Closed-loop Feedback Control System Research Articles

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.

Soft crawling robot integrated with liquid metal-based flexible strain sensor and closed-loop feedback control

Due to the inherent softness and adaptability, soft robots have shown great application prospects in medical rehabilitation, environmental exploration, and other fields. However, the unpredictable and complex behaviors of the elastic materials greatly increased the difficulty in the control of soft robots. This study proposed an integrated soft crawling robot (ISCR) with flexible strain sensors to achieve the closed-loop feedback control of motion. First, a flexible strain sensor was fabricated by injecting liquid metal (LM) into an elastomer with a wavy structure. The key performances of the sensor were investigated. The developed flexible strain sensor can achieve a high gauge factor of 2.07, a low hysteresis of 1.902%, a wide detection range of 150% strain, a low detection limitation as low as 0.4% strain change, and an excellent stability and durability, which fully satisfy the requirements for the proprioception of soft robots. We built a closed-loop feedback control system to achieve closed-loop control of the ISCR. Finally, we successfully realized the continuous motion of the ISCR under closed-loop control. We believe this work will be helpful for the development of automation and intelligence of soft crawling robots.

Sound-Based Depth Estimation of Glass Microchannel in Laser-Induced Backside Wet Etching Using Wavelet Transform

AbstractLaser-induced backside wet etching (LIBWE) has been proposed to fabricate high-quality micromachined components on transparent materials. However, the process is limited by poor repeatability when fabricating high-aspect-ratio structures, even under the same conditions due to uncertainties arising from the thermal process and the complex mechanisms associated with the indirect irradiation of the etching process. Such errors could lead to redundant trials and wastages when trying to achieve the desired dimension. To identify the factors causing these variations, we targeted the process sounds generated during the etching. This study uses a microphone to measure factors that result in variations in material removal quantity during the etching process under the same conditions. The sound was filtered at frequencies between 3 and 6 kHz, which were selected as characteristic frequencies for the process under various laser conditions. By integrating the root mean squared value of the detail coefficient of the wavelet transform, the depth estimation closely matched the measured depth of the fabricated part. This finding suggests that determining the etching rate from sound at a certain characteristic frequency during the LIBWE process is feasible; this approach can improve the accuracy and repeatability of the process. Based on this estimation mechanism, we designed a closed-loop feedback control system capable of fabricating highly accurate microchannels in the range of 80–120 μm with a maximum error of 5.6%.

Design and analysis of root locus based controllers

The root locus method is the classical method for analyzing the variation of the position of the poles of a closed-loop control system transfer function in the complex plane. It is also the basis for guiding the design of various controllers. Starting from the nature of the root locus, this paper takes a standard form of closed-loop feedback control system as an example, and analyzes in detail the internal logic of designing a controller and compensator by the root locus method. At the same time, software such as MATLAB/Simulink, and Octave are used. The working principles and properties of the PD controller; the lead and lag compensator, their interconnections, advantages, and characteristics, as well as the adjustment of parameters to better suit practical needs, are sorted out. The PD controller improves the system response, the lead compensator eliminates the PD controller’s defects, and the lag compensator reduces the steady-state error of the system. It provides help for understanding the essence of controller design and provides design ideas for control problems in actual engineering.

Skydiving technique analysis from a control engineering perspective: Developing a tool for studying human motor equivalence

This study offers a novel mathematical approach to sports technique analysis and motor equivalency investigation. The method is aimed to deal with intensive interaction between the environment and a trainee, and activities for which imitation learning is less efficient and movements must be established individually. The free-fall stage of skydiving is investigated, when aerial manoeuvres are performed by changing the body posture and thus deflecting the surrounding airflow. The natural learning process of body flight is hard and protracted as the required movements are not similar to our natural movement repertoire, are often counter-intuitive, and highly dependant on anthropometric factors. The proposed method extracts the trainee’s movement patterns via Principal Component Analysis, and analyses the performed manoeuvres by modelling the dynamic response of the closed-loop feedback control system comprising the body actuated by each pattern and the environment. The method can provide a valuable insight into the subject’s learning process. The main novelty is that instead of comparing trainee’s movements to a template or a movement pattern, extracted from a top-rated athlete, we offer an independent way of technique analysis. Design tools of automatic control theory are utilised to suggest technique modifications that provide the desired performance improvement. An unconventional insight into the motor equivalence problem is achieved: Multiple body degrees-of-freedom are needed for shaping dynamic characteristics of the controlled plant. Movement patterns synergies provide a trade-off between these characteristics, e.g. stability and agility. The method is demonstrated on three case studies of aerial rotation of skilled, less-skilled, and elite skydivers.

Chemical-imaging-guided optical manipulation of biomolecules.

Chemical imaging via advanced optical microscopy technologies has revealed remarkable details of biomolecules in living specimens. However, the ways to control chemical processes in biological samples remain preliminary. The lack of appropriate methods to spatially regulate chemical reactions in live cells in real-time prevents investigation of site-specific molecular behaviors and biological functions. Chemical- and site-specific control of biomolecules requires the detection of chemicals with high specificity and spatially precise modulation of chemical reactions. Laser-scanning optical microscopes offer great platforms for high-speed chemical detection. A closed-loop feedback control system, when paired with a laser scanning microscope, allows real-time precision opto-control (RPOC) of chemical processes for dynamic molecular targets in live cells. In this perspective, we briefly review recent advancements in chemical imaging based on laser scanning microscopy, summarize methods developed for precise optical manipulation, and highlight a recently developed RPOC technology. Furthermore, we discuss future directions of precision opto-control of biomolecules.

Real-time quality control of dripping pill’s weight based on laser detection technology

The present work reports developing the first process analytical technology (PAT)-based real-time feedback control system for maintaining the Ginkgo biloba leaf dripping pills weight during manufacturing. The opening degree of the drop valve and the weight of dripping pills were chosen as the manipulated variable and as the controlled variable, respectively. A proportional-integral controller was programmed to automatically reach the desired dripping pills weight by adjusting the opening degree of the drop valve. The closed-loop feedback control system could automatically compensate for the disturbances and ensure a predefined weight of the dripping pills with excellent robustness, high accuracy, and high efficiency during manufacturing. Furthermore, the closed-loop feedback control system improved the process capability of the dripping process, and the process capability index was > 1.67. This study provides a new approach to real-time control of the weight of dripping pills and improves the process capability during Ginkgo biloba leaf dripping pills manufacturing.

Fault Diagnosis Methodology of Redundant Closed-Loop Feedback Control Systems: Subsea Blowout Preventer System as a Case Study

In closed-loop feedback control systems, faults are propagated through the feedback link, which eventually leads to the abnormality of the entire system. Generally, it is very difficult to identify the faults of systems under the influence of the closed-loop feedback link. The existence of redundancy improves the reliability of the system. Meanwhile, it also poses new challenges to the fault diagnosis of multiple redundant systems. In this regard, a causality-based method is proposed for the fault diagnosis of closed-loop feedback control system with multiple modular redundancy. The dynamic Bayesian networks for fault diagnosis are established based on sensor data and system parameters. The networks consist of four layers, which are sensors, performances, monitors, and faults, respectively. Furthermore, the conditional probabilities of the fault nodes are calculated by Noisy-OR and Noisy-MAX models. The proposed method can dynamically evaluate system performance and integrate other monitoring information as evidence to assist faults diagnosis and location. A double modular redundant control system for a subsea blowout preventer is used as a case to demonstrate the proposed method, and the results show that the proposed method has high accuracy. The influence of sampling frequency, noise, and redundancy mode on diagnosis results is studied and discussed in the case study.

A Portable Servoregulation Controller to Automate CO2 Removal in Artificial Lungs.

Artificial lung (AL) systems provide respiratory support to patients with severe lung disease, but none can adapt to the changing respiratory needs of the patients. Precisely, none can automatically adjust carbon dioxide () removal from the blood in response to changes in patient activity or disease status. Because of this, all current systems limit patient comfort, activity level, and rehabilitation. A portable servoregulation controller that automatically modulates removal in ALs to meet the real-time metabolic demands of the patient is described. The controller is based on a proportional-integral-derivative (PID) based closed-loop feedback control system that modulates sweep gas (air) flow through the AL to maintain a target exhaust gas partial pressure (target or ). The presented work advances previous research by (1) using gas-side sensing that avoids complications and clotting associated with blood-based sensors, (2) incorporating all components into a portable, battery-powered package, and (3) integrating smart moisture removal from the AL to enable long term operation. The performance of the controller was tested in vitro for ∼12 h with anti-coagulated bovine blood and 5 days with distilled water. In tests with blood, the sweep gas flow was automatically adjusted by the controller rapidly (<2 min) meeting the specified tEG level when confronted with changes in inlet blood partial pressure of (p) levels at various AL blood flows. Overall, the removal from the AL showed a strong correlation with blood flow rate and blood p levels. The controller successfully operated continuously for 5 days when tested with water. This study demonstrates an important step toward ambulatory AL systems that automatically modulate removal as required by lung disease patients, thereby allowing for physiotherapy, comfort, and activity.

Sensorized Reconfigurable Soft Robotic Gripper System for Automated Food Handling

This article presents a versatile soft robotic gripper system whereby its fingers can be reconfigured into different poses such as scoop, pinch, and claw. This allows the gripper to efficiently and safely handle food samples of different shapes, sizes and stiffness such as uncooked tofu and broccoli floret. The 3D-printed fingers were tested to last up to 25 000 cycles without significant changes in the curvature profile and force output profile. A benchmark experiment was conducted to evaluate the performance of the gripper and state-of-the-art gripping solutions. Capability of versatile soft gripper was optimized by integrating vision and tactile sensing facilities. An object recognition system was developed to identify food samples such as potato, broccoli, and sausage. Position and orientation of food samples were identified and pick-and-place pathway was optimized to achieve the best gripping performance. Flexible tactile sensors were integrated into soft fingers and closed-loop force feedback control system was developed. This allowed the gripper to automatically explore and select the most stable grip pose for different food samples. Integration of vision and force feedback system ensure that objects detected by the system would be firmly gripped. The reconfigurable soft robotic gripper system has been demonstrated to perform high-speed pick-and-place tasks (∼3 s per item) with object recognition system, making it a potential solution to food and grocery supply chain needs.

TACAN: The Shaping of Delay Distribution Under Multipath Fading Channel for Industrial IoT Systems

The wireless-enabled Industrial Internet of Things (IIoT) system is promising due to its flexibility and cable-free deployment. The varying fading channel will lead to the random transmission delays and jitters, which are the major challenges hindering the adoption of wireless communication in mission-critical industrial systems. The performance or even the stability of closed-loop feedback control system will degrade severely with such delays and jitters. As a result, the maximum delay margin should be met to guarantee the performance of the IIoT system. Aiming to better satisfy this requirement, a novel concept to shape the delay distribution under the industrial multipath fading channel is proposed in this article. Consequently, a two-layer closed feedback control algorithm, referred as TACAN in this article, is designed through the decoupling of original optimization function, by which the variance of the delay distribution is minimized to improve the reliability and stability of the IIoT systems. The performance of proposed delay shape control method is verified by both the classical Rician channel model and the field measured industrial fading channel responses.

Review on Haptic Assistive Driving Systems Based on Drivers’ Steering-Wheel Operating Behaviour

With the availability of modern assistive techniques, ambient intelligence, and the Internet of Things (IoT), various innovative assistive environments have been developed, such as driving assistance systems (DAS), where the human driver can be provided with physical and emotional assistance. In this human–machine collaboration system, haptic interaction interfaces are commonly employed because they provide drivers with a more manageable way to interact with other components. From the view of control system theory, this is a typical closed-loop feedback control system with a human in the loop. To make such a system work effectively, both the driving behaviour factors, and the electrical–mechanical components should be considered. However, the main challenge is how to deal with the high degree of uncertainties in human behaviour. This paper aims to provide an insightful overview of the relevant work. The impact of various types of haptic assistive driving systems (haptic guidance and warning systems) on driving behaviour performance is discussed and evaluated. In addition, various driving behaviour modelling systems are extensively investigated. Furthermore, the state-of-the-art driving behaviour controllers are analysed and discussed in driver–vehicle–road systems, with potential improvements and drawbacks addressed. Finally, a prospective approach is recommended to design a robust model-free controller that accounts for uncertainties and individual differences in driving styles in a haptic assistive driving system. The outcome indicated that the haptic feedback system applied to the drivers enhanced their driving performance, lowered their response time, and reduced their mental workload compared to a system with auditory or visual signals or without any haptic system, despite some annoyances and system conflicts. The driving behaviour modelling techniques and the driving behaviour control with a haptic feedback system have shown good matching and improved the steering wheel’s base operation performance. However, mathematical principles, a statistical approach, and the lack of consideration of the individual differences in the driver–vehicle–road system make the modelling and the controller less robust and inefficient for different driving styles.

Feedback Method of Force Controlled by Electrical Muscle Stimulation Based on Infrared Optical Sensing

The goal of the study was to develop a system that can adjust the electrical muscle stimulation parameters for individuals when sharing experiences with stimulation by sensing the degree of muscle contraction during electrical stimulation. If we do not know the appropriate amount of current for stimulation for an individual, the muscles would not contract as we aimed, and we will not be able to share the experience as we expected. In this study, we presented a system estimating fingertip force as the output of electrical muscle stimulation by monitoring the muscle state based on infrared optical sensing for adjusting electrical muscle stimulation parameters for the individual. We developed a regression model based on support vector regression during electrical stimulation using an infrared optical sensor with seven people's data to estimate the pushing force. The coefficient of determination between the measured pushing force and estimated pushing force was greater than 0.8 and 0.9 for the index and middle fingers, respectively. The system can monitor a feedback value of electrical muscle stimulation fingertip control. The system showed the feasibility of infrared optical sensing for the closed-loop feedback control system of the electrical stimulation parameters for an individual.

Application of active magnetic bearings in control and estimation of geared-rotor faults in high speed offset spur gear transmission system

This study is focused on the mathematical modelling, analysis, active transverse vibration control and fault analysis of a single-stage spur geared-rotor system. It uses Active Magnetic Bearings (AMBs) as an auxiliary bearing in addition to the conventional mechanical bearing in rotor fault detection considering the dynamic model with gyroscopic effects. The change in dynamic forces primarily due to the mesh deformation, gear runout, and variable transmission error are considered, which causes unwanted vibration and noise. To prevent the damage of gears and other supporting structures, these forced vibrations and control currents are meticulously investigated. From the dynamic responses of geared-rotor, the AMB control the stiffness and damping to minimize the transmission of forced vibrations to the mountings by closed-loop feedback control system. For an accurate modelling the dynamic transmission error is taken in two orthogonal transverse directions, which has asymmetric transmission error characteristics. Full spectrum is used for frequency domain analysis with tilting phenomena of the gears at high spin speed. An identification algorithm is developed for fault detection and estimation of system parameters from vibration and current of the proposed geared-rotor-AMB system. The robustness of the proposed algorithm is verified with different added percentage of noise and modeling errors.

Number and Distribution of Fiducial Fibers in a Spectroscopic Survey Telescope

To date, the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has been in operation for 12 yr. To improve the telescope's astronomical observation accuracy, the original open-loop fiber positioning system of LAMOST is in urgent need of upgrading. The upgrade plan is to install several fiber view cameras (FVCs) around primary mirror B to build a closed-loop feedback control system. The FVCs are 20 m from the focal surface. To reduce a series of errors when the cameras detect the positions of the optical fibers, we designed fiducial fibers on the focal surface to be fiducial points for the cameras. Increasing the number of fiducial fibers can improve the detection accuracy of the FVC system, but it will also certainly reduce the number of fiber positioners that can be used for observation. Therefore, the focus of this paper is how to achieve the quantity and distribution that meet the requirements of system detection. In this paper, we introduce the necessity of using fiducial fibers, propose a method for selecting their number and present several methods for assessing the uniformity of their distribution. Finally, we implement particle swarm optimization to find the best distribution of fiducial fibers.

Closed-loop control of neural spike rate of cultured neurons using a thermoplasmonics-based photothermal neural stimulation

Objective. Photothermal neural stimulation has been developed in a variety of interfaces as an alternative technology that can perturb neural activity. The demonstrations of these techniques have heavily relied on open-loop stimulation or complete suppression of neural activity. To extend the controllability of photothermal neural stimulation, combining it with a closed-loop system is required. In this work, we investigated whether photothermal suppression mechanism can be used in a closed-loop system to reliably modulate neural spike rate to non-zero setpoints. Approach. To incorporate the photothermal inhibition mechanism into the neural feedback system, we combined a thermoplasmonic stimulation platform based on gold nanorods (GNRs) and near-infrared illuminations (808 nm, spot size: 2 mm or 200 μm in diameter) with a proportional-integral (PI) controller. The closed-loop feedback control system was implemented to track predetermined target spike rates of hippocampal neuronal networks cultured on GNR-coated microelectrode arrays. Main results. The closed-loop system for neural spike rate control was successfully implemented using a PI controller and the thermoplasmonic neural suppression platform. Compared to the open-loop control, the target-channel spike rates were precisely modulated to remain constant or change in a sinusoidal form in the range below baseline spike rates. The spike rate response behaviors were affected by the choice of the controller gain. We also demonstrated that the functional connectivity of a synchronized bursting network could be altered by controlling the spike rate of one of the participating channels. Significance. The thermoplasmonic feedback controller proved that it can precisely modulate neural spike rate of neural activity in vitro. This technology can be used for studying neuronal network dynamics and might provide insights in developing new neuromodulation techniques in clinical applications.

Localization and Control of Magnetic Suture Needles in Cluttered Surgical Site with Blood and Tissue.

Real-time visual localization of needles is necessary for various surgical applications, including surgical automation and visual feedback. In this study we investigate localization and autonomous robotic control of needles in the context of our magneto-suturing system. Our system holds the potential for surgical manipulation with the benefit of minimal invasiveness and reduced patient side effects. However, the nonlinear magnetic fields produce unintuitive forces and demand delicate position-based control that exceeds the capabilities of direct human manipulation. This makes automatic needle localization a necessity. Our localization method combines neural network-based segmentation and classical techniques, and we are able to consistently locate our needle with 0.73 mm RMS error in clean environments and 2.72 mm RMS error in challenging environments with blood and occlusion. The average localization RMS error is 2.16 mm for all environments we used in the experiments. We combine this localization method with our closed-loop feedback control system to demonstrate the further applicability of localization to autonomous control. Our needle is able to follow a running suture path in (1) no blood, no tissue; (2) heavy blood, no tissue; (3) no blood, with tissue; and (4) heavy blood, with tissue environments. The tip position tracking error ranges from 2.6 mm to 3.7 mm RMS, opening the door towards autonomous suturing tasks.

Serial correlated–uncorrelated concurrent space method for process monitoring

Due to the closed-loop feedback control system, the data collected in the industry process is always temporal serial correlated. Moreover, different types of sensor data (such as temperature, pressure, liquid level, flow and so on) would show different temporal serial correlations. Meanwhile, the current process monitoring method considers all types of sensor data equally, ignoring the difference in temporal serial correlations caused by various sensor types. In response to this, serial correlated–uncorrelated concurrent space method is proposed in this work, where process variable original space is divided into serial correlated space and serial uncorrelated space via the degree of temporal serial correlations. In addition, the proposed method not only considers the local monitoring within space via principal component analysis and slow feature analysis respectively, but also monitors the information between-space based on the moving window and the mutual information. Finally, on the basis of the monitoring statistic within space and between-space, the comprehensive monitoring indicator is constructed via the local outlier factor method. The advantage and superiority of the proposed method is illustrated via testing on Tennessee Eastman process and Continuous Stirred Tank Reactor process.

Cascaded robust control of mechanical ventilator using fractional order sliding mode control.

A mechanical ventilator is an important medical equipment that assists patients who have breathing difficulties. In recent times a huge percentage of COVID-19 infected patients suffered from respiratory system failure. In order to ensure the abundant availability of mechanical ventilators during COVID-19 pandemic, most of the manufacturers around the globe utilized open source designs. Patients safety is of utmost importance while using mechanical ventilators for assisting them in breathing. Closed loop feedback control system plays vital role in ensuring the stability and reliability of dynamical systems such as mechanical ventilators. Ideal characteristics of mechanical ventilators include safety of patients, reliability, quick and smooth air pressure buildup and release.Unfortunately most of the open source designs and mechanical ventilator units with classical control loops cannot achieve the above mentioned ideal characteristics under system uncertainties. This article proposes a cascaded approach to formulate robust control system for regulating the states of ventilator unit using blower model reduction techniques. Model reduction allows to cascade the blower dynamics in the main controller design for airway pressure. The proposed controller is derived based on both integer and non integer calculus and the stability of the closed loop is ensured using Lyapunov theorems. The effectiveness of the proposed control method is demonstrated using extensive numerical simulations.

Integrated Prognostics Observer for Condition Monitoring of an Automated Manual Transmission Dry Clutch System

The closed loop feedback control system of an Automated Manual Transmission (AMT) electro-pneumatic clutch actuator is used for intelligent real time condition monitoring, enhanced diagnostics and prognostic health management of the dry clutch system, by integrating with the existing gearbox prognostics observer. The real-time sensor data of the clutch actuator piston position is analyzed for monitoring the condition of the clutch system. Original parameters of the new clutch are stored in the Electrically Erasable Programmable Read-only Memory (EEPROM) of the AMT controller and the real-time data is used by the observer for assessing the degradation/wear of the frictional clutch parts. Also, clutch slip during torque transmission is monitored, using the engine speed and the gearbox input shaft speed from Controller Area Network (CAN). Condition monitoring of clutch system provides enhanced prognostic functionality for AMT system which ensures consistent clutch performance, gear shift quality and timely warning for recalibration, repair and/or replacement of the critical wear and tear parts. Also, systematic analysis of the monitored data provides an accurate diagnosis of a developing fault. Thus, with the advanced control systems in place for AMT, a closed loop feedback based condition monitoring system is modelled for improved diagnostics and prognostics of AMT clutch system.