Closed-loop Control Research Articles

Experimental investigation of closed-loop active control to modulate coherent structures by mu-level method*

Abstract The experimental research on zero-net-mass-flux jet closed-loop active control was conducted in the wind tunnel.The mu-level method successfully detected burst events of the coherent structures. The streamwise velocity signals in the turbulent boundary layer were measured by HWA. The drag reduction rate of 16.7% is obtained comparable to that of the open-loop control and saves 75% of the input energy at the ASYN 100V/160 Hz control case, which reflects the advantages of the closed-loop control.The experimental findings indicate that the intensity increases in the near-wall region. The perturbation of the PZT vibrators on skewness factor is concentrated in the region y + < 60 . The generation of high-speed fluids is depressed and the downward effect of high-speed fluids weakens. The alteration of energy distribution and the discernible impact of modulation between structures of varying scales is observed. The correlation coefficient exhibits a strong positive correlation, which indicates that the large-scale structures produce modulation effect on small-scale ones. The occurrence of burst events is effectively suppressed.The disturbance has the characteristics of stable periodicity, positive and negative symmetry, low intermittency and high pulsation strength.The conditional phase waveform shows the fluctuation amplitude increases, indicating amplitude modulation effects on coherent structures.

Analysis and control of split-source current-type inverter for grid-connected applications

Current source inverters (CSIs) have been widely used for renewable energy sources integration with the utility grid. However, traditional CSIs provide only voltage-boost power conversion, which can be considered a limitation for grid-interfacing applications when consistent tracking of a widely varying input voltage is necessary. Hence, an additional front-end converter is usually utilized in an attempt to step down the input source voltage. Nevertheless, adding the dc-dc bucking stage would reduce the system reliability and its overall efficiency, increase the converter cost, and complicate the control scheme. In order to address the aforementioned shortcomings, this paper proposes a novel three-phase single-stage inverter, suitable for low-power applications, called split-source current-type inverter (SSCTI). The proposed converter can achieve high current boosting capability with lower component count and high-quality output current in comparison with other single-stage topologies. In addition, it utilizes the conventional modulation scheme of the CSI, while maintaining low current stresses on the inverter switches. Comprehensive analysis, modelling, and closed-loop control scheme of the proposed converter are developed. Also, a comparison between the proposed topology and several single-stage topologies is carried out. Moreover, an experimental prototype is built to validate the proposed converter analysis under steady-state operation and different transient scenarios.

A Robust Switching Control Strategy for Three-Phase Voltage Source Converters with Uncertain Circuit Parameters

This study proposes a novel double closed-loop robust control strategy based on a power switching affine model of three-phase voltage source converters (VSCs). The aim is to overcome the challenges posed by inaccurate mathematical models, complex controller configurations, indirect switching control, and performance degradation under circuit parameters uncertainty or load variation in conventional methods. These conventional methods rely on linearization models, duty ratio regulation, and pulse width modulation (PWM) technologies. The contributions of work are the following: (1) A two-dimensional (2D) power switching affine model is constructed without any approximation or averaging. (2) The proposed approach achieves direct switching control of three-phase VSCs, eliminating the need for complex rotation coordinate transformation, PWM, and phase locking loop (PLL), which are utilized in traditional control methods. (3) The rigor of the system stability analysis is enhanced based on the 2D power switching model compared to the existing three-dimensional (3D) current switching model. (4) A simple control structure with only two control parameters is employed to address circuit parameter uncertainties. The effectiveness and superiority of the proposed method is validated through simulation and experimental comparison results.

Deep learning-assisted concentration gradient generation for the study of 3D cell cultures in hydrogel beads of varying stiffness.

The study of dose-response relationships underpins analytical biosciences. Droplet microfluidics platforms can automate the generation of microreactors encapsulating varying concentrations of an assay component, providing datasets across a large chemical space in a single experiment. A classical method consists in varying the flow rate of multiple solutions co-flowing into a single microchannel (producing different volume fractions) before encapsulating the contents into water-in-oil droplets. This process can be automated through controlling the pumping elements but lacks the ability to adapt to unpredictable experimental scenarios, often requiring constant human supervision. In this paper, we introduce an image-based, closed-loop control system for assessing and adjusting volume fractions, thereby generating unsupervised, uniform concentration gradients. We trained a shallow convolutional neural network to assess the position of the laminar flow interface between two co-flowing fluids and used this model to adjust flow rates in real-time. We apply the method to generate alginate microbeads in which HEK293FT cells could grow in three dimensions. The stiffnesses ranged from 50Pa to close to 1kPa in Young modulus and were encoded with a fluorescent marker. We trained deep learning models based on the YOLOv4 object detector to efficiently detect both microbeads and multicellular spheroids from high-content screening images. This allowed us to map relationships between hydrogel stiffness and multicellular spheroid growth.

Control-oriented modelling of wind direction variability

Abstract. Wind direction variability significantly affects the performance and lifetime of wind turbines and wind farms. Accurately modelling wind direction variability and understanding the effects of yaw misalignment are critical towards designing better wind turbine yaw and wind farm flow controllers. This review focuses on control-oriented modelling of wind direction variability, which is an approach that aims to capture the dynamics of wind direction variability for improving controller performance over a complete set of farm flow scenarios, performing iterative controller development and/or achieving real-time closed-loop model-based feedback control. The review covers various modelling techniques, including large eddy simulations (LESs), data-driven empirical models, and machine learning models, as well as different approaches to data collection and pre-processing. The review also discusses the different challenges in modelling wind direction variability, such as data quality and availability, model uncertainty, and the trade-off between accuracy and computational cost. The review concludes with a discussion of the critical challenges which need to be overcome in control-oriented modelling of wind direction variability, including the use of both high- and low-fidelity models.

Research on Vibration Suppression Methods for Industrial Robot Time-Lag Filtering

This paper analyzes traditional vibration suppression methods in order to solve the vibration problem caused by the stiffness of flexible industrial robots. The principle of closed-loop control dynamic feedforward vibration suppression is described as the main method for solving robot vibration suppression. This paper proposes a method for time-lag filtering based on T-trajectory interpolation, which combines the T-planning curve and the time-lag filtering method. The method’s basic principle is to dynamically adjust the trajectory output through the algorithm, which effectively suppresses the amplitude of the harmonic components of a specific frequency band to improve the vibration response of industrial robot systems. This experiment compared traditional vibration suppression methods with the time-lag filtering method based on T-trajectory interpolation. A straight-line method was proposed to measure the degree of vibration. The results demonstrate that the time-lag filtering method based on T-trajectory interpolation is highly effective in reducing the vibration of industrial robots. This makes it an excellent option for scenarios that demand real-time response and high-precision control, ultimately enhancing the efficiency and stability of robots in performing their tasks.

Medical Microrobots.

Scientists around the world have long aimed to produce miniature robots that can be controlled inside the human body to aid doctors in identifying and treating diseases. Such microrobots hold the potential to access hard-to-reach areas of the body through the natural lumina. Wireless access has the potential to overcome drawbacks of systemic therapy, as well as to enable completely new minimally invasive procedures. The aim of this review is fourfold: first, to provide a collection of valuable anatomical and physiological information on the target working environments together with engineering tools for the design of medical microrobots; second, to provide a comprehensive updated survey of the technological state of the art in relevant classes of medical microrobots; third, to analyze currently available tracking and closed loop control strategies compatible with the in-body environment; and fourth, to explore the challenges still in place, to steer and inspire future research.

Model-based closed-loop control of thalamic deep brain stimulation.

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

Use of the Community-Derived Open-Source Automated Insulin Delivery Loop System in Type 2 Diabetes.

Background: No published data are available on the use of the community-derived open-source Loop hybrid closed-loop controller ("Loop") by individuals with type 2 diabetes (T2D). Methods: Through social media postings, we invited individuals with T2D currently using the Loop system to join an observational study. Thirteen responded of whom seven were eligible for the study, were using the Loop algorithm, and provided data. Results: Mean (±standard deviation) age was 61 ± 13 years, and mean body mass index was 31 ± 5 kg/m2. All but one participant were using noninsulin glucose-lowering medications. Self-reported mean hemoglobin A1c decreased from 7.3% ± 1.1% before starting Loop to 6.0% ± 0.5% on Loop. Time in range 70-180 mg/dL increased from 84% to 93%. The amount of time in hypoglycemia was extremely low before and with Loop (time <54 mg/dL was 0.04% ± 0.06% vs. 0.09% ± 0.07%, respectively). No severe hypoglycemia or diabetic ketoacidosis events were reported while using Loop. Conclusion: These data, though limited, suggest that the Loop system is likely to be effective when used by individuals with T2D and should be evaluated in large-scale studies. Clinical Trial Registration numbers: NCT05951569.

High-speed fluidic processing circuits for dynamic control of haptic and robotic systems.

Fluidic logic circuits simplify system design for soft robotics by eliminating bulky components while enabling operation in a range of hostile environments that are incompatible with electronics but at the expense of limited computational capabilities and response times on the order of seconds. This paper presents a four-terminal fluidic transistor optimized for fast switching times, reduced component count, low unit cost, and high reproducibility to achieve complex fluidic control circuits while maintaining flow rates of liters per minute. A ring oscillator using three fluidic transistors achieves oscillation frequencies up to a kilohertz with full signal propagation, tolerating billions of cycles without failure. Fundamental processor circuits like a full adder and a 3-bit analog-to-digital converter require just seven transistors each. A decode circuit drives a high-resolution soft haptic display with refresh times below the human perception threshold for latency, and an electronics-free control circuit performs closed-loop position control of a pneumatic actuator with disturbance rejection, demonstrating the value across domains.

Brain–machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study

BackgroundThis research focused on the development of a motor imagery (MI) based brain–machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will.MethodsA total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants’ neural activity using the second deep learning approach for the decoding.ResultsThe three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance.ConclusionThis research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study’s discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.

MAXIMUM POWER POINT TRACKING IN A PEROVSKITE SOLAR PUMPING SYSTEM WITH A SIX-PHASE INDUCTION MOTOR

In this work, the maximum power point (MPP) of a photovoltaic (PV) array utilized as a source of electricity in standalone mode is tracked using a genetic algorithm (GA) as an optimization tool. The major objective of this work is to implement and regulate a six-phase induction motor (SPIM) powered by a centrifugal pump and fed by a perovskite solar array through a three-phase inverter. Perovskite solar cells are the most promising third-generation photovoltaic technology to replace silicon-based photovoltaic. These cells are made cheaply, at a low temperature, and efficiently. The outcomes are then investigated utilizing a SPIM scaler (v/f) closed-loop control. PID controllers are tuned to keep the motor speed aligned with the reference value. The transformer's six-phase secondary is linked to an LC filter to reduce the voltage and current waveform ripple. A test setup was created and installed to investigate the potential of controlling the SPIM fed via a three-to-six-phase transformer. The findings showed that the area needed for the plates when utilizing perovskite solar cells is just 14 % of what it would be if a silicon-based solar array were used for the identical purpose. Perovskite solar cells' tiny surface area helps to prevent partial shading and lower costs. It was discovered that the suggested method works well for powering SPIM from a three-phase source.

Collaborative control of path tracking and roll stability in intelligent commercial vehicle: A minimax robust regulator based on Nash game

—Currently, most vehicles are generally equipped with roll stability control system or electronic stability control system to improve stability. However, when encountering emergency obstacle avoidance and similar conditions, roll stability control system may compete with path tracking control system for vehicle control, resulting in the inability to balance path tracking and roll stability. Therefore, a closed-loop feedback Nash game collaborative control method is proposed to solve the collaborative problem between the two control systems. Firstly, a fifth-order vehicle model with coupled yaw-roll dynamics is presented, which was extended to a vehicle-road closed-loop model based on lateral position, yaw angle and road preview information. Secondly, the path tracking control system and roll stability control system are regarded as two agents in the dynamic game control process. On this basis, a Nash game collaborative control strategy is designed by fully considering the control interaction between two game agents. Finally, considering the influence of uncertain disturbances, a finite-time minimax robust control strategy is proposed by using minimax optimization theory. Simulation and hardware in loop experiments show that the minimax robust regulator based on Nash game controller can effectively improve the path tracking and roll stability performance of vehicles. Moreover, the regulator improves the robustness of the system when it is subjected to bounded uncertain lateral disturbances.

Closed-loop Flow Control Method Based on Deep Reinforcement Learning using a Co-flow Jet

Closed-loop Flow Control Method Based on Deep Reinforcement Learning using a Co-flow Jet

Gap Self-Sensing Method for Maglev System Using Partial Electromagnet Coil

The use of dedicated sensor to measure levitation gap in maglev system leads to the problems of large volume, inconvenient installation and high cost. In this article, a gap self-sensing method using partial electromagnet coil is proposed to solve these problems. The entire electromagnet coil provides levitation force, while a small part of the electromagnet coil (the subcoil) on the side facing the levitated object is injected with high frequency excitation for gap self-sensing. The theoretical derivation shows the effects of levitation gap on the impedance of the subcoil. The frequency of the injected excitation and the structure of the subcoil are determined with finite element analysis for high sensitivity. The crosstalk between the gap self-sensing system and the levitation system is suppressed by the designed signal processing based on series resonance. The prototype in this article can cover a gap measurement range of 0–20 mm for maglev ball, with a nonlinearity error of 0.2% and a measurement accuracy of 0.2 mm. The closed-loop feedback control experiment shows that the performances of the proposed gap self-sensing system meet the control requirements.

Porosity prediction in laser-based powder bed fusion of polyamide 12 using infrared thermography and machine learning

Achieving precise control in laser-based powder bed fusion of polymers is crucial for ensuring the structural integrity of aerospace and automotive components. Closed-loop feedback control systems using process monitoring techniques, such as infrared thermography, have the potential to provide reliable production by controlling the temperature of the melt. However, challenges arise from complex interactions among variables, such as part geometry, scan strategy, and laser parameters, affecting the melted polymer's temperatures and subsequent particle fusion. Thus, the correlation between thermal signals and resulting part density still needs to be clarified. In this work, a machine learning algorithm is trained to predict local porosity or, rather, solidity based on thermal and temporal features extracted from the melt's temperature-time profile. This enables statistical techniques to assess the contribution of the melt's thermal and temporal features in the decision-making process for evaluating porosity, along with the influence of voxel size and configuration. The in-situ process signature is measured using infrared thermography, and the porosity is analyzed by X-ray micro-computed tomography. The 2D thermal data is first converted into voxel information and then stitched with the 3D mircoCT data in a second step. The resulting 3D thermal features and porosity matrices are downsampled and utilized to train a machine learning algorithm (lightGBM). Models with high prediction accuracy are achieved using a small voxel size to avoid over-homogenizing features and by utilizing thermal signals from adjacent voxels to determine porosity in a volume element. The highest predictor for resulting porosity is the peak temperature of the melt during laser exposure. Interlayer effects, such as sufficient reheating of subsurface layers, are the second-highest indicator for dense parts. Furthermore, the model's performance is also affected by intra-layer effects, including the peak temperature of adjacent voxels and the cooling behavior after laser exposure. This research has several implications for industry, as it enables the detection of process defects based on in-situ process monitoring data without post-process material testing. Moreover, identifying thermal signal ranges that lead to the highest porosity can reduce the number of experiments needed for material qualification processes.

A Untethered Magnetic Micro-Robot for Local Therapeutic Delivery for Neurosurgical Oncology

INTRODUCTION: Microrobots designed for biomedical applications have garnered significant research interest, offering advantages in drug delivery, microsurgery, and biopsies. Magnetic fields enabled by electromagnetic coils permit untethered nonlinear movement of these tiny robots within the brain on the surface, the cerebrospinal fluid, and parenchyma. Microdrillers (either individually or in swarms) are a promising tool for microsurgery, capable of drilling through viscoelastic media (brain tissue). However, challenges remain in scaling down the robot size and exploring diverse applications. METHODS: Parallelizable (batch) microfabrication involved 3D printing via two-photon lithography. The pH-sensitive hydrogel was synthesized through UV-initiated radical polymerization mixed with a tracer (fluorescein sodium salt). Imaging via photon-counting CT was performed for microbot tracking in preclinical models including hydrogel phantoms, large animal cadaveric fresh brain tissue, and human cadaveric tissue. RESULTS: Microbot speeds in various tissues were characterized with speeds up to 800 um/sec over magnetic field amplitudes of 5 to 55 mT while performing complex nonlinear trajectories via open-loop surgeon control via surgeon. Targeted hydrogel delivery of fluorescein label/nanoparticle drug over a pH range of 6.2 (tumor tissue) and 7.8 (normal tissue) over a time interval of 50 s is demonstrated as proof of concept with a rate of 2.4 times faster in acidic conditions. Lastly, we show state-of-the-art micromillmetric resolution for real time imaging via photon-counting spectral CT for implementation of closed loop control. CONCLUSIONS: A novel minimally invasive microrobotic platform technology capable of targeted local drug delivery in brain parenchyma is demonstrated.

Digital Close-Loop Active Gate Driver for Static and Dynamic Current Sharing of Paralleled SiC MOSFETs

SiC power devices have been extensively for the high-power density application scenarios. To increase the current rating, SiC devices are usually connected in parallel. However, the mismatching current brought by unbalanced electrical parameters can increase the current stress of a device and pose reliability concern of the converter system. Aiming at addressing the current imbalance for paralleled SiC devices, this paper reports the application of an improved active gate driver (AGD) on the paralleled SiC MOSFETs to address the current imbalance problems. The three-level driver voltage can minimize the overshoot voltage and current. The adjustable turn-on voltage and gate signal delay time can realize the current sharing of both static and dynamic process. Current sensors and a digital controller are utilized for close-loop control. The functionality of the proposed AGD is validated in continuous operating experiment.

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. To fill this gap, the Open RAN paradigm and its specification introduce an “open” architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the userlevel. This is obtained through custom RAN control applications (i.e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network. Despite these premises, as of today the research community lacks a sandbox to build data-driven xApps, and create large-scale datasets for effective Artificial Intelligence (AI) training. In this paper, we address this by introducing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ns-O-RAN</i> , a software framework that integrates a real-world, production-grade near- RT RIC with a 3GPP-based simulated environment on ns-3, enabling at the same time the development of xApps and automated large-scale data collection and testing of Deep Reinforcement Learning (DRL)- driven control policies for the optimization at the user-level. In addition, we propose the first user-specific O-RAN Traffic Steering (TS) intelligent handover framework. It uses Random Ensemble Mixture (REM), a Conservative Q-learning (CQL) algorithm, combined with a state-of-the-art Convolutional Neural Network (CNN) architecture, to optimally assign a serving base station to each user in the network. Our TS xApp, trained with more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC and controls the ns-O-RAN base stations. We evaluate the performance on a large-scale deployment with up to 126 users with 8 base stations, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead.

A Switched-Capacitor Multilevel Inverter With Modified Pulsewidth Modulation and Active DC-Link Capacitor Voltage Balancing

Although switched-capacitor (SC) multilevel inverters (MLIs) offer self-voltage balancing of flying capacitors and voltage gain higher than unity, the advantages come at the cost of high current stress and power loss in the SC circuit and DC source. Moreover, the voltage balancing is often restricted to a limited range of modulation index. Another problem of MLIs with neutral-point clamped (NPC) front end is with DC-link capacitor voltage balancing under unbalanced load conditions, imploring sensor-based closed-loop control. This paper proposes a unity gain five-level active-NPC SC-MLI with inrush charging current attenuation and actively balanced DC-link capacitor voltages under all load conditions and over the entire range of modulation index and power factor. The modified pulse-width modulation results in most switches operating at a low switching frequency, minimizing switching losses in the SC circuit. The proposed MLI attains a maximum efficiency of 98.04% at an output power of 510 W. Experiments on a 2 kVA laboratory prototype validate the theoretical analysis. Results from transformerless grid-connected solar PV system show that the proposed MLI can inject power into the grid with a unity power factor under conditions of varying irradiance and provide the grid with reactive power as well.