Dynamic Control Task Research Articles

Editorial: special issue on marine robotics

The marine environment is an exciting and challenging context for the robotics community. Autonomous underwater and surface vehicles face a demanding environment with poor perception and actuation capabilities with respect to the ground-based robotics. In case of the underwater scenario, for example, communications are complicated by the limited bandwidth, and the position measurement of the vehicles is not obvious. Moreover, the presence of current and wind disturbances enhances the complexity of the dynamic control tasks. With the above difficulties, a successful mission must be supported by themost advanced robot technologies,with particular attention to autonomy. Themarked progress of the last decade in marine technologies and robotics has led to massive improvements ofmarine robotics. The state of the art and some achievements in this area are particularly innovative. The ocean covers about two-thirds of the earth and has a great effect on the future existence of all human beings. In the near future,marine robotswill be used for homeland security, for rise inspection or intelligent sampling, and researchers will be able to deploy numerous networked robotic ocean observatories, expanding the quantity and quality of data

Transfer of short-term motor learning across the lower limbs as a function of task conception and practice order

Interlimb transfer of motor learning, indicating an improvement in performance with one limb following training with the other, often occurs asymmetrically (i.e., from non-dominant to dominant limb or vice versa, but not both). In the present study, we examined whether interlimb transfer of the same motor task could occur asymmetrically and in opposite directions (i.e., from right to left leg vs. left to right leg) depending on individuals’ conception of the task. Two experimental conditions were tested: In a dynamic control condition, the process of learning was facilitated by providing the subjects with a type of information that forced them to focus on dynamic features of a given task (force impulse); and in a spatial control condition, it was done with another type of information that forced them to focus on visuomotor features of the same task (distance). Both conditions employed the same leg extension task. In addition, a fully-crossed transfer paradigm was used in which one group of subjects initially practiced with the right leg and were tested with the left leg for a transfer test, while the other group used the two legs in the opposite order. The results showed that the direction of interlimb transfer varied depending on the condition, such that the right and the left leg benefited from initial training with the opposite leg only in the spatial and the dynamic condition, respectively. Our finding suggests that manipulating the conception of a leg extension task has a substantial influence on the pattern of interlimb transfer in such a way that the direction of transfer can even be opposite depending on whether the task is conceived as a dynamic or spatial control task.

A generic dynamic control task for behavioral research and education

Recent research in behavioral sciences presents strong evidence of poor human understanding for dynamic systems. Computer-based dynamic control tasks have an important potential for helping behavioral scientists advance research that investigates reasons for poor understanding and for helping students understand how dynamic systems work. In this paper, we introduce a simulation called Dynamic Stocks and Flows (DSF) that portrays the basic building blocks of dynamic systems: an accumulation; an inflow and outflow determined by an environment; and an inflow and outflow determined by a decision maker. In DSF, decision makers control the accumulation to a goal level by making repeated inflow and outflow decisions. We provide details of an experiment conducted with DSF that highlight some problems people face in controlling a dynamic system with different kinds of environmental inflow and outflow functions. DSF is flexible enough to represent dynamic systems with continuous or discrete accumulations, and with real-time or event-driven decision-making. We suggest that these and other features in DSF make it a good research and educational tool.

Robot-environment dynamic interaction survey and future trends

Our aim was to consider interaction control problems from different viewpoints, primarily taking into account practical problems and needs. Basic strategies for controlling the interaction of a robot with the environment are the subject of the paper. The paper also provides a historical perspective on interaction control, summarizing the major achievements in this area for the last 25 years. After this long period of investigation we are now faced with an inevitable change of generation in this field. Many young enthusiastic researchers are focusing now on various attractive issues in human-robot-environment interaction control, especially from the viewpoint of novel disciplines such as artificial intelligence, mechatronics, augmented reality, etc. Considering more complex tasks, the application of force sensors and interaction control techniques is certainly not sufficient to provide the robot with a required degree of autonomy and intelligence. The paper attempts to provide unified theoretical force and position control paradigms considering basic control issues: stability, performance, and robustness. This framework assumes a general dynamic environment and uses an inverse dynamic control strategy to design various controllers for specific force and position stabilization tasks. Stability problems during the dynamic control tasks are also considered in the paper using different stability criteria. The established contact stability theory has been expanded to the control and synthesis. Therefore, one of the basic characteristics of regular bipedal walk of humanoid robots is the maintenance of their dynamic balance during the walk, whereby a decisive role is played by the unpowered degrees of freedom arising at the foot-ground contact. Hence, the role of Zero-Moment Point (ZMP) as an indicator of dynamic balance is indispensable. On the other hand, we are witnesses of the diverse realizations of locomotion systems, from those with human-like feet, aiming to mimic in full the human gait, passive walkers, which practically roll on specially profiled feet, to the footless locomotion systems. It is quite clear that any of these systems can realize a gait (very often such gait is not dynamically balanced), but our present study shows that the performances of such walking systems are essentially different and inapt to meet the requirements that are stated for the humanoids in a human environment. This study points out the indispensability of the regular, fully dynamically balanced gait for the simultaneous realization of locomotion-manipulation activities, as well as for the walk in an unstructured environment.

Marine Robotic Systems [From the Guest Editors

The marine environment is an exciting and challenging context for the robotics community. Autonomous surface vehicles (ASVs) or autonomous underwater vehicles (AUVs) face a demanding environment with poor perception and actuation capabilities in comparison with most ground-based robotics. In the case of the underwater scenario, for example, communications are complicated by the limited bandwidth, and the position measurement of the vehicles is not obvious. Both underwater and surface vehicles, moreover, suffer from the presence of current and wind disturbances that enhance the complexity of the dynamic control task.

Editorial: Cognitive Architectures, Model Comparison and AGI

Cognitive Science and Artificial Intelligence share compatible goals of understanding and possibly generating broadly intelligent behavior. In order to determine if progress is made, it is essential to be able to evaluate the behavior of complex computational models, especially those built on general cognitive architectures, and compare it to benchmarks of intelligent behavior such as human performance. Significant methodological challenges arise, however, when trying to extend approaches used to compare model and human performance from tightly controlled laboratory tasks to complex tasks involving more open-ended behavior. This paper describes a model comparison challenge built around a dynamic control task, the Dynamic Stocks and Flows. We present and discuss distinct approaches to evaluating performance and comparing models. Lessons drawn from this challenge are discussed in light of the challenge of using cognitive architectures to achieve Artificial General Intelligence.

Controlling uncertainty: A review of human behavior in complex dynamic environments.

Complex dynamic control (CDC) tasks are a type of problem-solving environment used for examining many cognitive activities (e.g., attention, control, decision making, hypothesis testing, implicit learning, memory, monitoring, planning, and problem solving). Because of their popularity, there have been many findings from diverse domains of research (economics, engineering, ergonomics, human-computer interaction, management, psychology), but they remain largely disconnected from each other. The objective of this article is to review theoretical developments and empirical work on CDC tasks, and to introduce a novel framework (monitoring and control framework) as a tool for integrating theory and findings. The main thesis of the monitoring and control framework is that CDC tasks are characteristically uncertain environments, and subjective judgments of uncertainty guide the way in which monitoring and control behaviors attempt to reduce it. The article concludes by discussing new insights into continuing debates and future directions for research on CDC tasks.

Patients with Parkinson's disease learn to control complex systems via procedural as well as non-procedural learning

The striatum is considered to mediate some forms of procedural learning. Complex dynamic control (CDC) tasks involve an individual having to make a series of sequential decisions to achieve a specific outcome (e.g. learning to operate and control a car), and they involve procedural learning. The aim of this study was to test the hypothesis that patients with Parkinson's disease who have striatal dysfunction, are impaired on CDC tasks only when learning involves procedural learning. 26 patients with Parkinson's disease (PD) and 26 age-matched controls performed two CDC tasks, one in which training was observation-based (non-procedural), and a second in which training was action-based (procedural). Both groups were able to control the system to a specific criterion equally well, regardless of the training condition. However, when reporting their knowledge of the underlying structure of the system, both groups showed poorer accuracy when learning took place through observation-based compared with action-based training. Moreover, the controls’ accuracy in reporting the underlying structure of the systems was superior to that of PD patients. The findings suggest that the striatal dysfunction in Parkinson's disease is not associated with impairment of procedural learning, regardless of whether the task involved procedural learning or not. It is possible that the learning and performance on CDC tasks are mediated by perceptual priming mechanisms in the neocortex.

Type 2 Diabetes Self-Management: Controlling a Dynamic System

People with type 2 diabetes risk disability and early death when they fail to control their blood glucose levels. Despite advances in medicine, pharmacology, human factors, and education, dangerous glucose levels remain endemic. To investigate cognitive barriers to control, we observed American Diabetes Association (ADA) certified training programs; reviewed ADA and National Institute of Health diabetes Web sites; and interviewed patients with type 2 diabetes using a critical decision method. A consistent picture emerged. The prevailing rules and procedures approaches are not preparing patients for the dynamic control task they face. Patients are often unable to understand and use the rules and procedures provided. They are unprepared to detect problems, make sense of dynamic relationships, and manage complex situations. Our results suggest that glucose self-regulation is better conceptualized as a dynamic control challenge requiring complex processes, including problem detection, sensemaking, decision making, and planning/replanning. The mismatch between most patient training and the dynamic demands of glucose regulation helps explain limitations in existing training and poor patient outcomes. We argue that constructs gleaned from naturalistic decision-making research in other complex domains can help many but not all patients develop the cognition necessary for effective blood glucose self-management.

Modeling of a Four-Quadrant Switched Reluctance Motor Drive on EMTDC/PSCAD

This paper introduces a complete package for SRM drive on Power System Computer-Aided Design / Electromagnetic Transients (PSCAD/EMTDC). A three-phase SRM drive is modeled and simulated on PSCAD. The motor is modeled using an accurate nonlinear analytical model that takes into consideration the machine nonlinearities. A current control algorithm is applied for torque ripple minimization to achieve a smooth output torque which is necessary for high performance applications. The motor drive is tested for four-quadrant operations. The modeled SRM is capable of operating as a motor or generator during clockwise and counterclockwise motions. The proposed package helps in understanding the operational principles of switched reluctance motors, investigating the dynamic characteristics of SRM drives, and achieving a high performance dynamic control task.

Facilitative interactions of model- and experiencebased processes: Implications for type and flexibility of representation

People are often taught using a combination of instruction and practice. In prior research, we have distinguished between model-based knowledge (i.e., acquired from explicit instruction) and experience-based knowledge (i.e., acquired from practice), and have argued that the issue of how these types of knowledge (and associated learning processes) interact has been largely neglected. Two experiments explore this issue using a dynamic control task. Results demonstrate the utility of providing model-based knowledge before practice with the task, but more importantly, suggest how this information improves learning. Results also show that learning in this manner can lead to "costs" such as slowed retrieval, and that this knowledge may not always transfer to new task situations as well as experientially acquired knowledge. Our findings also question the assumption that participants always acquire a highly specific "lookup" table representation while learning this task. We provide an alternate view and discuss the implications for theories of learning.

Corticospinal control of the thumb–index grip depends on precision of force control: a transcranial magnetic stimulation and functional magnetic resonance imagery study in humans

The corticospinal system (CS) is well known to be of major importance for controlling the thumb-index grip, in particular for force grading. However, for a given force level, the way in which the involvement of this system could vary with increasing demands on precise force control is not well-known. Using transcranial magnetic stimulation and functional magnetic resonance imagery, the present experiments investigated whether increasing the precision demands while keeping the averaged force level similar during an isometric dynamic low-force control task, involving the thumb-index grip, does affect the corticospinal excitability to the thumb-index muscles and the activation of the motor cortices, primary and non-primary (supplementary motor area, dorsal and ventral premotor and in the contralateral area), at the origin of the CS. With transcranial magnetic stimulation, we showed that, when precision demands increased, the CS excitability increased to either the first dorsal interosseus or the opponens pollicis, and never to both, for similar ongoing electromyographic activation patterns of these muscles. With functional magnetic resonance imagery, we demonstrated that, for the same averaged force level, the amplitude of blood oxygen level-dependent signal increased in relation to the precision demands in the hand area of the contralateral primary motor cortex in the contralateral supplementary motor area, ventral and dorsal premotor area. Together these results show that, during the course of force generation, the CS integrates online top-down information to precisely fit the motor output to the task's constraints and that its multiple cortical origins are involved in this process, with the ventral premotor area appearing to have a special role.

USING COGNITIVE MODELING FOR THE EVALUATION OF HUMAN-MACHINE INTERACTION

Cognitive architectures enable the modeling of human performance at a detailed level. Data at this level is seldom used to analyze simulated performance to evaluate human-machine systems. To remedy the main obstacles we developed SimTrA (Simulation Trace Analyzer), a method and tool to process simulated and human performance data and to compare the outcomes in an effective and efficient manner. We show the applicability and benefits of SimTrA by presenting the results of a study. We successfully used simulated and human eye movement data to discriminate between two cognitive models and human performance data in a dynamic control task.

Towards a Sensitive Measure of Situation Awareness in Adaptively Automated Systems

The objective of this research was to assess the effectiveness of the Situation Awareness Global Assessment Technique (SAGAT) as an indicator of automation state changes in adaptive automation (AA) of a complex, dynamic control task. An air traffic control (ATC)-related simulation was developed to present automation of four different information processing (IP) functions, including information acquisition, information analysis, decision making, and action implementation, as well as to simulate a completely manual control condition. Eight participants operated the ATC simulation under the five conditions. SAGAT data revealed only a general effect of automated versus manual control, but no significant effects of the modes of AA on SA. These results suggested that SAGAT was not a sensitive measure in the ATC-related task. Consequently, a modified SAGAT measure is proposed with relevance weighting of environmental stimuli to promote sensitivity and reliability of measurement of SA in the target domain.

The effects of moderate fatigue on dynamic balance control and attentional demands

BackgroundDuring daily activities, the active control of balance often is a task per se (for example, when standing in a moving bus). Other constraints like fatigue can add to the complexity of this balance task. In the present experiment, we examined how moderate fatigue induced by fast walking on a treadmill challenged dynamic balance control. We also examined if the attentional demands for performing the balance task varied with fatigue.MethodsSubjects (n = 10) performed simultaneously a dynamic balance control task and a probe reaction time task (RT) (serving as an indicator of attentional demands) before and after three periods of moderate fatigue (fast walking on a treadmill). For the balance control task, the real-time displacement of the centre of pressure (CP) was provided on a monitor placed in front of the subject, at eye level. Subjects were asked to keep their CP within a target (moving box) moving upward and downward on the monitor. The tracking performance was measured (time spent outside the moving box) and the CP behavior analyzed (mean CP speed and mean frequency of the CP velocity).ResultsModerate fatigue led to an immediate decrement of the performance on the balance control task; increase of the percentage of time spent outside the box and increase of the mean CP speed. Across the three fatigue periods, subjects improved their tracking performance and reduced their mean CP speed. This was achieved by increasing their frequency of actions; mean frequency of the CP velocity were higher for the fatigue periods than for the no fatigue periods. Fatigue also induced an increase in the attentional demands suggesting that more cognitive resources had to be allocated to the balance task with than without fatigue.ConclusionFatigue induced by fast walking had an initial negative impact on the control of balance. Nonetheless, subjects were able to compensate the effect of the moderate fatigue by increasing the frequency of actions. This adaptation, however, required that a greater proportion of the cognitive resources be allocated to the active control of the balance task.

Chronic Ankle Instability and Fatigue Create Proximal Joint Alterations during Performance of the Star Excursion Balance Test

The combined effects of chronic ankle instability (CAI) and lower extremity fatigue on measures of neuromuscular control have not been well established. The purpose of this study was to investigate the influence of CAI on the performance of a dynamic postural control task, the Star Excursion Balance Test (SEBT), after fatiguing activities. Thirty subjects with (n = 14) or without (n = 16) unilateral CAI completed anterior, medial, and posterior reaching directions of the SEBT performed before and after a lunging fatigue protocol and an open chain ankle isokinetic fatigue protocol. Pre-post fatigue change scores were calculated for sagittal plane kinematics of the stance leg and the normalized reach distances (%MAXD). Using a regression model, group and kinematic data were used to explain between subject differences in %MAXD. For each reaching direction, a separate analysis was completed for the two fatigue conditions. When reaching anteriorly after the lunge fatigue condition, CAI and the changes in knee and hip flexion predicted approximately 49 % of the variance in %MAXD (R2 = .487; p = .001). When reaching medially under lunge fatigue, CAI predicted approximately 20 % of the variance in %MAXD (R2 = .198; p = .014). Isolated ankle fatigue did not cause significantly different responses between groups. For two of the reaching directions, CAI status significantly influenced the variances in %MAXD under the influence of lunge fatigue. Functional fatigue protocols

Situation awareness implications of adaptive automation for information processing in an air traffic control-related task

The objective of this research was to assess the effectiveness of adaptive automation (AA) for supporting information processing (IP) in a complex, dynamic control task by defining a measure of situation awareness (SA) sensitive to differences in the forms of automation. The task was an air traffic control (ATC)-related simulation and was developed to present four different modes of automation of IP functions, including information acquisition, information analysis, decision making and action implementation automation, as well as a completely manual control mode. A total of 16 participants were recruited for a pilot study and primary experiment. The pilot assessed the sensitivity and reliability of the Situation Awareness Global Assessment Technique (SAGAT) for describing AA support of the IP functions. Half of the participants were used in the primary experiment, which refined the SA measure and described the implications of AA for IP on SA using the ATC-like simulation. Participants were exposed to all forms of automation and manual control. AA conditions matched operator workload states to dynamic control allocations in the primary task. The pilot did not reveal significant differences in SA among the various AA conditions. In the primary experiment, participant recall of aircraft was cued and relevance weights were assigned to aircraft at the time of simulation freezes. The modified measure of SA revealed operator perception and Total SA to improve when automation was applied to the information acquisition function. In both experiments, performance in the ATC-related task simulation was significantly superior when automation was applied to information acquisition and action implementation (sensory and motor processing), as compared to automation of cognitive functions, specifically information analysis. The primary experiment revealed information analysis and decision-making automation to cause higher workload, attributable to visual demands of displays. Industry relevance The results of this research may serve as a general guide for the design of adaptive automation functionality in the aviation industry, particularly for information processing support in air traffic control tasks.

Examining the Effects of Combined Stressors on Dynamic Task Performance

In this study, I examined the effects of sleep loss, time pressure, and workload on performance in a dynamic air traffic control (ATC) task. Participants performed the ATC task following 8, 16, and 24 hr of sleep loss under conditions of low and high time pressure and workload. In general, the findings revealed that ATC performance was susceptible to the effects of time pressure and workload. However, there was only partial support for the assumption that the effects of sleep loss on performance are dependent on the level of cognitive challenge posed by a task. Furthermore, I observed a dissociation between ATC performance and subjective assessments of workload (i.e., the National Aeronautics and Space Administration Task Load Index; Hart & Staveland, 1988), suggesting that the cognitive processes responsible for performance are not identical to those processes responsible for subjective assessments of workload.

The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task

This paper extends previous research on two approaches to human-centred automation: (1) intermediate levels of automation (LOAs) for maintaining operator involvement in complex systems control and facilitating situation awareness; and (2) adaptive automation (AA) for managing operator workload through dynamic control allocations between the human and machine over time. Some empirical research has been conducted to examine LOA and AA independently, with the objective of detailing a theory of human-centred automation. Unfortunately, no previous work has studied the interaction of these two approaches, nor has any research attempted to systematically determine which LOAs should be used in adaptive systems and how certain types of dynamic function allocations should be scheduled over time. The present research briefly reviews the theory of human-centred automation and LOA and AA approaches. Building on this background, an initial study was presented that attempts to address the conjuncture of these two approaches to human-centred automation. An experiment was conducted in which a dual-task scenario was used to assess the performance, SA and workload effects of low, intermediate and high LOAs, which were dynamically allocated (as part of an AA strategy) during manual system control for various cycle times comprising 20, 40 and 60% of task time. The LOA and automation allocation cycle time (AACT) combinations were compared to completely manual control and fully automated control of a dynamic control task performed in conjunction with an embedded secondary monitoring task. Results revealed LOA to be the driving factor in determining primary task performance and SA. Low-level automation produced superior performance and intermediate LOAs facilitated higher SA, but this was not associated with improved performance or reduced workload. The AACT was the driving factor in perceptions of primary task workload and secondary task performance. When a greater percentage of primary task time was automated, operator perceptual resources were freed-up and monitoring performance on the secondary task improved. Longer automation cycle times than have previously been studied may have benefits for overall human–machine system performance. The combined effect of LOA and AA on all measures did not appear to be ‘additive’ in nature. That is, the LOA producing the best performance (low level automation) did not do so at the AACT, which produced superior performance (maximum cycle time). In general, the results are supportive of intermediate LOAs and AA as approaches to human-centred automation, but each appears to provide different benefits to human–machine system performance. This work provides additional information for a developing theory of human-centred automation.

The effects of feedback format on dynamic decision making

The tendency for people to perform poorly on dynamic decision-making tasks has been attributed to their inability to form adequate mental models of dynamic systems ( Sterman, 1994). An alternative explanation is that the tabular feedback formats used in previous studies do not facilitate mental representations of the dynamic task and lead to processing overload. Drawing on research in the human factors area, this study examined the impact of graphical versus tabular feedback formats on performance, response times, and learning on a dynamic inventory control task under differing levels of complexity. The graphical feedback group performed better but the tabular group showed stronger evidence of learning. The two groups did not differ in response times. Records of decision rationales showed that participants were aware of complexity factors but were unable to cope with them effectively under either feedback format. The proximity compatibility principle ( Wickens & Carswell, 1995) from human factors, which argues that feedback must be matched to the processing requirements of the task, provides a useful theoretical framework for the design and testing of feedback formats for organizational tasks and criteria.