Accurate Control Research Articles

Моделювання інжекції газу в надзвукову частину сопла при газодинамічному регу-люванні вектора тяги

Solid-propellant rocket engines are simple in design, highly reliable, and able to store the propellant for a long time without its degradation. Their main feature is that the propellant is a mixture of a solid fuel and a solid oxidizer, thus ensuring a uniform combustion and a stable discharge of the combustion products. However, the combustion rate cannot be controlled, and the combustion cannot be stopped or restarted. This calls for efficient methods of thrust vector control. Gas-dynamic methods, such as a gas injection into the supersonic nozzle area, offer a required flight path control without complex high-power mechanical systems. The importance of this study lies in improving the accuracy and efficiency of rocket flight control, which is critical for today’s space and defense tasks. The numerical simulation of gas-dynamic control systems, in particular by an asymmetric gas injection, allows one to obtain detailed data on the flow behavior and optimize the design and operating conditions of the system. This study is concerned with a full-scale solid-propellant rocket engine with a gas-dynamic thrust vector control system based on the use of asymmetric forces that occur on the nozzle wall when the supersonic flow interacts with the injected transverse jets. To simulate the process in the Ansys Fluent software package, a geometric model of a nozzle with an asymmetric injection of the chamber gas into the supersonic area was developed. The injection flow rate was controlled by moving the valve flap. The simulation was carried out taking into account the temperature dependence of the main thermophysical gas parameters with consideration for dissociation processes by way of data approximation. The approximation was performed using piecewise polynomial functions. Nozzle flow patterns were obtained. The calculated results were compared with experimental test data and shown to be in satisfactory agreement with the lateral force measured during the fire bench tests of the prototype. From a practical point of view, the results obtained may be directly used to improve existing thrust vector control systems and develop new ones. This will improve rocket navigation accuracy, flight stability, and maneuverability, which is critical for complex space and defense tasks.

New Opportunities in Real-Time Diagnostics of Induction Machines

This manuscript addresses the critical challenges in achieving high-accuracy remote control of electromechanical systems, given their inherent nonlinearities and dynamic complexities. Traditional diagnostics often suffer from data inaccuracies and limitations in analytical techniques. The focus is on enhancing the dynamic model accuracy for remote induction motor control in both closed- and open-loop speed control systems, which is essential for real-time process monitoring. The proposed solution includes real-time measurements of input and output physical quantities to mitigate inaccuracies in traditional diagnostic methods. The manuscript discusses theoretical aspects of nonlinear torque formation in induction drives and introduces a dynamic model employing vector control and speed control schemes alongside standard frequency control methods. These approaches optimize frequency converter settings to enhance system performance under varying nonlinear conditions. Additionally, the manuscript explores methods to analyze dynamic, systematic errors arising from frequency converter inertial properties, thereby improving electromechanical equipment condition diagnostics. By addressing these challenges, the manuscript significantly advances the field, offering a promising future with enhanced dynamic model accuracy, real-time monitoring techniques, and advanced control methods to optimize system reliability and performance.

Electronic Skin: Opportunities and Challenges in Convergence with Machine Learning.

Recent advancements in soft electronic skin (e-skin) have led to the development of human-like devices that reproduce the skin's functions and physical attributes. These devices are being explored for applications in robotic prostheses as well as for collecting biopotentials for disease diagnosis and treatment, as exemplified by biomedical e-skins. More recently, machine learning (ML) has been utilized to enhance device control accuracy and data processing efficiency. The convergence of e-skin technologies with ML is promoting their translation into clinical practice, especially in healthcare. This review highlights the latest developments in ML-reinforced e-skin devices for robotic prostheses and biomedical instrumentations. We first describe technological breakthroughs in state-of-the-art e-skin devices, emphasizing technologies that achieve skin-like properties. We then introduce ML methods adopted for control optimization and pattern recognition, followed by practical applications that converge the two technologies. Lastly, we briefly discuss the challenges this interdisciplinary research encounters in its clinical and industrial transition.

Control method for the ship track and speed in curved channels

Due to natural and external influences in curved channels, ships frequently require adjustments in course and speed, posing challenges for existing control methods. Particularly lacking are speed control methods suitable for curved channel navigation. This study initially developed a three-degree-of-freedom MMG model incorporating external interference. It introduced an OP-PID heading controller merging optimal control strategies with traditional PID, adaptable to both external conditions and ship speed, validated through heading control simulations. The study analysed the ship's speed change process, deriving a mathematical expression for advance distance, and proposed a dichotomy-based speed control method to determine speed change points, addressing differential equations with unknown integrands. To mitigate uncertainty errors like parameter inaccuracies in ship maneuvering models and dynamic environmental disturbances, the study proposed a comprehensive control approach. This approach integrates model predictive control, feedback compensation, segment identification, and an enhanced line-of-sight (LOS) guidance method alongside the OP-PID course controller and dichotomy-based speed control. Simulation experiments in the Dongboliao Channel compared the proposed and existing methods. Results demonstrate the proposed method's capability to handle frequent course and speed adjustments effectively, even under model errors and external interference, showcasing superior track deviation and course control accuracy over existing methods.

Deep reinforcement learning attitude control of stabilized platform for rotary steerable system based on extended state observer

A Deep Deterministic Policy Gradient with Extended State Observer (DDPG_ESO) method is proposed to address control accuracy and robustness issues in the attitude control of stabilized platforms for rotary steerable systems. A nonlinear extended state observer is developed to estimate the total system disturbance, which is then integrated into a deep reinforcement learning framework to enhance the controller’s adaptive capabilities. The controller is trained using variable amplitude and frequency external disturbances to improve its ability to suppress these disturbances. The superiority of the DDPG_ESO controller is validated through simulation experiments, demonstrating rapid tracking of toolface angles, reduced overshoot, and maintained steady-state errors. Compared to Proportional Integral Derivative (PID), Active Disturbance Rejection Control (ADRC), and Deep Deterministic Policy Gradient (DDPG) controllers, the DDPG_ESO algorithm exhibits higher robustness and adaptability under varying operational conditions, effectively mitigating the impact of external disturbances and modeling errors. The results indicate potential for practical application in the drilling industry

Fast and Accurate Neutral-Point Voltage Control Under Balanced and Unbalanced Neutral-Point Voltage for Coupled Ten-Switch Three-Phase Three-Level Inverter

Fast and Accurate Neutral-Point Voltage Control Under Balanced and Unbalanced Neutral-Point Voltage for Coupled Ten-Switch Three-Phase Three-Level Inverter

Impact of Glycemic Control on Shoulder Proprioception in Type 2 Diabetes Mellitus: Mediating the Connection - Insights from a Cross-Sectional Analysis.

Shoulder proprioception is vital and this cross-sectional study investigated the association between glycemic control and shoulder joint proprioception in Type 2 Diabetes Mellitus (T2DM). A total of 120 participants, including 60 with T2DM and 60 healthy individuals, were assessed for shoulder joint position sense (JPS) using a digital inclinometer. The T2DM group exhibited significantly greater mean shoulder joint position errors in flexion (4.32° vs 2.15°), abduction, medial rotation, and lateral rotation compared to the healthy group (p < 0.001). The study found significantly greater shoulder joint position errors in the T2DM group compared to the healthy group, highlighting notable proprioceptive deficits in individuals with T2DM. Additionally, a significant positive correlation was found between HbA1c levels and shoulder joint position errors in the T2DM group, suggesting a link between long-term glycemic control and proprioceptive accuracy. The significant positive correlation between HbA1c levels and shoulder joint position errors suggests that poor glycemic control is associated with impaired proprioception in T2DM patients. This underscores the need for comprehensive management strategies to mitigate proprioceptive deficits and improve the quality of life in individuals with T2DM.

Output voltage control of DAB converters based on uncertainty and disturbance estimation.

The dual active bridge (DAB) converter is a power electronic device commonly used for DC voltage regulation and stabilization. However, during its control process, external disturbances, load variations, input voltage variations, switching tube voltage drops, dead time, etc. lead to errors in the control output, thus reducing the control accuracy of the system. Therefore, this article propose a robust control scheme for the output voltage based on uncertainty and disturbance estimator. In this article, an average small-signal model of the dual active bridge converter was established in terms of the basic principles and operation mechanisms, simplifying the controller's design. Then, the basic principles of the uncertainty and disturbance estimator (UDE) are introduced. The small-signal model of the dual active bridge (DAB) converter is applied to the UDE to minimize output voltage error by enabling the controller to directly regulate the shift ratio. Finally, this article discusses the application and effectiveness of the uncertainty and disturbance estimator (UDE) in the simulation and control of dual active bridge (DAB) converters. A series of experimental comparative studies are conducted, demonstrating that this scheme offers significant advantages in suppressing system uncertainties and disturbances.

Predictive control of linear time-varying model for hydraulic erecting systems based on linear expanded state observer

By analyzing the deficiencies of existing hydraulic erecting systems (HESs) control methods, this study proposes a linear time-varying model predictive control (LTV-MPC) method based on the linear extended state observer (LESO) for HESs. First, the working mechanism of HESs is methodically analyzed and the corresponding state space equations are established. Second, the LESO system is designed to estimate the current unknown real-time states. Then, the LTV-MPC is employed to evaluate and output the optimal solution of the servo voltage signal. Finally, through simulation and experiment, the effectiveness of the proposed method is confirmed and discussed. The results show that the displacement error rate of the proposed method is still lower than 0.223% under larger external disturbances, which can effectively improve the control accuracy and stability of the system compared with other methods.

Fast Convergent Antinoise Dual Neural Network Controller With Adaptive Gain for Flexible Endoscope Robots.

Manual rigid endoscopes have defects such as a low efficiency, difficult operation, and safety risks, and the antinoise interference ability, convergence speed, and control accuracy of the neural network control technology for the existing autonomous endoscopes are often ignored. Solving these problems is important for the stable operation of endoscopes. Therefore, a new adaptive fast convergent antinoise dual neural network (AFA-DNN) controller for the visual servo control of ten-degree of freedom flexible endoscope robots (FERs) with physical constraints is proposed in this work. First, the control scheme of the FERs is formulated as a quadratic programming problem, and then, an AFA-DNN visual servo controller is designed for the FERs. The adaptive gains of the controller can accelerate the convergence, improve the antinoise ability, and increase the convergence accuracy of the controller. Then, according to the Lyapunov theory, the fast convergence of the AFA-DNN in finite time is proven for both noise-free and noisy conditions. The experimental results indicate that the FER controlled by the proposed AFA-DNN can accurately track various trajectories and that the AFA-DNN has a better antinoise interference ability, higher convergence accuracy, and faster convergence speed than conventional methods. The convergence speed of the AFA-DNN is increased by a factor of 4.22 by using the adaptive gains. Experiments also indicate that the AFA-DNN remains well functioning under various noise disturbances (such as constant, periodic, linear, and Gaussian noise).

Automated Shape Correction for Wood Composites in Continuous Pressing

The effective and comprehensive utilization of forest resources has become the theme of the global “dual-carbon strategy”. Forestry restructured wood is a kind of wood-based panel made of wood-based fiber composite material by high-temperature and high-pressure restructuring–molding, and has become an important material in the field of construction, furniture manufacturing, as well as derivative processing for its excellent physical and mechanical properties, decorative properties, and processing performance. Taking Medium Density Fiberboard (MDF) as the recombinant material as the research object, an event-triggered synergetic control mechanism based on interventional three-way decision making is proposed for the viscoelastic multi-field coupling-distributed agile control of the “fixed thickness section” in the MDF continuous flat-pressing process, where some typical quality control problems of complex plate shape deviations including thickness, slope, depression, and bump tend to occur. Firstly, the idea of constructing the industrial event information of continuous hot pressing based on information granulation is proposed, and the information granulation model of the viscoelastic plate shape process mechanism is established by combining the multi-field coupling effect. Secondly, an FMEA-based cyber granular method for diagnosing and controlling the plate thickness diagnosis and control failure information expression of continuous flat pressing is proposed for the problems of plate thickness control failure and plate thickness deviation defect elimination that are prone to occur in the continuous flat-pressing process. The precise control of the plate thickness in the production process is realized based on event-triggered control to achieve the intelligent identification and processing of the various types of faults. The application test is conducted in the international mainstream production line of a certain type of continuous hot-pressing equipment for the production of 18 mm plate thickness; the synergistic effect is basically synchronized after 3 s, the control accuracy reaches 30%, and the average value of the internal bond strength is 1.40, which ensures the integrity of the slab. Practical tests show that the method in the actual production is feasible and effective, with detection and control accuracy of up to ±0.05 mm, indicating that in the production of E0- and E1-level products, the rate of superior products can reach more than 95%.

Design and experimental evaluation of a variable pesticide application control system for the air-assisted rubber tree powder sprayer.

In order to address the issues of uneven pesticide deposition and low pesticide utilization in rubber gardens caused by the traditional diffuse plant protection spraying method, this study focuses on the air-assisted powder sprayer and proposes a variable pesticide application control system. A variable pesticide application decision-making model integrating the leaf area index (LAI) was designed based on powdery mildew control standards and individual rubber tree information. According to the target powder spraying accuracy requirements, a control model of the air velocity adjustment device was established and a fuzzy proportional-integral-differential (PID) air velocity control system was developed. The simulation results indicate that the wind speed control system exhibits a maximum overshoot of 2.18% and an average response time of 1.48 s. The field experiment conducted in a rubber plantation revealed that when the air-assisted powder sprayer operates in the variable powder spraying mode, the average response time of the control system is 2.5 s. The control accuracy of each executive mechanism exceeded 95.9%. The deposition coefficient of variation (CV) at different canopy heights was relatively consistent, with values of 35.38%, 36.26% and 36.90%. In comparison to the quantitative mode, the variable mode showed a significant 20.03% increase in the effective utilization rate of sulfur powder. These research findings provide valuable technical support for the advancement of mechanized variable powder spraying equipment in rubber tree cultivation. © 2024 Society of Chemical Industry.

Adaptive neural sliding mode control of an uncertain permanent magnet linear motor system with unknown input backlash in laser processing

In this article, a novel robust and high-precision control scheme is developed for a permanent magnet linear motor system in large-range laser processing subject to unknown system uncertainties and input backlash. First, to enhance the robustness of the controller, an adaptive neural sliding mode control (SMC) scheme is proposed. Besides, to address the system uncertainties, a novel simplified radial basis neural network (RBFNN) is applied in the adaptive SMC scheme. Subsequently, considering that the unexpected chattering introduced by SMC strategy, another RBFNN is employed to approximate the optimal switching gain. In addition, considering that the nonlinear input backlash caused by actuator can also cause the fluctuation of the input signal, an inverse backlash model with the method of reverse compensation is proposed to further minimize the effect of the input backlash on control accuracy. Finally, simulation results, validation experiments, and laser processing experiments are provided to demonstrate that the proposed control scheme significantly reduces the impact of chattering and input backlash. Additionally, it exhibits outstanding control accuracy.

Research on Predictive Control Method of Pigsty Environment Based on Fuzzy Control

At present, most of the environmental control systems of pigsties use direct control methods; when factors, such as temperature and humidity, exceed the set threshold value, the corresponding actuator is turned on for regulation. However, such methods have problems such as low control accuracy, high energy consumption, and untimeliness. In order to save on energy consumption and improve control accuracy, this paper takes the predicted value, set value, and current detection value of the internal environment of a pigsty as input, and combines fuzzy control and direct control methods to realize the predictive control of the pigsty environment. The test results show that, compared with the direct control method, the fuzzy predictive control method can make fluctuations in the internal temperature and humidity of the pigsty less close to the set threshold value, while the ammonia concentration hardly exceeds the set threshold value. The results show that predictive control can more accurately control the internal environment of the pigsty and reduce energy costs by about 20%. Therefore, this method can provide scientific and effective environmental control methods for agricultural production processes, such as livestock breeding and greenhouse cultivation, in semi-confined spaces.

Allo-Hemodialysis, a Novel Dialytic Treatment Option for Patients with Kidney Failure: Outcomes of Mathematical Modelling, Prototyping, and Ex Vivo Testing.

It has been estimated that in 2010, over two million patients with end-stage kidney disease may have faced premature death due to a lack of access to affordable renal replacement therapy, mostly dialysis. To address this shortfall in dialytic kidney replacement therapy, we propose a novel, cost-effective, and low-complexity hemodialysis method called allo-hemodialysis (alloHD). With alloHD, instead of conventional hemodialysis, the blood of a patient with kidney failure flows through the dialyzer's dialysate compartment counter-currently to the blood of a healthy subject (referred to as a "buddy") flowing through the blood compartment. Along the concentration and hydrostatic pressure gradients, uremic solutes and excess fluid are transferred from the patient to the buddy and subsequently excreted by the healthy kidneys of the buddy. We developed a mathematical model of alloHD to systematically explore dialysis adequacy in terms of weekly standard urea Kt/V. We showed that in the case of an anuric child (20 kg), four 4 h alloHD sessions are sufficient to attain a weekly standard Kt/V of >2.0. In the case of an anuric adult patient (70 kg), six 4 h alloHD sessions are necessary. As a next step, we designed and built an alloHD machine prototype that comprises off-the-shelf components. We then used this prototype to perform ex vivo experiments to investigate the transport of solutes, including urea, creatinine, and protein-bound uremic retention products, and to quantitate the accuracy and precision of the machine's ultrafiltration control. These experiments showed that alloHD performed as expected, encouraging future in vivo studies in animals with and without kidney failure.

Optimal control set expansion method for three‐phase voltage source converter with slip window selection and online parameter modification

AbstractFinite control set model predictive control (FCS‐MPC) has been widely used in the control of three‐phase voltage source converter (VSC), but its control performance declines sharply at low sampling frequencies. This paper proposes an optimal control set expansion method with slip window selection and online parameter modification to improve the performance and robustness of FCS‐MPC. First, the performance of different control set expansion methods is compared. Then, an optimal control set expansion method that carries 12 virtual vectors is proposed. Last, a slip window selection strategy and an online parameter modification algorithm are proposed to reduce the computational burden and improve the controller's robustness respectively. Experimental result show that the proposed method significantly improves the control accuracy and carries good robustness and anti‐disturbance ability. Compared with existing typical MPC strategies, the proposed method obtains better current quality with lower switching frequency and requires less calculation. Moreover, the design of the proposed method is simple and there is no weighting factor to be tuned, which increases the method's practicality.

Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression

In recent years, video data have increased in size, which results in enormous transmission pressure. Rate control plays an important role in stabilizing video stream transmissions by balancing the rate and distortion of video compression. To achieve high-quality videos through low-bandwidth transmission, video multi-scale-based end-to-end rate control is proposed. First, to reduce video data, the original video is processed using multi-scale bicubic downsampling as the input. Then, the end-to-end rate control model is implemented. By fully using the temporal coding correlation, a two-branch residual-based network and a two-branch regression-based network are designed to obtain the optimal bit rate ratio and Lagrange multiplier λ for rate control. For restoring high-resolution videos, a hybrid efficient distillation SISR network (HEDS-Net) is designed to build low-resolution and high-resolution feature dependencies, in which a multi-branch distillation network, a lightweight attention LCA block, and an upsampling network are used to transmit deep extracted frame features, enhance feature expression, and improve image detail restoration abilities, respectively. The experimental results show that the PSNR and SSIM BD rates of the proposed multi-scale-based end-to-end rate control are −1.24% and −0.50%, respectively, with 1.82% rate control accuracy.

Quantum model prediction for frequency regulation of novel power systems which includes a high proportion of energy storage

As the proportion of renewable energy generation continues to increase, the participation of new energy stations with high-proportion energy storage in power system frequency regulation is of significant importance for stable and secure operation of the new power system. To address this issue, an energy storage control method based on quantum walks and model predictive control (MPC) has been proposed. First, historical frequency deviation signals and energy storage charge–discharge state signals are collected. Simulation data are generated through amplitude encoding and quantum walks, followed by quantum decoding. Subsequently, the decoded data are inputted into the MPC framework for real-time control, with parameters of the predictive model continuously adjusted through a feedback loop. Finally, a novel power system frequency regulation model with high-proportion new energy storage stations is constructed on the MATLAB/Simulink platform. Simulation verification is conducted with the proportional–integral–derivative (PID) and MPC methods as comparative approaches. Simulation results under step disturbances and random disturbances demonstrate that the proposed method exhibits stronger robustness and better control accuracy.

Robust Speed Control of Permanent Magnet Synchronous Motor Drive System Using Sliding-Mode Disturbance Observer-Based Variable-Gain Fractional-Order Super-Twisting Sliding-Mode Control

This paper proposes a novel nonlinear speed control method for permanent magnet synchronous motors that enhances their robustness and tracking performance. This technique integrates a sliding-mode disturbance observer and variable-gain fractional-order super-twisting sliding-mode control within a vector-control framework. The proposed control scheme employs a sliding-mode control method to mitigate chattering and improve dynamics by implementing fractional-order theory with a variable-gain super-twisting sliding manifold design while regulating the speed of the considered motor system. The aforementioned observer is suggested to enhance the control accuracy by estimating and compensating for the lumped disturbances. The proposed methodology demonstrates its superiority over other control schemes such as traditional sliding-mode control, super-twisting sliding-mode control, and the proposed technique. MATLAB/Simulink simulations and real-time implementation validate its performance, showing its potential as a reliable and efficient control approach for the system under study in practical applications.

Application of ZigBee Technology in Smart Soil Temperature and Humidity Control Systems

As global food security increasingly becomes a focus, challenges faced by Chinese agriculture are particularly prominent. This study introduces an intelligent monitoring and control system for soil temperature and humidity using ZigBee technology, aimed at enhancing agricultural production efficiency and crop yield. The system's core, equipped with an STC89C52 microcontroller and DHT11 temperature and humidity sensors, gathers data and wirelessly transmits it to a central control unit via ZigBee modules, which then automatically adjusts the agricultural production environment to ensure crops grow in the most ideal soil temperature and humidity conditions. Compared to traditional methods, this system achieves remote monitoring and precise control, providing an innovative solution for modern agricultural production. Experiments prove that the system not only improves control accuracy but also significantly enhances operational convenience and response speed, playing an important role in promoting the development of smart agriculture.