greatly improved, and the high-speed refractive memory card was firstly utilized in the real-time density feedback control. (2) For the Polarimeter, the main interference source of Faraday rotation angle measurement was identified to be the stay light, and preliminary result indicated that the stray light coming from the detector surface can be largely suppressed by using the optic-isolator which was made up of a polarizer and a quarter wave plate. (3) The newly developed Far-forward collective scattering diagnostic based on two circularly polarized waves was widely used to investigate broadband density fluctuations in recent experimental campaign.

Optical surface measurements of a wavelength-tuned Fizeau interferometer based on an optical power, real-time feedback and compensation system

The wavelength-tuned Fizeau interferometer achieves the desired phase for interferograms based on the use of a phase-shifting technique. It can then use it to measure the surface and thickness of a parallel transparent object. However, phase errors occur owing to the inherent changes of the power of the tunable laser diode induced upon laser wavelength changes. To improve the measurement accuracy, we propose an optical power, real-time feedback control system, and a synchronous calibration scheme. The significance of the proposed method relies on the fact that the laser intensity variation can be detected quickly and compensated by adjusting the bias voltage. To verify the effectiveness of the proposed system, we incorporated it into the optical system of the wavelength-tuned, phase-shifting interferometer for experimentation. The control principles and the experimental validation of the proposed system are described in this letter.

Simulation-based design and characterization of a microwave applicator for MR-guided hyperthermia experimental studies in small animals

Purpose: The objective of this study was to design and characterize a 2.45 GHz microwave hyperthermia applicator for delivering hyperthermia in experimental small animals to 2–4 mm diameter targets located 1–3 mm from the skin surface, with minimal heating of the surrounding tissue, under 14.1 T MRI real-time monitoring and feedback control. Materials and methods: An experimentally validated 3D computational model was employed to design and characterize a non-invasive directional water-cooled microwave hyperthermia applicator. We assessed the effects of: reflector geometry, monopole shape, cooling water temperature, and flow rate on spatial-temperature profiles. The system was integrated with real-time MR thermometry and feedback control to monitor and maintain temperature elevations in the range of 4 °C–5 °C at 1–3 mm from the applicator surface. The quality of heating was quantified by determining the fraction of the target volume heated to the desired temperature, and the extent of heating in non-targeted regions. Results: Model-predicted hyperthermic profiles were in good agreement with experimental measurements (Dice Similarity Coefficient of 0.95–0.99). Among the four considered criteria, a reflector aperture angle of 120°, S-shaped monopole antenna with 0.6 mm displacement, and coolant flow rate of 150 ml min−1 were selected as the end result of the applicator design. The temperature of circulating water and input power were identified as free variables, allowing considerable flexibility in heating target sizes within varying distances from the applicator surface. 2–4 mm diameter targets positioned 1–3 mm from the applicator surface were heated to hyperthermic temperatures, with target coverage ratio ranging between 76%–93% and 11%–26% of non-targeted tissue heated. Conclusion: We have designed an experimental platform for MR-guided hyperthermia, incorporating a microwave applicator integrated with temperature-based feedback control to heat deep-seated targets for experimental studies in small animals.