Accurate Control Research Articles

Endovascular management of haemorrhage and vascular lesions in patients with multiple and/or severe injuries: a systematic review and clinical practice guideline update

Abstract Purpose Our aim was to update evidence-based and consensus-based recommendations for the inhospital endovascular management of haemorrhage and vascular lesions in patients with multiple and/or severe injuries based on current evidence. This guideline topic is part of the 2022 update of the German Guideline on the Treatment of Patients with Multiple and/or Severe Injuries. Methods MEDLINE and Embase were systematically searched to June 2021. Further literature reports were obtained from clinical experts. Randomised controlled trials, prospective cohort studies, and comparative registry studies were included if they compared endovascular interventions for bleeding control such as embolisation, stent or stent-graft placement, or balloon occlusion against control interventions in patients with polytrauma and/or severe injuries in the hospital setting. The diagnosis of pelvic haemorrhage was added post-hoc as an additional clinical question. We considered patient-relevant clinical outcomes such as mortality, bleeding control, haemodynamic stability, transfusion requirements, complications, and diagnostic test accuracy. Risk of bias was assessed using NICE 2012 checklists. The evidence was synthesised narratively, and expert consensus was used to develop recommendations and determine their strength. Results Forty-three new studies were identified. Interventions covered were resuscitative endovascular balloon occlusion of the aorta (REBOA) (n = 20), thoracic endovascular aortic repair (TEVAR) (n = 9 studies), pelvic trauma (n = 6), endovascular aortic repair (EVAR) of abdominal aortic injuries (n = 3), maxillofacial and carotid artery injuries (n = 2), embolisation for abdominal organ injuries (n = 2), and diagnosis of pelvic haemorrhage (n = 1). Five recommendations were modified, and one additional recommendation was developed. All achieved strong consensus. Conclusion The following key recommendations are made. Whole-body contrast-enhanced computed tomography should be used to detect bleeding and vascular injuries. Blunt thoracic and abdominal aortic injuries should be managed using TEVAR/EVAR. If possible, endovascular treatment should be delayed beyond 24 h after injury. Bleeding from parenchymatous abdominal organs should be controlled using transarterial catheter embolisation. Splenic injuries that require no immediate intervention can be managed with observation.

Research on the Application of PID Control Algorithm and Fuzzy Control Theory in the Field of Temperature Control

In the field of industrial automation control systems, PID control algorithms are widely used in various control fields such as temperature, speed and position. Among them, fuzzy PID control can improve control accuracy and stability, can significantly improve product quality and production efficiency, in food processing, chemical engineering, pharmaceutical and textile industries have a wide range of applications. Therefore, this paper comprehensively discusses the theory of fuzzy PID control algorithm and its application in the field of temperature control. The advantages of fuzzy PID control in complex industrial environments are emphasized through a comparative analysis with other temperature control methods as well as traditional linear PID control. The research indicates that by optimizing the control algorithm and hardware design, the fuzzy PID control algorithm can reduce the system overshoot and steady state error, thus improving the control accuracy and response speed. At the same time, the introduction of multivariable PID controllers or the use of advanced multivariable control strategies can achieve coordinated control of multiple variables, thereby improving the performance of the entire system. In addition, the integration of advanced energy management system and data analysis technology can comprehensively monitor and optimize the management of energy consumption, further improving the energy efficiency of the system

Application of Active Components such as DC Generators and Operational Amplifiers in Mechanical Design

DC generators and operational amplifiers have become increasingly important as the need for automation and precise control in mechanical engineering continues to grow. Since their invention in the 19th century, DC generators have played an important role in power systems, especially in applications that require a stable DC power supply. Meanwhile, since the 1960s, operational amplifiers have played an important role in analog circuits, and their applications cover a wide range of fields such as audio processing, signal conditioning, data acquisition and control systems. This paper explores the multiple applications of DC motors and operational amplifiers in mechanical systems and their integration through a literature review approach. It is found that these components not only improve the efficiency and performance of mechanical systems, but also provide higher control accuracy in areas such as robotics and industrial automation. In addition, with the continuous advancement of technology, the combination of the two is expected to realize innovations and breakthroughs in more fields. The research in this paper provides an important reference for the future innovation and development of mechanical engineering

Fuzzy Control of Multivariable Nonlinear Systems Using T–S Fuzzy Model and Principal Component Analysis Technique

In this work, a new nonlinear control method is proposed, which integrates the Takagi–Sugeno (T–S) fuzzy model with the Principal Component Analysis (PCA) technique. The approach uses PCA to reduce the system’s dimensionality, minimizing the number of fuzzy rules required in the T–S fuzzy model. This reduction not only simplifies the system variables but also decreases the computational complexity, resulting in a more efficient control with smooth transient responses and zero steady-state error. To validate the performance of this PCA-based approach for both system identification and control, an interconnected double-tank system was employed. The results demonstrate the method’s capacity to maintain control accuracy while reducing computational load, making it a promising solution for applications in industrial and engineering systems that require robust, efficient control mechanisms.

Enhanced speed sensorless control of IPMSM using integral synergetic observer

Owing to the benefits of uncomplicated design, compact dimensions, and superior power density, interior permanent magnet synchronous motors (IPMSMs) have garnered the attention of numerous researchers, both nationally and internationally. Nevertheless, conventional IPMSM models incorporating sensors, encoders, and additional apparatus often incur substantial equipment expenses and heavily depend on sensor accuracy for effective control. Consequently, the pursuit of sensorless control has emerged as a prominent trend in the domain of IPMSM control. In this work, a new sensorless control based on an integral synergetic observer (ISO) for an IPMSM is presented. The suggested observer has been developed to overcome the challenges posed by conventional observers, such as fragility against various disturbances, and establishing a sensorless control scheme capable of easily integrating into a modern application like electric vehicles. A comparison through numerical simulation tests using MATLAB software, between the proposed observer and the classic Luenberger observer (LO) clearly showed its qualities and its great importance. Notably, the ISO achieves improved performance metrics, such as the integral absolute error (IAE), integral square error (ISE), and integral time absolute error (ITAE). In low-speed control tests, the proposed ISO reduced the IAE by 56.90%, the ITAE by 55.83%, and the ISE by 75.82% compared to the LO.

Implementation and Preliminary Results of the First Vertical Stabilization Control System for HL-2M

Achieving advanced divertor configurations and high-confinement operating regimes is crucial for mitigating divertor heat loads and exploring enhanced confinement physics in the HL-2M tokamak. However, these scenarios with highly elongated plasmas face severe vertical displacement events that can lead to rapid plasma termination and potential device damage. Robust active control of vertical instability is therefore essential. As HL-2M lacks internal control coils, we developed two sets of vertical stabilization (VS) control systems, each employing a pair of external poloidal field (PF) coils, PF main power supplies, and VS power supplies. This paper details the first vertical stabilization (VS1) control system’s circuit diagram, hardware architecture, and software implementation and discusses issues encountered during commissioning and their solutions. By improving the internal hardware of the VS power supply, the voltage rise time was reduced to approximately 30 μs, resolving branch current imbalances. The transmission delay of the control signals is approximately 38 μs. Preliminary plasma experiments demonstrated effective vertical displacement control with the VS1 control system, achieving a maximum plasma elongation of 1.73 and typical control accuracy of ~20 mm. This work lays the foundation for robust control under high-parameter operational scenarios and the design and implementation of the higher-power second vertical stabilization (VS2) control system.

Model-Free Speed Control for Pumping Kite Generator Systems Based on Nonlinear Hyperbolic Tangent Tracking Differentiator

This paper investigates the emerging field of grid-connected wind-powered pumping kite generator system (PKGS), focusing on the challenges associated with the generator/motor speed control. Conventional use of proportional–integral (PI) controllers faces difficulties in meeting requirements for dynamic response, tracking performance, stability, and disturbance rejection encountered in this technology, notably the periodical variation in the rotational speed reference in maximum power point tracking in generation phases and the dynamic response for the step reference in transient ones. To overcome these limitations, a model-free controller (MFC) approach is introduced, also known as intelligent PID controllers. Unlike traditional methods, MFC does not rely on a control model of the system and adapts to uncertainties and disturbances through online estimation based on the system’s input–output behavior. To further improve the control performances, a tracking differentiator based on a nonlinear hyperbolic tangent function is integrated in the MFC. The effectiveness of the proposed strategy is proved through simulations in MATLAB/Simulink. The results highlight the superior performances of the proposed MFC approach in terms of speed control accuracy, response time, and robustness.

U-Statistic Reduction: Higher-Order Accurate Risk Control and Statistical-Computational Trade-Off

U-statistics play central roles in many statistical learning tools but face the haunting issue of scalability. Despite extensive research on accelerating computation by U-statistic reduction, existing results almost exclusively focused on power analysis. Little work addresses risk control accuracy, which requires distinct and much more challenging techniques. In this paper, we establish the first statistical inference procedure with provably higher-order accurate risk control for incomplete U-statistics. The sharpness of our new result enables us to reveal how risk control accuracy also trades off with speed, for the first time in literature, which complements the well-known variance-speed trade-off. Our general framework converts the challenging and case-by-case analysis for many different designs into a surprisingly principled and routine computation. We conducted comprehensive numerical studies and observed results that validate our theory’s sharpness. Our method also demonstrates effectiveness on real-world data applications.

Low-dimensional controllability of brain networks.

Identifying the driver nodes of a network has crucial implications in biological systems from unveiling causal interactions to informing effective intervention strategies. Despite recent advances in network control theory, results remain inaccurate as the number of drivers becomes too small compared to the network size, thus limiting the concrete usability in many real-life applications. To overcome this issue, we introduced a framework that integrates principles from spectral graph theory and output controllability to project the network state into a smaller topological space formed by the Laplacian network structure. Through extensive simulations on synthetic and real networks, we showed that a relatively low number of projected components can significantly improve the control accuracy. By introducing a new low-dimensional controllability metric we experimentally validated our method on N = 6134 human connectomes obtained from the UK-biobank cohort. Results revealed previously unappreciated influential brain regions, enabled to draw directed maps between differently specialized cerebral systems, and yielded new insights into hemispheric lateralization. Taken together, our results offered a theoretically grounded solution to deal with network controllability and provided insights into the causal interactions of the human brain.

Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review

The integration of neural networks into robotic exoskeletons for physical rehabilitation has become popular due to their ability to interpret complex physiological signals. Surface electromyography (sEMG), electromyography (EMG), electroencephalography (EEG), and other physiological signals enable communication between the human body and robotic systems. Utilizing physiological signals for communicating with robots plays a crucial role in robot-assisted neurorehabilitation. This systematic review synthesizes 44 peer-reviewed studies, exploring how neural networks can improve exoskeleton robot-assisted rehabilitation for individuals with impaired upper limbs. By categorizing the studies based on robot-assisted joints, sensor systems, and control methodologies, we offer a comprehensive overview of neural network applications in this field. Our findings demonstrate that neural networks, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Radial Basis Function Neural Networks (RBFNNs), and other forms of neural networks significantly contribute to patient-specific rehabilitation by enabling adaptive learning and personalized therapy. CNNs improve motion intention estimation and control accuracy, while LSTM networks capture temporal muscle activity patterns for real-time rehabilitation. RBFNNs improve human–robot interaction by adapting to individual movement patterns, leading to more personalized and efficient therapy. This review highlights the potential of neural networks to revolutionize upper limb rehabilitation, improving motor recovery and patient outcomes in both clinical and home-based settings. It also recommends the future direction of customizing existing neural networks for robot-assisted rehabilitation applications.

Eye posture and screen alignment with simulated see-through head-mounted displays.

When rendering the visual scene for near-eye head-mounted displays, accurate knowledge of the geometry of the displays, scene objects, and eyes is required for the correct generation of the binocular images. Despite possible design and calibration efforts, these quantities are subject to positional and measurement errors, resulting in some misalignment of the images projected to each eye. Previous research investigated the effects in virtual reality (VR) setups that triggered such symptoms as eye strain and nausea. This work aimed at investigating the effects of binocular vertical misalignment (BVM) in see-through augmented reality (AR). In such devices, two conflicting environments coexist. One environment corresponds to the real world, which lies in the background and forms geometrically aligned images on the retinas. The other environment corresponds to the augmented content, which stands out as foreground and might be subject to misalignment. We simulated a see-through AR environment using a standard three-dimensional (3D) stereoscopic display to have full control and high accuracy of the real and augmented contents. Participants were involved in a visual search task that forced them to alternatively interact with the real and the augmented contents while being exposed to different amounts of BVM. The measured eye posture indicated that the compensation for vertical misalignment is equally shared by the sensory (binocular fusion) and the motor (vertical vergence) components of binocular vision. The sensitivity of each participant varied, both in terms of perceived discomfort and misalignment tolerance, suggesting that a per-user calibration might be useful for a comfortable visual experience.

Accuracy of apical limit control during retreatment provided by hybrid electronic foraminal locators: A micro-CT study

Accuracy of apical limit control during retreatment provided by hybrid electronic foraminal locators: A micro-CT study

Autonomous Landing of a Fixed‐Wing UAV With RTK GNSS and MEMS IMU

Autonomous landing system is an important part of unmanned aerial vehicle (UAV) autonomous flight. Large fixed‐wing UAVs often consume more onboard space and computing resources. With the improvement of system integration and the reduction of high‐performance global navigation satellite system (GNSS) costs, real‐time kinematic (RTK) GNSS has been widely used in small‐scale and low‐cost fixed‐wing UAVs. This article uses less onboard hardware and ground facility requirements to design an autonomous taking off and landing system suitable for general fixed‐wing UAVs. A modified gradient descent attitude estimator, based on microelectromechanical system (MEMS) inertial measurement unit (IMU) and RTK GNSS, and a guidance method based on a tracking differentiator for using in a high dynamic environment are proposed, and circular path following and vertical transient simulations are performed. The simulation results show that this method can improve attitude estimation accuracy, motion control accuracy, and altitudinal transient quality. Finally, the actual flight test verifies its feasibility in the actual environment. The flight test results prove that it is feasible to implement the autonomous landing process of fixed‐wing UAV with the proposed system and method.

Fractional-order Sliding Mode Control of Manipulator Combined with Disturbance and State Observer

A fractional-order sliding mode control (FSMC) method for a manipulator based on disturbance and state observers is proposed. First, a state estimator is designed that can estimate the velocity and acceleration, and only joint position feedback and the mathematical model of the manipulator are needed. The state estimator converges in finite time. Then, the disturbance observer is designed. By designing the nominal system model of the manipulator, a disturbance observation error is introduced into the closed-loop control so that the performance of the manipulator can track the nominal system. Finally, a sliding mode controller (SMC) based on the fractional differential operator theory is also designed. The value of the sliding mode variable in the derivation of the controller is composed of the fractional derivative of the trajectory tracking error of the manipulator, whereas the fractional differentiation operation uses integration in its realization, and the integration is a low-pass filter, thus, high-frequency noise is suppressed. In the experimental section, the method designed is compared with the conventional sliding mode, which further reveals the rapidity and control accuracy of FSMC.

High-Gain Observer in Fuel Cell and Cascaded DC-DC Boost Converter System

In this study, we propose using a high-gain observer (HGO) in fuel cell systems and the FC-cascade boost converter to achieve precise state estimation and improve control accuracy. The HGO offers significant advantages, including robustness to disturbances, simplicity of design, and reduced dependency on costly sensors, thereby enhancing system performance and reducing operational costs. Our results demonstrate the superior accuracy and reliability of this approach, confirming its potential as a promising solution for fuel cell applications.

Human Collaborative Control of Lower-Limb Prosthesis Based on Game Theory and Fuzzy Approximation.

For leg prosthesis user, the soft tissue and skin under the stump of are not accustomed to weight bearing, excessive continuous contact pressure can lead to the risk of degenerative tissue ulceration. This article presents a novel human-robot collaborative control scheme that achieves control weight self-adjustment for robotic prostheses to minimize interaction torque. To establish the human-robot interaction relationship, we regard the contact pressure between human residual limb and the prosthetic receiving cavity as the interaction force. We aim at reducing the interaction force under the premise of minimally changing the original motion trajectory of the robotic prosthesis. The control scheme mainly includes trajectory optimization based on a dual-agent game control scheme under a cooperative relationship, and a fuzzy logic system for improving the control accuracy of trajectory tracking of robotic prostheses with unknown dynamic parameters. Experiments were carried out on two amputee participants to verify the proposed human-robot interactive control scheme in a robotic prosthesis. The results show that the interaction torque could be reduced while maintaining minimal trajectory tracking error. The proposed control scheme could potentially facilitate the dexterous manipulation of leg prostheses, thus benefiting amputees.

An Intelligent System for Light and Air Conditioner Control Using YOLOv8

High energy consumption in classrooms is a significant concern, often resulting from inefficient lighting and air conditioning systems. Specifically, the problem lies in the lack of automated control mechanisms that adjust energy use based on real-time occupancy data. This study aims to develop and evaluate a system that employs a camera integrated with the YOLOv8 algorithm to detect human presence and optimize energy usage by controlling lights and air conditioning. The system's performance was assessed in three different classroom environments: two large and one small. The system's accuracy for occupancy detection varied from 13.64% to 100%, depending on lighting conditions and room size. Light control accuracy was highest in the classrooms with consistent lighting, reaching 99.77%. Air conditioning control achieved perfect accuracy of 100% in the classroom with a SHARP brand AC, with a maximum remote-control range of 7 meters. These findings indicate that the system's performance is influenced by lighting conditions and room size, with smaller rooms showing better results. The system demonstrates promising potential for reducing energy consumption in classroom settings, thereby contributing to more sustainable energy practices.

Digital Twin Development for the Automotive Industry

This study explores Digital Twin Development for the Automotive Industry which aims to bridge the divide between automotive and digital engineering. Results, which were analyzed using demographic and regression analyses, indicate that there are significant correlations between control accuracy and protection level, but not with data integrity and privacy, and real-time monitoring effectiveness.

Comprehensive Analysis of an EMG Based Human-Robot Interface Using Various Machine Learning Techniques

Electromyography (EMG) signals hold critical significance in biomedical research, capturing muscle electrical activity during rest and contraction in the upper limb. Their versatility in applications, particularly in human-assisting robotic tools, drives ongoing exploration and research. This paper presents an original study focusing on leveraging machine learning techniques to classify EMG datasets and efficiently control a robotic arm based on predicted gestures. Data acquisition involves strategically placing an EMG muscle sensor on the forearm to ensure precise measurement of signals associated with hand gestures and movements. Diverse classifiers including random forest, support vector machine (SVM), K-nearest neighbors (KNN), Gaussian naïve Bayes, gated recurrent unit (GRU), long short-term memory (LSTM), artificial neural network (ANN), recurrent neural network (RNN), convolutional neural network (CNN), and vision transformer (ViT) are employed. Performance results are meticulously analyzed and presented in tabular format, showcasing the ViT classifier as the most successful, achieving an impressive 97.7% accuracy in robotic arm control. Notably, ANN, RNN, and CNN also exhibit high accuracy exceeding 90%. Furthermore, this work is comprehensively compared with existing literature, laying the groundwork for future advancements in human-robot interaction and cutting-edge assistive technologies that markedly enhance the quality of life for individuals with motor impairments or disabilities. The findings carry significant implications for designing and implementing intuitive, responsive robotic systems based on EMG signals.

Optimization of data transmission in sensor networks for enhanced control of ozonator efficiency

The main object of the research is the efficiency of real-time ozonator control based on sensor networks. The study addressed the issue of low efficiency in ozonator control systems and the lack of reliability and speed in real-time data transmission. The research revealed that changes in pressure and temperature have a direct impact on ozone concentration. This finding made it possible to increase the ozonator's productivity by 15 %, reduce energy consumption by 10 %, and improve system reliability by 20 %. The key features of the results include the ability to monitor ozone levels in real-time, maintaining the stability of the ozonator, and optimizing its performance. Additionally, sensor networks ensured fast and accurate data delivery, enhancing the energy efficiency and reliability of the system. These results were explained based on experimental data that demonstrated how changes in pressure and temperature affect ozone concentration. The use of sensor networks contributed to increased system stability, reduced energy consumption, and improved control accuracy. The obtained results can be applied to ozonator systems and other fields requiring real-time environmental monitoring and control. The methods proposed in the study provide opportunities for optimizing industrial processes, reducing costs, and achieving sustainable development goals