Accurate Control Research Articles

The Association Between Motor Competence and Inhibitory Control in Preschool Children

Background: Inhibitory control is a higher-order cognitive function that affects children’s lives and learning, and the development of inhibitory control plays a vital role in the overall development of preschool children. However, most studies have paid more attention to inhibitory and physical fitness, but less focus on motor competence. Therefore, the purpose of this study was to explore the association between motor competence and inhibitory control in preschool children. Methods: A total of 160 preschool children aged 3 to 6 years were selected using a stratified random sampling method, and both motor ability and inhibitory control were assessed. Motor competence was assessed via the Children’s Motor Assessment Battery, Version 2 (MABC-2). Inhibitory control was assessed using the one-on-one iPad-based Early Year Toolbox and reflected by reaction time and accuracy. Results: A total of 153 preschoolers were included in the final statistical analysis. After adjusting the confounders, motor competence was associated with accuracy (β = 0.010, 95% CI: 0.003, 0.017). Similarly, a negative association was observed between motor competence and reaction time (β = −0.008, 95% CI: −0.014, −0.002). Compared with the participants in the lowest group, motor competence (β = 0.051 95% CI: 0.003, 0.098), manual dexterity (β = 0.106 95% CI: 0.043, 0.170), and balance (β = 0.065 95% CI: 0.002, 0.129) were all positively associated with accuracy of those in the highest group. Conclusions: A positive relationship between motor competence and the accuracy of inhibitory control was observed in preschoolers, whereas a negative relationship between motor competence and the reaction time of inhibitory control was also identified. Enhancing preschool children’s motor competence is likely to facilitate their development of inhibitory control.

MATHEMATICAL MODELING AND ANALYSIS OF DYNAMIC CHARACTERISTICS OF A BRUSHLESS MOTOR IN AUTOMATED CONTROL SYSTEMS

The article provides a detailed analysis of brushless motors (BM) within the context of automated control, focusing on dynamic modes and mathematical modeling of the electric drive. It addresses the dynamic characteristics, transfer function development, and specific operational aspects of BM in systems with regulated rotational speed. The article presents experimental data and computer simulation results that validate the effectiveness of the selected models. Experimental studies conducted on a laboratory model confirm the accuracy of the developed mathematical model, particularly demonstrating the feasibility of approximating the motor's transfer function in a linear form to simplify control system analysis and tuning. Computer simulations are conducted in MATLAB using SIMULINK, which enables precise and illustrative visualization of transient processes in the BM. The article also introduces a subordinate control principle within a closed-loop control system, which ensures control stability and accuracy by dividing into speed and current loops. The simulation results demonstrate the potential of utilizing the developed models for further research and the design of efficient and reliable control systems for brushless motors in industrial settings. The proposed modeling approach paves the way for the creation of energy-efficient electric drives with high-precision control.

CSTR parameter identification and PID control optimization based on improved swarm intelligence algorithm

Abstract Aiming at the problems of strong coupling and large hysteresis that exist in the production process of a typical thermal object continuous stirred reactor (CSTR), which make it difficult to recognize the system model parameters and control the temperature, an improved firefly algorithm is proposed to recognize the system parameters, and its recognition accuracy is higher than that of the original firefly algorithm; and an improved sparrow search algorithm (BCGSSA) is used to control the temperature of the CSTR system by combining the BCGSSA algorithm with PID control, which significantly improves the dynamic performance of the CSTR system. Then an improved sparrow search algorithm (BCGSSA) is combined with PID control to control the temperature of the CSTR system, which significantly improves the dynamic performance of the CSTR, and the improved algorithm (BCGSSA) has good control accuracy and robustness.

Design of a force-controlled end-effector based on the pneumatic artificial muscle for robotic polishing

Purpose Polishing is a crucial process in mechanical manufacturing. The use of industrial robots to automate polishing is an inevitable trend in future developments. However, current robotic polishing tools are too large to reach inside deep holes or grooves in workpieces. This study aims to use a pneumatic artificial muscle (PAM) as the actuator and designs a force-controlled end-effector to reach inside the deep and narrow areas in the workpiece. Design/methodology/approach This approach first addresses the challenge of converting the tensile force generated by the PAM into pushing force through mechanism design. In addition, a dynamics model of the end-effector was established based on the three-element model of the PAM. A combined control strategy was proposed to enhance force control accuracy and adaptability during the polishing process. Findings Experiments were conducted on a robotic platform equipped with the proposed end-effector. The experimental results demonstrate that the end-effector can polish the inner end face of holes or grooves with diameters as small as 80 mm and depths reaching 200 mm. By implementing the combined control strategies, the target force tracking error was reduced by 48.66% compared to the use of the PID controller alone. Originality/value A new force-controlled end-effector based on the PAM is designed for robotic polishing. According to the experimental result, this end-effector can polish not only the outer surfaces of the workpiece but also the internal surfaces of workpieces with deep holes or grooves specifically. By using the combined control strategy proposed in this paper, the end-effector significantly improves force control precision and polishing quality.

Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning Chebyshev neural network control approach

Purpose This paper aims to investigate the attitude synchronization issue of multi-spacecraft formation flying systems under the limited communication resources. Design/methodology/approach The authors propose a distributed learning Chebyshev neural network controller (LCNNC) combining a dynamic event-triggered (DET) mechanism and a learning CNN model to achieve accurate multi-spacecraft attitude synchronization under communication constraints. Findings The proposed method can significantly reduce the internal communication frequency and improve the attitude synchronization accuracy. Practical implications This method requires the low communication resources, has a high control accuracy and is thus suitable for engineering applications. Originality/value A novel DET mechanism-based LCNNC is proposed to achieve the accurate multi-spacecraft attitude synchronization under communication constraints.

Applications and Progress of Machine Learning Techniques in the Ladle Furnace Refining Process: A Review

Machine learning techniques have now become key technologies in optimizing the ladle furnace (LF) refining process, significantly improving prediction accuracy and operational efficiency in various aspects such as temperature control, slag optimization, and alloying processes. This article first reviews the principles of neural networks, utilizing their significant advantages in data processing capability and model generalization to explore the current applications and development trends of machine learning techniques in the LF refining process. It analyzes the advantages of hybrid dynamic models and ensemble learning methods in improving prediction accuracy and handling complex data. Additionally, this article explains the optimization of the LF refining process by combining machine learning techniques with traditional methods to reduce costs and improve production efficiency. It demonstrates the application effects and potential value of machine learning techniques in actual production, discusses the strategies and challenges in real‐time control and prediction accuracy, and envisions the future development direction of intelligent models for the LF refining process, providing a scientific basis and technical reference for the subsequent development of LF refining technology.

Modal Frequency and Damping Identification of the FAST Cabin-Cable System

The Five-Hundred-Meter Aperture Spherical Radio Telescope (FAST) faces challenges in establishing high-precision rigid connections between the receiver and the reflective surface due to its vast spatial span. Innovatively, FAST suspends the feed cabin in mid-air using six supporting cables. The precise positioning of the feed focal point is achieved through the coordinated control of cable extension and retraction, along with the A-B axis and the Stewart platform within the cabin. The cables and the feed cabin form a large parallel mechanism. Since the cables are flexible, and the feed cabin remains at a high altitude during observations, it is inevitably subject to internal and external disturbances. To quickly dissipate these disturbances, the system requires a certain level of damping, which directly affects the pointing and tracking accuracy of FAST. During the 2022–2023 operational period, there were multiple instances where the pulleys of the curtain mechanism on the supporting cables became stuck and were carried to the top of the towers by the cables. This also led to the phenomenon where the pulleys, after being stuck, would rapidly slide down the cables due to accumulation. At such moments, the cabin-cable system would experience instantaneous excitation, causing vibrations. This study uses the intrinsic time-scale decomposition (ITD) method to analyze the inertial navigation data installed in the cabin during these events, identifying modal frequencies and damping ratios. The analysis results show that the lowest primary vibration frequency of the FAST cabin-cable suspension system ranges from approximately 0.12 to 0.2 Hz, with a damping ratio of no less than 0.004. These data indicate that the current structure of FAST has a strong energy dissipation capability, providing important reference points for improving the control accuracy of FAST and for the upgrade of the feed support system.

Study on Self-tuning of Robot Parameters for EMC Vehicle Steering Test

With the continuous advancement of electric vehicles and smart internet technologies, ensuring vehicle safety through electromagnetic compatibility (EMC) testing in complex electromagnetic environments has become increasingly critical. However, due to the significant variability in vehicle response characteristics under electromagnetic interference, traditional PID control methods for steering robots struggle to meet the high-precision requirements of such tests. In this study, a novel fuzzy PID parameter self-tuning method is proposed, leveraging a Sparrow Search Algorithm-Back Propagation (SSA-BP) neural network. This method optimizes the fuzzy controller's quantization factor by constructing a neural network system where the expected motor angle serves as the input and the quantization factor as the output. The quantization factor is then calibrated online through iterative training.The proposed approach enables the steering robot to achieve real-time, adaptive tuning of the PID parameters for the drive motor by adjusting the steering torque according to different vehicle characteristics, thereby enhancing the robot's anti-interference capability and robustness in EMC testing. The effectiveness of this method is validated through Matlab/Simulink simulations, experiments conducted on the Sensodrive platform, and tests performed in an EMC anechoic chamber. The results indicate that the method offers substantial improvements in control accuracy and anti-interference capabilities, highlighting its strong potential for practical application.

Advanced Control Strategies for Cleaner Energy Conversion in Biomass Gasification

The escalating climate crisis necessitates urgent and decisive action to mitigate greenhouse gas emissions. Gasification stands out as a highly adaptable process for energy conversion, capable of handling a wide range of feedstocks, from coal to biomass. The process plays a significant role in improving sustainability by converting these feedstocks into synthesic gas (syngas), which can be used as a cleaner energy source or as a building block for producing various chemicals. The utilization of syngas obtained through gasification not only reduces the reliance on fossil fuels but also helps in reducing greenhouse gases (GHGs), thereby contributing to a more sustainable energy landscape. To maintain optimal operational conditions and ensure the quality and safety of the product, an effective control system is crucial in the gasification process. This paper presents a comparative analysis of three control strategies applied to a numerical model of rice husk gasification: classical control, fuzzy logic control, and dynamic matrix control. The analysis is based on a comprehensive model that includes the equations necessary to capture the dynamic behavior of the gasification process across its various stages. The goal is to identify the most effective control strategy, and the performance of each control strategy is evaluated based on the integral of the absolute value of the error (IAE). The results indicatethat fuzzy logic control consistently outperforms classical control techniques, demonstrating superior disturbance rejection, enhanced stability, and overall improved control accuracy. These findings highlight the importance of selecting an appropriate advanced control strategy to optimize sustainable gasification processes.

Digital Twin-Based Smart Feeding System Design for Machine Tools

With the continuous development of intelligent manufacturing technology, the application of intelligent feed systems in modern machine tools is becoming increasingly widespread. Digital twin technology achieves the monitoring and optimization of the entire life cycle of a physical system by constructing a virtual image of the system, while neural network controllers, with their powerful nonlinear fitting ability, can accurately capture and simulate the dynamic behavior of complex systems, providing strong support for the optimization control of intelligent feed systems. This article discusses the design and implementation of an intelligent feed system based on digital twins and neural network controllers. Firstly, this article establishes a mathematical model based on the traditional ball screw structure and analyzes the dynamic characteristics and operating mechanism of the system. Subsequently, the mathematical model is fitted using a neural network controller to improve control accuracy and system response speed. The experimental results demonstrate that the neural network controller shows good consistency in fitting traditional mathematical models, not only effectively capturing the nonlinear characteristics of the system but also maintaining stable control performance under complex operating conditions.

Tilt noise extraction method based on fourier transform and fitting of 2D images

Tilt control is necessary for high-power coherent beam combining systems with large array elements. A tilt noise extraction method based on 2D images has been developed, and the analysis of abnormal situations has been executed. The method achieves high accuracy solution of tilt after two-step operation of Fourier transform and fitting of interference fringes at the detecting module, and then feeds back the compensated amount to the controlling module to achieve tilt control. Monte Carlo simulation is used to solve the control accuracy of this method. The tilt residuals of the method were calculated at various intrinsic shears and data lengths. Results of the effect of the magnitude of the intrinsic shear and the amount of data on the tilt residuals verified the accuracy and stability of the method.

Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm

The increasing complexity of modern plant systems and the limitations of precise mathematical modeling have led to a shift towards data-driven control methods. These methods present an effective alternative that treats plants as black-box systems without requiring explicit models. The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. Nonetheless, control performance can be limited by the fixed probability coefficient used in the original SEDA to disrupt design parameters, especially when balancing exploration and exploitation phases. This study proposes an enhanced version of the SEDA: the modified SEDA (MSEDA), to address this issue. The MSEDA introduces a dynamic probability coefficient that decreases with each iteration. The adjustment improves the balance between exploration and exploitation phases, which enhances control accuracy. The MSEDA was used to tune the brain emotional learning-based intelligent controller (BELBIC) together with a proportional-integral-derivative (PID) controller. The result was the BELBIC-PID controller inspired by the limbic system of the human brain, which has high precision. The effectiveness of the proposed MSEDA-BELBIC-PID was validated using simulations on multi-input multi-output (MIMO) systems, with a focus on tracking performance and computational efficiency. The statistical analysis of 30 independent trials demonstrated that the proposed MSEDA-BELBIC-PID was significantly improved over the original SEDA-BELBIC-PID. Wilcoxon's rank test yielded a fitness function p-value < 0.05, which confirmed the robustness and effect of the proposed enhancement. The comparative results demonstrated that the MSEDA-BELBIC-PID consistently performed better than the original approach and had improved fitness function values, reduced total integral square error, and lower total integral square input. These findings underscored the MSEDA suitability as a data-driven tool for controller design parameter optimization. Furthermore, the low computational burden of MSEDA rendered it a strong alternative to heuristic multi-agent algorithms, which frequently encounter high computational costs with large controller design parameters.

Tailoring Solution Accuracy for Fast Whole-Body Model Predictive Control of Legged Robots

Tailoring Solution Accuracy for Fast Whole-Body Model Predictive Control of Legged Robots

Development and research of methods for strengthening the blades of a straw spreader

Abstract In this article, a comprehensive analysis of existing materials, hardening methods, design and features of the work of parts in the composition of aggregates is carried out. Based on this analysis, the choice of material for the base and reinforcing coating was carried out, and the most effective ways of strengthening the blades of the straw spreader were determined, which are installed on combines instead of similar imported parts. In the course of the study, the spread of blanks and hardened parts by weight, the degree of wear of knives in real operation, as well as the chemical and phase composition, structure and properties of reinforcing coatings were studied. 65G steel is proposed as a modern import-substituting material, and 6HV2C steel is proposed as a promising one. To increase the hardness and wear resistance of the knife blade, the technology of induction surfacing of modified hard alloys (composites) using high-frequency transistor converters (inverters) with a programmable mode is proposed. This technique makes it possible to achieve a more uniform heat distribution and ensure high accuracy of temperature control during the surfacing process, which improves the mechanical properties of the coating. The use of boron carbide and spherical tungsten carbide as a wear resistance modifier makes it possible to increase the hardness of the coating to 62-67 HRC, suppress the growth of dendritic structures, increase the adhesion of the functional filler to the composite matrix, reduce dispersion and increase the microhardness of the carbide phase, which together increases the potential wear resistance of the coating. The use of induction surfacing of modified hard alloys on knife blades contributes to a significant increase in their service life by increasing resistance to mechanical damage and wear.

MIMO ultra-local model-based adaptive enhanced model-free control using extremum-seeking for coupled mechatronic systems

Multiple degree-of-freedom (DOF) mechatronic systems, such as robots and robotic arms, play a crucial role in modern life and production. However, due to strong coupling, uncertain dynamics, and external disturbance, accurately modeling these systems is challenging, making traditional model-based control methods impractical. To address this, this paper proposes an extremum-seeking-based adaptive enhanced model-free control for multi-input multi-output (MIMO) mechatronic systems to realize robust trajectory tracking. Unlike previous model-free control methods that decouple and reorganize the MIMO system into several single-input single-output ultra-local models, this paper develops a MIMO ultra-local model with a non-diagonal gain matrix α to approximate the system dynamics within an ultra-short time window. Time-delay estimation (TDE), Proportional-Derivative (PD) control law and accuracy compensation compose an TDE-based enhanced intelligent PD control that ensures the closed-loop stability. Furthermore, an extremum-seeking (ES) technique is designed to optimize the gain matrix α to enhance control performance. The main contributions of this paper are the development of a model-free control framework based on the MIMO ultra-local model and the successful application of ES to optimize the non-diagonal gain matrix α. Stability analysis of the closed-loop system is conducted using Lyapunov theorem. Finally, numerical simulations on a 2-DOF robotic manipulator and co-simulation results on a 3-DOF PUMA 560 robotic manipulator validate the effectiveness and superiority of the proposed methods.

Research on deformation prediction of CFETR multi-purpose overloaded robot based on real-time structural simulator

The CFETR Multi-Purpose Overload Robot (CMOR) is a key subsystem of the China Fusion Engineering Test Reactor (CFETR), which can perform maintenance tasks of the internal components. However, the large slenderness ratio of the structure results in low control accuracy of the CMOR end-effector. This paper proposes a CMOR deformation prediction method based on the real-time structural simulator. The overall deformation model of CMOR is analyzed based on the single joint deformation mechanism. Based on the principle of layered control, the control framework of the CMOR structural simulator is constructed, and the deformation data of CMOR in different positions are calculated based on the finite element method. A hybrid neural network containing a multilayer perceptron, transformer, and attention mechanism is designed to train the CMOR deformation prediction model. The training results show that the deformation prediction model converges quickly and fits the deformation of the CMOR structure well with small prediction errors. Finally, the real-time structure simulator is developed based on the deformation prediction model, and the deformation of CMOR is reconstructed with the update frequency of 2 Hz and the absolute error at any point within ±10 mm, which verifies the correctness of the CMOR structural deformation prediction method.

Optimization Design and Experimental Study of Solid Particle Spreader for Unmanned Aerial Vehicle

This study designed and investigated a solid particle spreader, as well as parameter optimization and experimental for a groove wheel, to mitigate the problems of low uniformity and poor control accuracy of solid particulate material UAV spreading. The discrete element method was used to simulate and analyze the displacement range and stability of each grooved wheel at low speeds. Furthermore, orthogonal regression and response surface analyses were used to analyze the influence of each factor on the stability of the discharge rate and pulsation amplitude. The results showed that the helix angle, sharpness, and length of the groove significantly influenced the application performance, whereas the number of grooves had no significant influence. The groove shape was eccentric, the helix angle was 50°, the length was 35 mm, and the number of grooves was 7. Additionally, the bench test results showed that in the range of 10–60 rpm, the relative deviation of the discharging rate between the simulation and bench test is from 0.47% to 10.39%, and the average relative deviation is 3.93%. Between the groove wheel rotation speed and discharge rate, R2 was 0.991, and the adjustable range of the discharge amount was between 3.68 and 23.43 g/s. The minimum and maximum variation coefficients of the average discharge rate among individual applicators were 1.01% and 2.79%, respectively, whereas the standard deviations were 0.09 and 0.46 g/s, respectively. In conclusion, the discharge stability and adjustable range of the spreader using the optimized groove wheel satisfied the requirements for solid particulate material discharge.

Green building interior design based on digital image processing and thermal environment simulation

With the increasingly severe global climate change and energy crisis, this study aims to explore effective strategies for green building interior design, especially in terms of thermal environment control, through digital image processing and thermal environment simulation. The study simulates indoor scenes based on image processing technology, using methods such as image acquisition, localization, and object detection to improve the accuracy of scene reconstruction, and conducts in-depth analysis of detection effects, laying the foundation for subsequent thermal environment simulations. In the design of the thermal energy environment control system, the calculation of indoor cooling load was studied and compared with existing thermal environment models to determine the impact of user activities on the indoor thermal environment. By establishing a thermal energy environment control model and implementing precise environmental regulation for different usage scenarios, indoor comfort and energy efficiency have been significantly improved. The experimental results show that the use of digital image processing simulation technology can effectively improve the accuracy and flexibility of indoor thermal environment control in green buildings, optimize energy efficiency, emphasize the importance of thermal factors in interior design, and provide theoretical basis and practical guidance for future building design practices.

Characterization and modelling of carbon black nanocomposite strain sensor on pneumatic driven flexible catheter for pulmonary surgery robots

The pulmonary surgery robot is equipped with a flexible catheter designed to navigate narrow bronchi and perform precise surgical operations on lesions, offering benefits such as minimal invasiveness and high flexibility. This study focuses on the shape control and strain sensing of the catheter’s tip. A gas-driven control platform was developed for a three-channel catheter, incorporating miniaturized precision valves to enhance the accuracy of pneumatic pressure control. Concurrently, ultra-thin flexible strain sensors were fabricated using PDMS and carbon black to detect bending strain, and their sensing performance was assessed to identify optimal fabrication parameters. Finally, the control platform and strain sensors were integrated to validate the system’s capability to manage the catheter’s bending shape while simultaneously sensing its deformation. This integration provides a theoretical foundation for enhancing the precision and safety of catheters in practical applications of surgical robots.

Multi-objective hierarchical energy management strategy for fuel cell/battery hybrid power ships

The energy management strategy and the local controller in the ship energy management system are interconnected, impacting the performance of the hybrid propulsion system. To achieve the efficient operation of the hydrogen fuel cell (FC) and battery hybrid power system, based on the modelling and analysis of the hybrid power system, a nonlinear model predictive control (NMPC) based energy management strategy is proposed, and a dynamic virtual impedance droop controller and a classical proportional-integral (PI) controller are designed as local controllers. By simulating the designed random load conditions, pulse load conditions, and actual sailing conditions using hardware-in-the-loop (HiLs) technology, six different energy management strategies and their comprehensive performance with local controllers are compared and analysed. Comparing performance in terms of energy consumption, operating pressure, control accuracy, real-time performance, and robustness, it has been proven that the energy management strategy based on NMPC, coupled with a PI controller, is superior to other strategies overall. It can balance hydrogen consumption and the stable operation of the hybrid power system. Compared to existing energy management strategies, the proposed NMPC+PI strategy can reduce hydrogen consumption by 7.00 % and 40.29 %, and FC operating pressure by 44.96 % and 49.88 %, respectively, under both designed navigation conditions and actual navigation conditions.