Speed Adjustment Research Articles

DEVELOPING A CONTROLLER FOR AN ADAPTIVE CRUISE CONTROL (ACC) SYSTEM: UTILIZING DEEP REINFORCEMENT LEARNING (DRL) APPROACH

The addition of Adaptive Cruise Control (ACC) to vehicles enables automatic speed adjustments based on traffic conditions after the driver sets the maximum speed, freeing them to concentrate on steering. This study is dedicated to the development of a passenger car ACC system using Deep Reinforcement Learning (DRL). A critical aspect of this ACC system is its capability to regulate the distance between vehicles by taking into account preceding and following vehicle speeds. It considers three primary inputs: the memory-stored speed of the following vehicle, the lead time specified by the driver, and the radar-measured distance. By adapting speed in different traffic scenarios, the system contributes to averting potential accidents. This research delves into constructing a controller that utilizes the Deep Deterministic Policy Gradient (DDPG) algorithm and compares its outcomes with those from the DQN algorithm. The DDPG controller supervises the longitudinal control actions of a vehicle, enabling it to execute stopping and moving maneuvers safely and efficiently.

Application of PID Control Technology in Unmanned Aerial Vehicles

Abstract. The rapid advancement of unmanned aerial vehicle (UAV) technology underscores the essential role of PID (proportional-integral-derivative) control in ensuring flight stability, particularly through precise motor speed adjustments. Thus, this paper systematically analyzes the fundamental principles and limitations of traditional PID control, further highlighting its insufficient adaptability to dynamic external conditions, which includes load fluctuations and wind disturbances. In response to these challenges, it explores adaptive control strategies such as self-tuning PID and fuzzy logic PID, points out how these advanced methods can effectively improve flight stability by dynamically adjusting control parameters, and demonstrates their superiority over traditional PID through comparative analysis. In addition, the paper anticipates future developments in UAV control systems, thereby emphasizing the integration of artificial intelligence and machine learning technologies into PID control. And this integration seeks to optimize control strategies and markedly enhance the autonomous decision-making capabilities and adaptability of UAVs. Meanwhile, the paper also predicts the development trend of hybrid control systems, that is, combining PID with other advanced control methods (such as linear quadratic control) to enhance the overall control capabilities of UAVs. In short, it underscores the necessity for continuous innovation and in-depth research in response to the dynamic flight environment and the diverse mission requirements.

Influence of Control Strategy on Heat Recovery Efficiencyin a Single-Duct Periodic Ventilation Device

The subject of the research was a single-duct, decentralised periodic ventilation unit, using accumulative heat exchanger for heat recovery (also called single-core fixed-bed regenerator). It can achieve high efficiency of heat recovery but is vulnerable to pressure differences between the interior of the building and the outside. To counter this, two control strategies were proposed: adjustment of the fan speed based on an air flow sensor and adjustment of the working cycle length based on temperature sensors. The strategies were tested experimentally in actual working conditions. Due to the use of cheap and simple sensors, it was possible to retain the low price of the device. Both control strategies proved to be successful in equalising the amount of supplied and removed air in a single cycle. Moreover, the heat recovery efficiency increased by more than 10% compared to the default working mode.

A Formally Integrated Adaptive Speed Management for Proactive Traffic Safety

The emergence of connected autonomous vehicles (CAVs) represents a key development in the quest to enhance traffic safety. CAVs hold significant promise for improving traffic safety and have great potential to contribute to transportation sustainability. However, their safety depends on the accuracy of the programmed rules and algorithms that guide their decision-making process. This paper introduces an adaptive traffic management system that integrates a formal traffic safety rule defined by pre-established bounds for Traffic Conflict Techniques. This system enables dynamic speed adjustments for vehicles violating the traffic safety rule in order to prevent potential collisions. To evaluate the effectiveness of our approach, we study a traffic flow on the SR528 highway in Orlando, Florida, and analyze the behavior of each vehicle in traffic based on extracted traffic safety indicators such as time-to-collision and space headway. This analysis is performed by the traffic safety rule to identify violating vehicles, and the speed update is achieved through the integration of the computer algebra system Mathematica and a micro-simulation tool called SUMO. Our study aims to improve the safety and efficiency of the traffic flow by combining simulation, TCTs analysis, and semi-formal tools like Mathematica to aid in the decision-making process of vehicles during traffic events, particularly shockwaves. Preliminary results demonstrate the efficacy of our approach in mitigating the impact of shockwaves. After applying the speed update, we observe an increase in time-to-collision and space headway. This indicates improved safety and reduced likelihood of collisions. Our findings highlight the potential of adaptive traffic management systems to enhance transportation safety and efficiency.

Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making.

CHAOTIC RANDOMIZED SPACE VECTOR PULSE WIDTH MODULATION INTENDED FOR INDUCTION MOTOR DRIVES IN INDUSTRIAL APPLICATIONS

In this industrial era, induction motor control plays a vital role, especially in speed control. Adjustment of speed is essential in all control strategies from the application perspective. There are different pulse width modulation methodologies to tune the speed and reduce harmonics. Among various pulse width modulation (PWM) strategies, space vector PWM (SVPWM) proves best for harmonic reduction in drive applications of variable frequency. SVPWM topology is modified by incorporating some randomness, generating switching pulses for the inverter, thereby controlling induction motor drives. A chaotic-based randomized algorithm is synthesized in primitive SVPWM and termed chaotic random SVPWM (CRSVPWM). It is proved from the results that the efficiency and performance are higher for chaotic randomized SVPWM induction drives than all conventional methods of PWM, having higher harmonics, lower modulation index, and less flexibility. CRSVPWM shows the advantages of better fundamental output voltage, more straightforward digital realization, improved harmonic performance, and extreme simplicity in vector selection. It is simulated through MATLAB 2021R, and hardware-based experimental validation was done using the Vivado design suite environment with Spartans-6 FPGA (XC6SLX9-2tqg144).

Assessing the Effect of Toothpastes on Enamel Surface Roughness Using a Custom-Designed and Fabricated Toothbrush Simulator Device for Evaluation.

The primary objective of this study is to emphasize the importance of maintaining optimal oral health through regular toothbrushing practices. To achieve this objective, a custom-designed electromechanical toothbrush simulator device was developed. This innovative tool enables researchers to investigate the impact of abrasive-based whitening toothpastes on enamel surface roughness compared to brushing without toothpaste. The device design is composed of multiple systems, including mechanical, motorization, and toothpaste irrigation components. The device incorporates various components, including mechanical, motorization, and toothpaste irrigation systems. Specifically, the mechanical aspect comprises fabricated metal parts, 3D printed elements, and a load cell for measuring brushing force. The motorization section integrates a microcontroller and a stepper motor, allowing for the adjustment of brushing cycles and speed. Furthermore, the toothpaste irrigation system employs a pump with adjustable speed, along with a toothpaste canister and a waste receptacle. By providing a controlled environment for evaluating the effects of different toothpaste formulations on enamel integrity, this simulator device contributes significantly to advancements in oral care research and product development.

Delay-aware resource allocation for partial computation offloading in mobile edge cloud computing

Mobile Edge Cloud Computing (MECC), as a promising partial computing offloading solution, has provided new possibilities for compute-intensive and delay-sensitive mobile applications, which can simultaneously leverage edge computing and cloud services. However, designing resource allocation strategies for MECC faces an extremely challenging problem of simultaneously satisfying the end-to-end latency requirements and minimum resource allocation of multiple mobile applications. To address this issue, we comprehensively consider the randomness of computing request arrivals, service time, and dynamic computing resources. We model the MECC network as a two-level tandem queue consisting of two sequential computing processing queues, each with multiple servers. We apply a deep reinforcement learning algorithm called Deep Deterministic Policy Gradient (DDPG) to learn the computing speed adjustment strategy for the tandem queue. This strategy ensures the end-to-end latency requirements of multiple mobile applications while preventing overuse of the total computing resources of edge servers and cloud servers. Numerous simulation experiments demonstrate that our approach is significantly superior to other methods in dynamic network environments.

Interaction between material and process parameters during 3D concrete extrusion process

Extrusion of cement-based materials in additive manufacturing is influenced by a complex interaction of various material and process parameters. This study aims to investigate the impact of rheological properties and printing parameters on the extrudability of cement-based materials as well as the interaction between rheological properties of print materials and extrusion speed, as a key printing process parameter. 20 different print materials with diverse rheological properties were extruded at 10 different extrusion speeds. The effect of material parameters (i.e. rheological properties) and process parameters (i.e., extrusion speed) and their interaction during the extrusion process were evaluated using filament continuity, conformity and consistency. At a constant printing speed, a higher yield stress adversely impacted filament continuity in mixtures with a yield stress of over 260 Pa, whereas viscosity showed minimal influence on filament quality within the tested range of the rheological properties. The results of this study highlighted a significant correlation between material and process parameters and that adapting process parameters to variations in material properties is crucial for meeting printing criteria. The filament consistency of the mixtures with high yield stress was more sensitive to change in the extrusion speed than that of mixtures with low yield stress. An opposite trend, however, was observed for filament conformity where low yield stress mixtures showed a higher sensitivity to the extrusion speed. A procedure was suggested and applied to determine the optimum extrusion speed. Optimal extrusion speeds enabled printing mixtures with a broader spectrum of rheological properties with an acceptable visual quality, filament conformity and consistency requirements. With extrusion speed adjustment, the extrudability window was expanded from dynamic yield stress of 108–126 Pa to 108–263 Pa and plastic viscosity of 8.2–12.3 Pa.s to 5.1–16.4 Pa.s.

Design and test of reel speed control system for soybean combine harvester in the strip intercropping mode

At present, the speed of the reel of soybean combine harvester is mainly adjusted manually, and basically does not have the ability to automatically adjust the speed of the reel according to changes in crops or working speed. In the maize-soybean strip intercropping mode, soybean plants are densely planted. During harvesting, the feeding rate increases, making it even more necessary to adjust the reel speed according to the growth status of the crops. This article uses PLC as the main control platform to design a reel speed control system for the existing combine harvester. Tests have shown that the control system can meet the requirements for reel speed control. The relative error of the reel control system’s speed test is 2.6%, the relative error of the operating speed is 6.77%, and the maximum relative error of the automatic control of the reel speed is 5.38%. Compared with traditional reel, it reduces header loss by 2.12%. The soybean combine harvester reel speed control system designed in this article can realize automatic adjustment of the reel speed, reduce feeding losses caused by changes in crop height, changes in feed volume, and untimely adjustment of the reel, and can reduce header loss, increased crop yield.

Unmanned Agricultural Machine Operation System in Farmland Based on Improved Fuzzy Adaptive Priority-Driven Control Algorithm

Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery operating system based on an improved fuzzy adaptive PD control algorithm. Firstly, mechanical kinematic models and fuzzy adaptive control algorithms are introduced to achieve autonomous driving, and parameter settings and speed adjustments are made while considering errors. Secondly, in the autonomous driving operation system, taking a certain rice machine as an example, perception information, trajectory design, dynamic control, operation supervision, and remote control design are carried out. The experimental results show that the improved fuzzy algorithm exhibits smaller deviation results in driving path tracking, with an average error between the actual path and the expected path of less than 0.001 m. In different testing scenarios, compared with the actual control results, the maximum deviation of the control system platform in straight sections is less than 2.8 m, which is more stable. More than 95% of the lateral deviation results in the road sections are within 0.11 m. And the tracking distance error of the proposed method in the straight and curved segments is relatively small, far smaller than other comparative algorithms. The unmanned agricultural machinery operation system proposed in this study can significantly improve the efficiency and accuracy of agricultural machinery work, promote the development of intelligent and modern agricultural machinery, and provide reference value and important contributions to social and economic development as well as the progress and promotion of related technologies.

ADRC Control of Ultra-High-Speed Electric Air Compressor Considering Excitation Observation

With the increasing power of fuel cells, ultra-high-speed electric air compressors (UHSEACs) have been widely used. However, due to the ultra-high speeds involved, UHSEACs face problems such as long speed adjustment times and large speed fluctuations. Compared to other control methods, Active Disturbance Rejection Control (ADRC) is well-suited for highly nonlinear systems like UHSEACs. The Extended State Observer (ESO), a key component of the ADRC, struggles to accurately observe high-frequency excitations. To address this, the first step is to add a cascaded structure to the ESO and design a Current State Extended State Observer (CS-ESO) to better observe the electromagnetic and load excitations in the UHSEAC. The second step involves designing the ADRC based on the CS-ESO and performing speed adjustment simulations. The third step is to build a UHSEAC experimental platform and a conduct speed adjustment experiment. The findings indicate that, compared to the Proportional Integral Derivative (PID) control, the ADRC with the ESO, and the Sliding Mode Control (SMC), the use of the ADRC with the CS-ESO results in a significant reduction in overshoot—by at least 760 RPM under load-increasing conditions and 140 RPM under load-reducing conditions. Furthermore, the speed regulation time is notably decreased by at least 0.2 s and 0.1 s under these respective conditions.

Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learning

Transitions between free-surface and pressurized flows, known as transient mixed flows, have posed significant challenges in urban drainage systems (UDS), e.g., pipe bursts, road collapses, and geysers. However, traditional mechanistic modeling for mixed flows is challenged by the difficult integration of multi-source data, complex equation forms for the discovery of dynamic processes, and high computational demands. In response, we proposed a data-driven model, TMF-PINN, which utilizes a Physics-Informed Neural Network (PINN) to simulate and invert Transient Mixed Flow (TMF) in sewer networks. This model integrates experimental data, simulation results and Partial Differential Equations (PDEs) into its loss function, leveraging the extensive data available in smart urban water systems. A status factor (α) has been introduced to seamlessly link open channel and pressurized flow dynamics, facilitating rapid adjustments in wave speed. On this basis, Fourier feature extraction and quadratic neural networks have been employed to capture complex dynamic processes featuring high-frequency. Validation through three classical cases using the Storm Water Management Model (SWMM) and comparisons with finite volume Harten-Lax-van Leer (HLL) solver reveal that the proposed model circumvents the constraints of spatiotemporal resolution, yielding accurate flow field predictions.

The effect of Managerial Ability on Working Capital and Its speed adjustment: Case of Egyptian non-financial listed Companies.

The effect of Managerial Ability on Working Capital and Its speed adjustment: Case of Egyptian non-financial listed Companies.

Effects of Gait Speed and Sole Adjustment on Shoe–Floor Angles: Measurement Using Shoe-Type Sensor

Background: Assessment of walking with shoes is important for understanding different types of walking in various environments. Methods: In this study, a shoe-type sensor was used to demonstrate the shoe–floor angle in fifteen participants who walked on a treadmill under varying gait speed and sole adjustments, lifting one side of the sole. The shoe–floor angle in the sagittal; the angle of toe-up (θTup) and toe-down (θTdown) and frontal planes; and the angle of pronation (θPro) and supination (θSup) were calculated, and angles at the initial contact and maximum angles were extracted. Results: The results showed that most angles significantly increased with an increase in the gait speed (θTup and θTdown; p < 0.01 both, θPro and θSup; p < 0.02 and 0.04). Conversely, only the supination angle at the initial contact changed significantly, owing to the tilt of the sole (p < 0.01). Conclusion: Shoe movements were more strongly affected by gait speed than by sole adjustment.

Study on the internal flow evolution mechanism and pressure pulsation characteristics of the transient process of variable speed regulation of centrifugal pump

Abstract In view of the fact that it is not possible to quantitatively analyze any change in the internal flow characteristics of traditional centrifugal pumps during variable speed regulation, numerical computational fluid dynamics (CFD) simulations were carried out in this study to obtain the optimal adjustment range of variable speed regulation. A single-stage IS80-65-160 centrifugal pump is modeled in 3D using Pro/E and ICEM software, followed by CFD simulations. Results are compared with experimental data, showing good agreement. Variable speed adjustments ranging from 0.5-1.0 times the original values are applied to observe changes in internal flow characteristics, such as velocity and turbulent kinetic energy. Pressure and velocity variations in different parts of the pump are studied under varied working conditions. Pressure pulsation frequency domain characteristics are measured at monitoring points in the volute. Findings indicate that as flow rate decreases due to variable speed regulation, velocity decreases linearly, while turbulent kinetic energy decreases quadratically in a uniform distribution. Pressure fluctuations within the volute remain within 0.01 Cp range, but at the tongue, fluctuations can reach up to 0.6 Cp, suggesting an optimal adjustment range within 30% of the rated value.

User expectations for an automated motorway access assistant system

IntroductionTo avoid the uncertainty present in a cooperation between manual vehicles (MVs) and automated vehicles (AVs), cooperative connected automated mobility (CCAM) is frequently proposed. In this contribution, we consider the situation of a motorway slip road where an AV (SAE level 3 or higher) on the motorway cooperates by opening a suitable gap for an entering AV. To ensure a meeting of the cooperation partners, speed adjustment by the system might often be necessary.MethodsTo investigate the acceptance of an automated cooperative motorway access assistant a driving simulator study (N = 41) was conducted. Participants drove six laps on a virtual test track in a level 3 AV. Three levels of speed adjustment and two levels of an HMI (with or without HMI) were tested. They experienced the automated cooperative motorway access assistant in two situations. Firstly, when they entered the motorway themselves and benefited from the system, and secondly, when the system reduced their vehicle's speed to allow for cooperation with another vehicle on a motorway slip road ahead.ResultsThe results show that the acceptance of the assistant depends significantly on the magnitude of speed reduction: the greater the speed adjustment, the worse the user rating, and the more often the cooperation was terminated. Obviously, the acceptance depends on the perspective: If the users themselves benefit from the speed adjustment of others, they rate the system significantly better than if they themselves must accept the restriction. Only in the case of a small speed reduction, the assistant is rated as well as if the user benefits from the system. The availability of a human-machine interface (HMI) providing information about the system status influenced acceptance only marginally but had a significant influence on the trust in the automation.DiscussionThe results suggest that people are willing to accept a personal disadvantage in order to give others an advantage in the context of CCAM. However, acceptance depends on the magnitude of the disadvantage. The overall agreement scores of the system with HMI did not differ from those without HMI, although the HMI was found to slightly reduce the termination of cooperation. Three possible reasons for this result, which differs from previous research, are discussed.

Research on dynamic performance and mechanical model of a novel magnetorheological shear thickening damper with adaptive variable damping gap

Abstract Magnetorheological shear thickening fluid (MRSTF) is an intelligent composite material, which shows considerable potential in semi-active vibration control. However, the current research is mostly limited to the study of the rheological properties of materials, and there is still a lack of damper structures that can effectively utilize the dual-control characteristics of MRSTF. In this paper, a novel damper structure with adaptive adjustable damping gap length is proposed. Its unique design integrates an adaptive internal damping gap that changes with the movement of the piston and an external gap controlled by the magnetic field, providing a relatively wide range of current-controlled damping force and reliable adaptive adjustment capabilities. Based on the M-S shear stress constitutive model and structural characteristics, the mechanical model is derived from the force balance of the micro-element, and verified by performance test and curve fitting. The effectiveness of the new damper structure proposed in this paper is verified by dynamic performance test. The results show that the damper has a wide range of current controlled damping force at low speed and reliable adaptive adjustment capability at high speed. Finally, the accuracy and universality of the modified mechanical model of damper are verified by curve fitting method. All these studies can offer an effective guidance for further application of MRSTF in the field of semi-active vibration control.

Optimizing dose parameters for enhanced maskless lithography in MoS2-based devices

Maskless lithography simplifies the fabrication process and reduces costs compared to electron beam (E-beam) lithography, making it a more efficient choice for patterning nano-devices. Maskless lithography presents a promising avenue for expediting device fabrication by eliminating the need for masks. This technique can streamline the production of basic electronic devices, offering an efficient and low-cost alternative to traditional lithographic methods, like E-beam lithography. This study utilized a 405 nm photodiode to achieve pattern-writing with a minimum linewidth of 1 μm. Exploring optimal parameters includes adjustments in beam intensity, scan speed, and step size. Maskless lithography was applied to 2D transition metal dichalcogenides (TMDCs) material, MoS2, to investigate their electrical transport characteristics. The fabricated device exhibits an ON/OFF ratio of ∼1.7 × 106 and a mobility of ∼0.833 cm2/V·s, indicating a high switching efficiency. The results demonstrate optimized maskless lithography's potential for swift and cost-effective fabrication, offering intermediate-resolution patterning capabilities.

Energy-efficient multi-objective distributed assembly permutation flowshop scheduling by Q-learning based meta-heuristics

This study addresses energy-efficient multi-objective distributed assembly permutation flowshop scheduling problems with minimisation of maximum completion time, mean of earliness and tardiness, and total carbon emission simultaneously. A mathematical model is introduced to describe the concerned problems. Five meta-heuristics are employed and improved, including the artificial bee colony, genetic algorithms, particle swarm optimization, iterated greedy algorithms, and Jaya algorithms. To improve the quality of solutions, five critical path-based neighborhood structures are designed. Q-learning, a value-based reinforcement learning algorithm that learns an optimal strategy by repeatedly interacting with the environment, is embedded into meta-heuristics. The Q-learning guides algorithms intelligently select appropriate neighborhood structures in the iterative process. Then, two machine speed adjustment strategies are developed to further optimize the obtained solutions. Finally, extensive experimental results show that the Jaya algorithm with Q-learning has the best performance for solving the considered problems.