Fixed-Time-Convergent Sliding Mode Control with Sliding Mode Observer for PMSM Speed Regulation.

This paper focuses on the speed control of a permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and external disturbances. Conventional sliding mode control (CSMC) can only converge asymptotically in the infinite domain and will cause unacceptable sliding mode chattering. To improve the performance of the PMSM speed loop in terms of response speed, tracking accuracy, and robustness, a hybrid control strategy for a fixed-time-convergent sliding mode controller (FSMC) with a fixed-time-convergent sliding mode observer (FSMO) is proposed for PMSM speed regulation using the fixed-time control theory. Firstly, the FSMC is proposed to improve the convergence speed and robustness of the speed loop, which can converge to the origin within a fixed time independent of the initial conditions. Then, the FSMO is used as a compensator to further enhance the robustness of the speed loop and attenuate sliding mode chattering. Finally, simulation and experimental results show that the proposed method can effectively improve the dynamic performance and robustness of the PMSM speed control system.

In-Wheel Motor Control System for Four-Wheel Drive Electric Vehicle Based on CR-GWO-PID Control.

In order to improve the driving performance of four-wheel drive electric vehicles and realize precise control of their speed, a Chaotic Random Grey Wolf Optimization-based PID in-wheel motor control algorithm is proposed in this paper. Based on an analysis of the structural principles of electric vehicles, mathematical and simulation models for the whole vehicle are established. In order to improve the control performance of the hub motor, the traditional Grey Wolf Optimization algorithm is improved. In particular, an enhanced population initialization strategy integrating sine and cosine random distribution factors into a Kent chaotic map is proposed, the weight factor of the algorithm is improved using a sine-based non-linear decreasing strategy, and the population position is improved using the random proportional movement strategy. These strategies effectively enhance the global optimization ability, convergence speed, and optimization accuracy of the traditional Grey Wolf Optimization algorithm. On this basis, the CR-GWO-PID control algorithm is established. Then, the software and hardware of an in-wheel motor controller are designed and an in-wheel motor bench test system is built. The simulation and bench test results demonstrate the significantly improved response speed and control accuracy of the proposed in-wheel motor control system.

Ecological Interface Design and Head-Up Displays: The Contact-Analog Visualization Tradeoff.

OBJECTIVE: This study investigated how the visualization of an ecological interface affects its subjective and objective usefulness. Therefore, we compared a simple 2D visualization against a contact-analog 3D visualization. BACKGROUND: Recently, head-up displays (HUDs) have become contact-analog and visualizations have been enabled to be merged with the real environment. In this regard, ecological interface design visualizing boundaries of acceptable performance might be a perfect match. Because the real-world environment already provides such boundaries (e.g., lane markings), the interface might directly use them. However, visual illusions and undesired interference with the environment might influence the overall usability. METHOD: To allow for a comparison, 49 participants tested the same ecological interface in two configurations, contact-analog (3D) and two dimensional (2D). Both visualizations were shown in the car's head-up display (HUD). RESULTS: The driving simulator experiment reveals that 3D was rated as more demanding and more disturbing, but also more innovative and appealing. However, regarding driving performance, the 3D representation decreased the accuracy of speed control by 6% while significantly increasing lane stability by 20%. CONCLUSION: We conclude that, if we want environmental boundaries guiding our behavior, the indicator for the behavior should be visualized contact-analog. If we desire artificial boundaries (e.g., speed limits) to guide behavior, the behavioral indicator should be visualized in 2D. This is less prone to optical illusions and allows for a more precise control of behavior. APPLICATION: These findings provide guidance to human factors engineers, how contact-analog visualizations might be used optimally.

Multimodal Bubble Microrobot Near an Air-Water Interface.

The development of multifunctional and robust swimming microrobots working at the free air-liquid interface has encountered challenge as new manipulation strategies are needed to overcome the complicated interfacial restrictions. Here, flexible but reliable mechanisms are shown that achieve a remote-control bubble microrobot with multiple working modes and high maneuverability by the assistance of a soft air-liquid interface. This bubble microrobot is developed from a hollow Janus microsphere (JM) regulated by a magnetic field, which can implement switchable working modes like pusher, gripper, anchor, and sweeper. The collapse of the microbubble and the accompanying directional jet flow play a key role for functioning in these working modes, which is analogous to a "bubble tentacle." Using a simple gamepad, the orientation and the navigation of the bubble microrobot can be easily manipulated. In particular, a speed modulation method is found for the bubble microrobot, which uses vertical magnetic field to control the orientation of the JM and the direction of the bubble-induced jet flow without changing the fuel concentration. The findings demonstrate a substantial advance of the bubble microrobot specifically working at the air-liquid interface and depict some nonintuitive mechanisms that can help develop more complicated microswimmers.

Fast-forward scaling theory.

Speed is the key to further advances in technology. For example, quantum technologies, such as quantum computing, require fast manipulations of quantum systems in order to overcome the effect of decoherence. However, controlling the speed of quantum dynamics is often very difficult due to both the lack of a simple scaling property in the dynamics and the infinitely large parameter space to be explored. Therefore, protocols for speed control based on understanding of the dynamical properties of the system, such as non-trivial scaling property, are highly desirable. Fast-forward scaling theory (FFST) was originally developed to provide a way to accelerate, decelerate, stop and reverse the dynamics of quantum systems. FFST has been extended in order to accelerate quantum and classical adiabatic dynamics of various systems including cold atoms, internal state of molecules, spins and solid-state artificial atoms. This paper describes the basic concept of FFST and reviews the recent developments and its applications such as fast state-preparations, state protection and ion sorting. We introduce a method, called inter-trajectory travel, recently derived from FFST. We also point out the significance of deceleration in quantum technology. This article is part of the theme issue 'Shortcuts to adiabaticity: theoretical, experimental and interdisciplinary perspectives'.

A Hybrid Dragonfly Algorithm for Efficiency Optimization of Induction Motors.

Induction motors tend to have better efficiency on rated conditions, but at partial load conditions, when these motors operate on rated flux, they exhibit lower efficiency. In such conditions, when these motors operate for a long duration, a lot of electricity gets consumed by the motors, due to which the computational cost as well as the total running cost of industrial plant increases. Squirrel-cage induction motors are widely used in industries due to their low cost, robustness, easy maintenance, and good power/mass relation all through their life cycle. A significant amount of electrical energy is consumed due to the large count of operational units worldwide; hence, even an enhancement in minute efficiency can direct considerable contributions within revenue saving, global electricity consumption, and other environmental facts. In order to improve the efficiency of induction motors, this research paper presents a novel contribution to maximizing the efficiency of induction motors. As such, a model of induction motor drive is taken, in which the proportional integral (PI) controller is tuned. The optimal tuning of gains of a PI controller such as proportional gain and integral gain is conducted. The tuning procedure in the controller is performed in such a condition that the efficiency of the induction motor should be maximum. Moreover, the optimization concept relies on the development of a new hybrid algorithm, the so-called Scrounger Strikes Levy-based dragonfly algorithm (SL-DA), that hybridizes the concept of dragonfly algorithm (DA) and group search optimization (GSO). The proposed algorithm is compared with particle swarm optimization (PSO) for verification. The analysis of efficiency, speed, torque, energy savings, and output power is validated, which confirms the superior performance of the suggested method over the comparative algorithms employed.

Speed Control for Leader-Follower Robot Formation Using Fuzzy System and Supervised Machine Learning.

Mobile robots are endeavoring toward full autonomy. To that end, wheeled mobile robots have to function under non-holonomic constraints and uncertainty derived by feedback sensors and/or internal dynamics. Speed control is one of the main and challenging objectives in the endeavor for efficient autonomous collision-free navigation. This paper proposes an intelligent technique for speed control of a wheeled mobile robot using a combination of fuzzy logic and supervised machine learning (SML). The technique is appropriate for flexible leader-follower formation control on straight paths where a follower robot maintains a safely varying distance from a leader robot. A fuzzy controller specifies the ultimate distance of the follower to the leader using the measurements obtained from two ultrasonic sensors. An SML algorithm estimates a proper speed for the follower based on the ultimate distance. Simulations demonstrated that the proposed technique appropriately adjusts the follower robot's speed to maintain a flexible formation with the leader robot.

Controlling the Speed of Light-Activated Colloids with a Constant, Uniform Magnetic Field.

It is demonstrated how the strength of activation for photocatalytic, self-propelled colloids can be enhanced with a constant, uniform magnetic field. When exposed to ultraviolet light and hydrogen peroxide, the titanium dioxide-based colloids become actively propelled. Due to the iron oxide core, a uniform field oriented perpendicular to the surface where motion takes place causes the asymmetrically shaped particles to rotate, which consequently leads to an increase in activity. The field-dependent dynamics of self-propulsion is quantified, and a qualitative description of how this effect arises is proposed. Since the application of the field is easily reversible, modulating the field on-and-off serves as a de facto "switch" that controls particle behavior.

Modulation of ellipses drawing by sonification.

Most studies on the regulation of speed and trajectory during ellipse drawing have used visual feedback. We used online auditory feedback (sonification) to induce implicit movement changes independently from vision. The sound was produced by filtering a pink noise with a band-pass filter proportional to movement speed. The first experiment was performed in 2D. Healthy participants were asked to repetitively draw ellipses during 45 s trials whilst maintaining a constant sonification pattern (involving pitch variations during the cycle). Perturbations were produced by modifying the slope of the mapping without informing the participants. All participants adapted spontaneously their speed: they went faster if the slope decreased and slower if it increased. Higher velocities were achieved by increasing both the frequency of the movements and the perimeter of the ellipses, but slower velocities were achieved mainly by decreasing the perimeter of the ellipses. The shape and the orientation of the ellipses were not significantly altered. The analysis of the speed-curvature power law parameters showed consistent modulations of the speed gain factor, while the exponent remained stable. The second experiment was performed in 3D and showed similar results, except that the main orientation of the ellipse also varied with the changes in speed. In conclusion, this study demonstrated implicit modulation of movement speed by sonification and robust stability of the ellipse geometry. Participants appeared to limit the decrease in movement frequency during slowing down to maintain a rhythmic and not discrete motor regimen.

An Analysis Scheme of Balancing Energy Consumption with Mobile Velocity Control Strategy for Wireless Rechargeable Sensor Networks.

Wireless Rechargeable Sensor Networks (WRSN) are not yet fully functional and robust due to the fact that their setting parameters assume fixed control velocity and location. This study proposes a novel scheme of the WRSN with mobile sink (MS) velocity control strategies for charging nodes and collecting its data in WRSN. Strip space of the deployed network area is divided into sub-locations for variant corresponding velocities based on nodes energy expenditure demands. The points of consumed energy bottleneck nodes in sub-locations are determined based on gathering data of residual energy and expenditure of nodes. A minimum reliable energy balanced spanning tree is constructed based on data collection to optimize the data transmission paths, balance energy consumption, and reduce data loss during transmission. Experimental results are compared with the other methods in the literature that show that the proposed scheme offers a more effective alternative in reducing the network packet loss rate, balancing the nodes' energy consumption, and charging capacity of the nodes than the competitors.

The role of phase shifts of sensory inputs in walking revealed by means of phase reduction.

Detailed neural network models of animal locomotion are important means to understand the underlying mechanisms that control the coordinated movement of individual limbs. Daun-Gruhn and Tóth, Journal of Computational Neuroscience 31(2), 43-60 (2011) constructed an inter-segmental network model of stick insect locomotion consisting of three interconnected central pattern generators (CPGs) that are associated with the protraction-retraction movements of the front, middle and hind leg. This model could reproduce the basic locomotion coordination patterns, such as tri- and tetrapod, and the transitions between them. However, the analysis of such a system is a formidable task because of its large number of variables and parameters. In this study, we employed phase reduction and averaging theory to this large network model in order to reduce it to a system of coupled phase oscillators. This enabled us to analyze the complex behavior of the system in a reduced parameter space. In this paper, we show that the reduced model reproduces the results of the original model. By analyzing the interaction of just two coupled phase oscillators, we found that the neighboring CPGs could operate within distinct regimes, depending on the phase shift between the sensory inputs from the extremities and the phases of the individual CPGs. We demonstrate that this dependence is essential to produce different coordination patterns and the transition between them. Additionally, applying averaging theory to the system of all three phase oscillators, we calculate the stable fixed points - they correspond to stable tripod or tetrapod coordination patterns and identify two ways of transition between them.

Dynamic Speed Control of Unmanned Aerial Vehicles for Data Collection under Internet of Things.

With the new advancements in flight control and integrated circuit (IC) technology, unmanned aerial vehicles (UAVs) have been widely used in various applications. One of the typical application scenarios is data collection for large-scale and remote sensor devices in the Internet of things (IoT). However, due to the characteristics of massive connections, access collisions in the MAC layer lead to high power consumption for both sensor devices and UAVs, and low efficiency for the data collection. In this paper, a dynamic speed control algorithm for UAVs (DSC-UAV) is proposed to maximize the data collection efficiency, while alleviating the access congestion for the UAV-based base stations. With a cellular network considered for support of the communication between sensor devices and drones, the connection establishment process was analyzed and modeled in detail. In addition, the data collection efficiency is also defined and derived. Based on the analytical models, optimal speed under different sensor device densities is obtained and verified. UAVs can dynamically adjust the speed according to the sensor device density under their coverages to keep high data collection efficiency. Finally, simulation results are also conducted to verify the accuracy of the proposed analytical models and show that the DSC-UAV outperforms others with the highest data collection efficiency, while maintaining a high successful access probability, low average access delay, low block probability, and low collision probability.

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver's EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver's vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

Effect of light intensity on flight control and temporal properties of photoreceptors in bumblebees.

To control flight, insects rely on the pattern of visual motion generated on the retina as they move through the environment. When light levels fall, vision becomes less reliable and flight control thus becomes more challenging. Here, we investigated the effect of light intensity on flight control by filming the trajectories of free-flying bumblebees (Bombus terrestris, Linnaeus 1758) in an experimental tunnel at different light levels. As light levels fell, flight speed decreased and the flight trajectories became more tortuous but the bees were still remarkably good at centring their flight about the tunnel's midline. To investigate whether this robust flight performance can be explained by visual adaptations in the bumblebee retina, we also examined the response speed of the green-sensitive photoreceptors at the same light intensities. We found that the response speed of the photoreceptors significantly decreased as light levels fell. This indicates that bumblebees have both behavioural (reduction in flight speed) and retinal (reduction in response speed of the photoreceptors) adaptations to allow them to fly in dim light. However, the more tortuous flight paths recorded in dim light suggest that these adaptations do not support flight with the same precision during the twilight hours of the day.

Adding a lens Improves spinning speed characterization.

Highly stable sample rotation is important in many solid-state NMR experiments. Whether the necessary stability is achieved is not always clear. Typically only an average frequency over some time interval (often relatively long and unknown) is available from the spinning speed controller readout, which is not representative of the short-term variations of instantaneous rotation frequency. The necessity of the relatively slow measurement of spinning speed is a consequence of phase noise in the tachometer, which prevents speed measurement to be both rapid and precise at the same time. We show that adding a lens to the tachometer, without any other changes in the probe, reduces phase noise by nearly an order of magnitude and allows improved measurement of the spinning speed.

Evaluation of point-to-point speed cameras to control speeding behavior in Thailand.

INTRODUCTION: This research examines the effectiveness of point-to-point speed cameras in Thailand compared with spot speed camera enforcement, which is widely used in the country. METHOD: The evaluation includes the speed control's efficiency by observing the 85th percentile average speed of drivers on the road; the speed detection rate; conducting a comparison with spot speed camera enforcement; and a cost effectiveness analysis of this measure. RESULTS: After speed detection using point-to-point cameras were employed during which warning tickets were issued in many days of experiment, the 85th percentile average speed in the controlled area decreased by 10 km/hr, with a maximum decrease of 20 km/hr. When comparing the detection rate of the two camera models, point-to-point cameras could detect 95% of drivers' driving speed, with 45% of the detected drivers identified as speeding drivers, while spot speed cameras were only able to detect 10% to 20% of speeders. Drivers traveling through the area with point-to-point cameras were more likely to drive at a constant speed throughout the average speed detection area. PRACTICAL APPLICATIONS: The point-to-point camera system is determined to be quite cost-effective.

A perspective review on the role of engine sound in speed perception and control: state of the art and methodological suggestions.

In this review we focus on the role of in-car sound, specifically the artificial engine sounds, on drivers' speed perception and control, a topic that has received little attention so far. Previous studies indicate that removing or reducing engine sound leads drivers to underestimate speed and, consequently, to drive faster. Furthermore, evidence suggests that specific sound frequencies could play a role in this process, highlighting the importance of in-car sound features. First, we show that the amount of research in the field is scarce and rather outdated, and that this is largely due to the fact that industrial research is subject to very few publications. Then, we examine benefits and limitations of different research paradigms used and we propose a protocol to investigate systematically the phenomenon. In particular, we argue for the benefits of a wider use of psychophysical methods in speed perception, a field that has been typically explored by means of driving simulation. Finally, we highlight some methodological and statistical limitations that might impact the interpretation of the evidence considered. Our methodological considerations could be particularly useful for researchers aiming to investigate the impact of sound on speed perception and control, as well as for those involved in the design of in-car sounds. These are particularly relevant for the design of electric vehicles, which represent a challenge but also the ideal testing ground to advance the knowledge in the field.

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.

A CAV-Lead speed advice approach considering local spatiotemporal traffic state near bottlenecks.

Bottlenecks of the freeway generated especially by traffic accidents or temporary work zones contribute to significant reductions in system throughput and hinder the efficient traffic operations. It is imperative to take proactive measures to improve traffic state. With the rapid advancements in intelligent transportation, connected and autonomous vehicles (CAVs) have attracted much attention by its speculated capabilities in improving traffic safety and well-organized operational coordination. Therefore, reasonably utilizing the advantages of CAVs is possible to reduce the impact induced by bottlenecks. In this research, we propose a novel algorithm called CAV-Lead to obtain the CAV's regulated speed under mixed CAVs and human-driven vehicles (HVs) environment to improve the overall utilization of the freeway capacity near bottlenecks. Firstly, we illustrate the basic principle of the CAV-Lead algorithm that takes both microscopic and macroscopic traffic characteristics into account. Then, based on the local spatiotemporal traffic state, the CAV-Lead algorithm is proposed to determine each CAV's speed under mixed flow. Furthermore, a real-time simulation control framework considering the random behavior of HVs is presented. Moreover, several simulation evaluations including comparisons with basic scenarios and similar research are conducted under various CAV market penetration rates (MPRs). The results demonstrate that the CAV-Lead could improve the traffic performance, especially for the high traffic demand with certain MPRs.

Unique Neural Mechanisms Underlying Speed Control of Low-Force Ballistic Contractions.

According to the speed-control hypothesis, the rate of force development (RFD) during ballistic contractions is dictated by force amplitude because time to peak force (TPF) remains constant regardless of changes in force amplitude. However, this hypothesis has not been tested at force levels below 20% of an individual's maximum voluntary contraction (MVC). Here, we examined the relationship between the RFD and force amplitude from 2 to 85% MVC and the underlying structure of muscle activity in 18 young adults. Participants exerted ballistic index finger abductions for 50 trials in each of seven randomly assigned force levels (2, 5, 15, 30, 50, 70, and 85% MVC). We quantified TPF, RFD, and various EMG burst characteristics. Contrary to the speed-control hypothesis, we found that TPF was not constant, but significantly varied from 2 to 85% MVC. Specifically, the RFD slope from 2 to 15% MVC was greater than the RFD slope from 30 to 85% MVC. Longer TPF at low force levels was associated with the variability of EMG burst duration, whereas longer TPF with higher force levels was associated with the EMG burst integral. Contrary to the speed-control hypothesis, we found that the regulation of TPF for low and high force levels was different, suggesting that neuronal variability is critical for force levels below 30% MVC and neuronal amplitude for force levels above 30% MVC. These findings present compelling new evidence highlighting the limitations of the speed-control hypothesis underscoring the need for a new theoretical framework.