Speed Instructions Research Articles

Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

Autonomous Underwater Vehicles (AUVs) are widely used for the inspection of seabed pipelines. To address the issues of low trajectory tracking accuracy in AUV inspection processes due to uncertain ocean current disturbances, this paper designs a new dual-loop controller based on Model Predictive Control (MPC) and Variable Universe S-plane algorithms (S-VUD FLC, where VUD represents Variable Universe Discourse and FLC represents Fuzzy Logic Control) to achieve three-dimensional (3-D) trajectory tracking of an over-actuated AUV under uncertain ocean current disturbances. This paper uses MPC as the outer-loop position controller and S-VUD FLC as the inner-loop speed controller. The outer-loop controller generates desired speed instructions that are passed to the inner-loop speed controller, while the inner-loop speed controller generates control input and uses a direct logic thrust distribution method that approaches optimal energy consumption to distribute the thrust generated by the propellers to the over-actuated AUV, achieving closed-loop tracking of the entire trajectory. When designing the outer-loop MPC controller, the actual control input constraints of the system are considered, and control increments are introduced to reduce control model errors and the impact of uncertain external disturbances on the actual AUV model parameters. When designing the inner-loop S-VUD FLC, the strong robustness of the variable universe fuzzy controller and the easy construction characteristics of the S-plane algorithm are combined, and integral action is introduced to improve the system’s tracking accuracy. The stability of the outer loop controller is proven by the Lyapunov method, and the stability of the inner loop controller is verified by simulation. Finally, simulations show that the over-actuated AUV has fast tracking processes and high tracking result accuracy under uncertain ocean current disturbances, demonstrating the effectiveness of the designed dual-loop controller.

Developed and Intelligent Structure of a Control for PV Water Treatment System

The subject of this work is a UV-irradiated water disinfection prototype intended for use in rural areas where access to water is difficult. Given the favorable climatic conditions of our country, the use of photovoltaic panels as a source of energy is particularly interesting, and has relevance in regions with a similar climate. PV energy being a fluctuating source that influences water disinfection operations, we have developed a database to distribute the energy available to the loads (UV lamps, electric pumps) in order to ensure a better quality of the water. This database is used in deep learning to model water disinfection phenomena. This method is able to adjust the speed instructions of the motor pump (therefore the flow rate) and the UV irradiation according to the energy available to ensure optimal water quality. Several other techniques have been implemented to control the instructions generated by the deep learning developed, to control the motor, the inverter and the DC/DC converter (IRFOC, SVPWM, sliding mode). All these approaches are tested in real time and they represent good results in terms of water treatment control. The effectiveness of these types of control is proven by the results obtained.

Linear and nonlinear Model Predictive Control (MPC) for regulating pedestrian flows with discrete speed instructions

Linear and nonlinear Model Predictive Control (MPC) for regulating pedestrian flows with discrete speed instructions

Multiple-Input-Single-Output prediction models of crowd dynamics for Model Predictive Control (MPC) of crowd evacuations

Predicting crowd dynamics in real-time may allow the design of adaptive pedestrian flow control mechanisms that prioritize attendees’ safety and overall experience. Single-Input-Single-Output (SISO) AutoRegresive eXogenous (ARX) prediction models of crowd dynamics have been effectively used in Linear Model Predictive Controllers (MPC) that adaptively regulate the movement of people to avoid overcrowding. However, an open research question is whether Multiple-Input, State-space, and Nonlinear modeling approaches may improve MPC control performance through better prediction capabilities. This paper considers a simulated controlled evacuation scenario, where evacuees in a long corridor dynamically receive speed instructions to modulate congestion at the exits. We aim to investigate Multiple-Input-Single-Output (MISO) prediction models such that the inputs are the control action (speed recommendation) and pedestrian flow measurement, and the output is the local density of the pedestrian outflow. State-space and Input–output MISO models, linear and neural, are identified using a data-driven approach in which input–output datasets are generated from strategically designed microscopic evacuation simulations. Different estimation algorithms, including the subspace method, prediction error minimization, and regularized AutoRegressive eXogenous (ARX) model reduction, are evaluated and compared. Finally, to investigate the importance of measuring and modeling the pedestrian inflow, the case in which the models’ structure is defined as a Single-Input-Single-Output (SISO) system has been explored, where the pedestrian inflow is considered an unmeasured input disturbance. This study has important implications for the design of more effective MPC controllers for regulating pedestrian flows. We found that the prediction error minimization algorithm performs best and that nonlinear state-space modeling does not improve prediction performance. The study suggests that modeling the inner state of the evacuation process through a state-space model positively influences predicting system dynamics. Also, modeling pedestrian inflow improves prediction performance from a predefined prediction horizon value. Overall, linear state-space models have been deemed the most suitable option in corridor-type scenarios.

ROS-Based Autonomous Navigation Robot Platform with Stepping Motor

Indoor navigation robots, which have been developed using a robot operating system, typically use a direct current motor as a motion actuator. Their control algorithm is generally complex and requires the cooperation of sensors such as wheel encoders to correct errors. For this study, an autonomous navigation robot platform named Owlbot was designed, which is equipped with a stepping motor as a mobile actuator. In addition, a stepping motor control algorithm was developed using polynomial equations, which can effectively convert speed instructions to generate control signals for accurately operating the motor. Using 2D LiDAR and an inertial measurement unit as the primary sensors, simultaneous localization, mapping, and autonomous navigation are realised based on the particle filtering mapping algorithm. The experimental results show that Owlbot can effectively map the unknown environment and realise autonomous navigation through the proposed control algorithm, with a maximum movement error being smaller than 0.015 m.

Combined effect of eating speed instructions and food texture modification on eating rate, appetite and later food intake

Modifying food texture and eating slowly each reduce appetite and energy intake. No study has evaluated the effect of combining these measures to slow eating speed and determine the effect on appetite. The aim of this study was to investigate whether there is a combined effect of manipulating oral processing behaviours (OPBs) in this manner on self-reported satiety and subsequent food intake. A 2 × 2 design was used with four breakfast conditions in total. Twenty-four participants attended four study visits where they were asked to consume one of two isocaloric fixed-portion breakfasts differing in texture: 1) granola with milk and 2) yogurt with muesli and conserve. Participants consumed each breakfast twice, with verbal instructions to chew slowly at one visit and at a normal rate at another. Consumption was video-recorded to behaviourally code OPBs. Participants completed visual analogue scales of self-reported appetite measures at the beginning of the test session, immediately prior to and immediately after breakfast consumption. They also completed a food diary documenting food intake for the remainder of the day. The breakfast designed to be eaten slowest (the harder-textured meal with instructions to eat slowly) was eaten at a slower rate, with a greater number of chews per bite and a slower bite rate (p < 0.001) compared to the other meals. No differences were observed between the breakfast conditions on subjective measures of post-prandial satiety, or subsequent energy or macronutrient consumption. Results of this study highlight that combined effects of texture and instructions are most effective at reducing eating rate, though eating slower was not shown to enhance post-meal satiety. Reduced eating speed has previously been shown to reduce ad-libitum energy intake. Future research should consider combined approaches to reduce eating speed, to mitigate the risk of overconsumption within meals.

Larger error negativity peak amplitudes for accuracy versus speed instructions may reflect more neuro-cognitive alignment, not more intense error processing

Understanding human error processing is a highly relevant interdisciplinary goal. More than 30 years of research in this field have established the error negativity (Ne) as a fundamental electrophysiological marker of various types of erroneous decisions (e.g. perceptual, economic) and related clinically relevant variations. A common finding is that the Ne is more pronounced when participants are instructed to focus on response accuracy rather than response speed, an observation that has been interpreted as reflecting more thorough error processing. We challenge this wide-spread interpretation by demonstrating that when controlling for the level of non-event-related noise in the participant-average waveform and for single-trial peak latency variability, the significant speed-accuracy difference in the participant-average waveform vanishes. This suggests that the previously reported Ne differences may be mostly attributable to a more precise alignment of neuro-cognitive processes and not (only) to more intense error processing under accuracy instructions, opening up novel perspectives on previous findings.

Speed and accuracy instructions affect two aspects of skill learning differently

Procedural learning is key to optimal skill learning and is essential for functioning in everyday life. The findings of previous studies are contradictory regarding whether procedural learning can be modified by prioritizing speed or accuracy during learning. The conflicting results may be due to the fact that procedural learning is a multifaceted cognitive function. The purpose of our study is to determine whether and how speed and accuracy instructions affect two aspects of procedural learning: the learning of probability-based and serial-order-based regularities. Two groups of healthy individuals were instructed to practice on a cued probabilistic sequence learning task: one group focused on being fast and the other on being accurate during the learning phase. The speed instruction resulted in enhanced expression of probability-based but not serial-order-based knowledge. After a retention period, we instructed the participants to focus on speed and accuracy equally, and we tested their acquired knowledge. The acquired knowledge was comparable between groups in both types of learning. These findings suggest that different aspects of procedural learning can be affected differently by instructions. However, only momentary performance might be boosted by speed instruction; the acquired knowledge remains intact. In addition, as the accuracy instruction resulted in accuracy near ceiling level, the results illustrate that response errors are not needed for humans to learn in the procedural domain and draw attention to the fact that different instructions can separate competence from performance.

Speed feedback observation algorithm based on parameter introduction and its application in asynchronous motor

In this article, three-phase asynchronous motor and photoelectric encoder are taken as the research objects. In order to solve the problem of delay in the velocity feedback measurement result of asynchronous motor by photoelectric encoder, this article proposes a velocity feedback observation algorithm which introduces parameters according to different velocity instructions. The proposed method ensures that the speed feedback of asynchronous motor can keep good tracking effect under different speed instructions, reduces feedback delay, and the oscillation of speed tracking waveform is small. Stability analysis is also carried out in this article to ensure the reliability of the system. In the whole control system, the position signal output by the photoelectric encoder is the input of the algorithm, and the output of the algorithm is the speed feedback of the system. The proposed method establishes the speed control system model of three-phase asynchronous motor through MATLAB/Simulink for simulation, and the experiment is carried out based on the simulation. The simulation and experiment are compared with the traditional differential velocity measurement method of encoder. The results show that the velocity observation algorithm proposed is verified under different velocity instructions and varying loads, and the system has smaller steady-state error, better dynamic response, and good robustness.

Impact of ATC speed instructions on fuel consumption and noise exposure: an assessment of real operations in Zurich

Approach operations at busy airports are louder and less fuel efficient than they technically could be. Carrying out a safe approach under fluctuating wind and weather conditions while following individual air traffic control (ATC) instructions imposes a significant workload on the flight crew, especially with the limited systems support and information availability on the flight deck today. The SESAR exploratory research project DYNCAT (Dynamic Configuration Adjustment in the TMA) aims to highlight the impact of current approach operations on the environment based on all relevant data sources (on-board operational data, ATC commands, noise measurement data, surrounding traffic and weather information) allowed to evaluate individual approach operations in their full context, exemplarily for the Airbus A320 at Zurich airport. The evaluations performed are a unique opportunity to analyse the impact of ATC instructions on fuel consumption and noise exposure during real operations. The results showed that speed instructions, especially when given early during the transition phase, lead to high fuel consumption, due to the long-time flight in low speed levels correlated to earlier usage of Flaps configurations. A mean difference of almost 50 kg in fuel burned is observed when comparing the flights with speed restrictions to those without. On the other hand, speed restrictions provide a well-defined airspeed guidance for the pilots during transition and final approach, which leads to lower usage of speed brakes and also to lower speed levels for the landing gear extension. The less speed restrictions were given to the pilots by ATC, the higher was the usage of speed brakes during the final approach, and also the landing gear deployment was performed at higher speed levels, which ceteris paribus contributes significantly to the noise exposure and noise footprint. Based on these evaluations, requirements for a novel flight management system (FMS) function, which is set to be developed in the next steps of the project, were derived to support pilots and controllers through extended information exchange, thus increasing predictability of the lateral and vertical flight profiles for both sides. A central component is a novel airborne energy management assistance system including a configuration management functionality, to be implemented through an extension of the FMS capabilities.

Pushing to the Limits: What Processes during Cognitive Control are Enhanced by Reaction-Time Feedback?

To respond as quickly as possible in a given task is a widely used instruction in cognitive neuroscience; however, the neural processes modulated by this common experimental procedure remain largely elusive. We investigated the underlying neurophysiological processes combining electroencephalography (EEG) signal decomposition (residue iteration decomposition, RIDE) and source localization. We show that trial-based response speed instructions enhance behavioral performance in conflicting trials, but slightly impair performance in nonconflicting trials. The modulation seen in conflicting trials was found at several coding levels in EEG data using RIDE. In the S-cluster N2 time window, this modulation was associated with modulated activation in the posterior cingulate cortex and the superior frontal gyrus. Furthermore, in the C-cluster P3 time window, this modulation was associated with modulated activation in the middle frontal gyrus. Interestingly, in the R-cluster P3 time window, this modulation was strongest according to statistical effect sizes, associated with modulated activity in the primary motor cortex. Reaction–time feedback mainly modulates response motor execution processes, whereas attentional and response selection processes are less affected. The study underlines the importance of being aware of how experimental instructions influence the behavior and neurophysiological processes.

The Effects of Time Pressure on Temporal Overestimation Due to Threat

Abstract How does emotion change the way we perceive time? Studies have shown that we overestimate the duration of faces that express anger of fear – an effect that has been explained as due the speeding of a pacemaker that resides within an internal clock. Here, we test the idea that attending longer to facial threat leads to an overestimation of time. Seventy participants (16 male) estimated the duration of angry, fearful and neutral expressions under conditions designed to either reduce attention to time (by emphasising speedy responses) or lengthen attention to time (by emphasising accuracy). Results were modelled using Bayesian Multilevel Logistic Regression. The results replicate previous findings: speed emphasis reduced temporal sensitivity and led to both a higher overall proportion of long responses and faster reaction times. Facial threat attenuated the drop in temporal sensitivity due to speed instructions supporting the idea that people prolong attention to threat (even when they are not directly instructed to do so). We relate the findings to research into attention bias to threat and more broadly to models of perceptual decision making.

The effect of different test protocols and walking distances on gait speed in older persons

Background and aimsWalking is the core physical activity of older persons. The assessment of walking capacity is increasingly important for clinical purposes and clinical research. Differences between assessment tools and protocols for short walks to obtain gait characteristics can be responsible for changes, e.g., in gait speed from 0.1 to 0.2 m/s. The purpose of this study was to generate further knowledge for the harmonization and/or standardization of short walk-test protocols for assessing gait characteristics under supervised conditions.MethodsFor this cross-sectional study, 150 community-dwelling older adults (mean age 80.5 ± 4.5 years) were recruited. Participants performed eight walks differing in the distance (8-versus 4-m), static versus dynamic trials and comparing different test speed instructions (usual versus maximal) on an electronic walkway.ResultsA meaningful significant difference in mean usual gait speed was documented comparing the 4-m dynamic and static test protocol (0.12 m/s; p = 0.001). For the same comparison over an 8-m distance (dynamic versus static) and for the comparison between usual gait speed over 4-and 8-m, the differences in gait speed were smaller, but still statistically significant (p = 0.001).ConclusionsGait speed was faster, if the test protocol did not include a static start or stop. The differences were greater for a shorter walking distance. This aspect should be considered for the comparison of study results and is particularly relevant for systematic reviews and meta-analyses.

Model predictive control of autonomous underwater vehicles for trajectory tracking with external disturbances

This paper designs a novel double closed-loop three-dimensional (3-D) trajectory tracking method based on model predictive control (MPC) for an autonomous underwater vehicle (AUV) under external disturbances. Different from the conventional model predictive control, the designed double closed-loop control can be divided into two stages: 1) the outer-loop controller generates the desired speed instruction and passes it to the inner-loop speed controller; 2) the inner-loop speed controller generates the available control inputs to ensure the whole closed-loop trajectory tracking. In the controller design stage, the actual constraints on system inputs and state are effectively considered. In order to ensure the smooth operation of AUV, the double closed-loop controller takes the control increments as the control inputs. In addition, the stability analysis based on Lyapunov method proves the nominal stability of the controller. Finally, simulation experiments are designed to verify the tracking performance of the AUV under external disturbances. The results show the effectiveness of the controller.

Speed or Accuracy Instructions During Skill Learning do not Affect the Acquired Knowledge.

A crucial question in skill learning research is how instruction affects the performance or the underlying representations. Little is known about the effects of instructions on one critical aspect of skill learning, namely, picking-up statistical regularities. More specifically, the present study tests how prelearning speed or accuracy instructions affect the acquisition of non-adjacent second-order dependencies. We trained 2 groups of participants on an implicit probabilistic sequence learning task: one group focused on being fast and the other on being accurate. As expected, we detected a strong instruction effect: accuracy instruction resulted in a nearly errorless performance, and speed instruction caused short reaction times (RTs). Despite the differences in the average RTs and accuracy scores, we found a similar level of statistical learning performance in the training phase. After the training phase, we tested the 2 groups under the same instruction (focusing on both speed and accuracy), and they showed comparable performance, suggesting a similar level of underlying statistical representations. Our findings support that skill learning can result in robust representations, and they highlight that this form of knowledge may appear with almost errorless performance. Moreover, multiple sessions with different instructions enabled the separation of competence from performance.

Vergence Eye Movements: From Basic Science to Clinical Application - Foreword to the Special Issue.

The abstract book of the last European Conference on Eye Movements [1] lists abstracts of 373 presentations, but less than five percent investigate vergence eye movements, i.e. the coordination of the right and left eye. Why then a special issue on this neglected issue? Human vision under natural conditions involves both eyes in coordination controlled by interacting processes subsumed under the concept of vergence.. Further, vergence is important for people in their daily lives since disorders of vergence can have serious consequences: ophthalmologists deal with squinting patients on the basis of heterophoria and heterotropia testing, eye strain or visual complaints can be related to impaired vergence dynamic or less accurate static vergence, remediation by optometrist includes vergence training or prism eye glasses, etc.

A dynamic merge assistance method based on the concept of instantaneous virtual trajectory for vehicle-to-infrastructure connected vehicles

Lane-changing activities near freeway merging, diverging, and weaving sections are one of the major factors leading to recurrent bottleneck congestion. In recent years, microscopic dynamic merging assistance (DMA) methods are found efficient to improve mobility and safety in merging areas. The article proposes a solution, the connected vehicle (CV) vehicle-to-infrastructure (V2I)-based dynamic merge assistance (DMA) method, analyzing vehicle trajectory data collected at CV roadside units (RSU) and implements a “vehicle-gap” pairing process for vehicle-gap synchronized control. The vehicle-gap pairing is determined by dynamically predicting their merging potential per the relationship between the “instantaneous virtual trajectories” (IVTs) of mainline and onramp vehicles. The IVTs are generated according to the prevailing speed profiles on both ramp and mainline through lane. Other than those automated control-based methods, this article focuses on providing a realistic speed instruction to human drivers through CV communication. A Cooperative Adaptive Cruise Control model is adopted to calculate such speed instruction. The paired onramp/mainline vehicles who follow the speed instruction can form a putative platoon so that their speed and location can be synchronized all through the merging area to ensure the gap-opening and catching-up. The proposed method is evaluated within a simulation network based on the field data collected on Interstate 35 at Austin, TX. The proposed method is tested in a microscopic traffic simulation environment using VISSIM external driver model Application Programing Interface. The simulation results indicated significant reduced average travel time at merge sections during congestion and increased time-to-collision during merging compared with other existing microscopic merge control models.

The Effect of Target Speed and Verbal Instruction on NPC Measures in a Young, Healthy, and Active Population.

Purpose: Evaluate the effect of target speed and verbal instruction on near point of convergence (NPC) measurements in a young, healthy, and active population. Methods: NPC was measured in 20 individuals with three target speeds and two sets of verbal instruction. The target speeds used were 1 cm/s, 3 cm/s, 5 cm/s, and participant self-paced. The verbal instruction given was either to indicate when the target became “double” or “blurry”. Results: Paired-samples t-tests revealed significant differences between 5 cm/s (5.44 ± 2.01) and 1 cm/s (6.72 ± 2.39, p = .003), 3 cm/s (6.10 ± 2.36, p = .030) and self-paced (6.63 ± 2.26, p = .005). A significant difference (p < .001) was also found between the “double” (6.72 ± 2.39) and “blurry” (10.82 ± 3.08) conditions. Conclusion: For young, healthy and active individuals, target speed and verbal instruction matter when measuring NPC.

Flexibility of error-monitoring in obsessive-compulsive disorder under speed and accuracy instructions.

Hyperactive error monitoring is a robust neurocognitive characteristic in obsessive-compulsive disorder (OCD). Yet, relatively little is known about the flexibility and potential moderators of error monitoring in OCD. The current study investigates error monitoring in 30 healthy participants and 28 patients with OCD using a flanker task in 2 conditions either emphasizing speed or accuracy. Results indicate that no group difference is observable in error-related negativity (ERN) under the accuracy-focused condition. Both groups show an ERN attenuation in the speed condition; however, this reduction is larger in healthy controls leading to pronounced group differences in the speed-focused condition. Similarly, receiver operating characteristic analyses yield an area under the curve (AUC) of .62 in the accuracy-focused condition, whereas under speed instruction an AUC of .85 suggests a marked increase in classification accuracy. Behavioral results and results from a drift-diffusion model of decision making indicate a more cautious response strategy and less adaptability to experimental manipulations in OCD. Overall, results suggest that the observed increases in error-related neural activity in OCD might stem from a reduced flexibility and diminished ability to disengage from an accuracy-oriented, error-avoidant response style. Further, the present results suggest that emphasizing speed in experimental instructions may lead to a better neural differentiation between patients with OCD and healthy controls which has important implications for the use of the ERN as a risk indicator. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The effects of the six thinking hats and speed on creativity in brainstorming

Although De Bono’s ‘six thinking hats’ have been suggested to be beneficial for developing creativity in learners, little is known about how these hats are effective in doing so. This paper reports on two experiments using thinking hats. In the first one, the participants were allocated one of the six colors of ‘thinking hat’s and read an explanation of what focus of frame that hat required. They then were asked, individually, to generate ideas for 12 min in a brainstorming session. Experiment 1 investigated the effectiveness of each hat separately and showed that the participants in the yellow hat outperformed those in the red one in terms of unique or creative ideas in brainstorming. Albeit being not significant, there was a trend for higher performance of participants using the green hat in terms of deep ideas. Experiment 2 was conducted to investigate the characteristics of creativity produced by the green and yellow hat as well that of the red one that produced the lowest performance in Experiment 1. In addition, the effect of speed on idea generation was investigated in Experiment 2. Findings suggested that the green hat enhanced the generation of more unique ideas than the yellow hat and red one. The speed instruction appeared to further enhance the number of unique and flexible ideas of participants with the highest number of unique ideas being generated by participants using the green hat. The implications for the creativity in education are discussed in the light of these new findings.