Static-dynamic model for endpoint control used in Smederevo's BOF shop

This paper describes the computer model for BOF control that was in use at Smederevo, Serbia, during the period 1994-2006. The model was developed at the Institute of Metallurgy of the Smederevo Steelwork in mid-1994 and was motivated by the fact that the plant in Smederevo, by that time, had many years of experience in endpoint control using Intermediate Stop Practice (ISP). The vision for the model was to continuously improve and adapt to the working conditions of production through self-learning and adjustments. The model belongs to the well-known family of Static-Dynamic models (SDMs). It is aimed to reduce the "oxygen off-to-start tap" time and thus increase productivity and reduce production costs. The paper briefly describes the metallurgical software, operator operations and provides some information on the model's effectiveness.

Older adults use a motor plan that is detrimental to endpoint control

Here, we aimed to understand if older adults (OA) use a unique motor plan that is detrimental to endpoint control. We performed two experiments that used ankle ballistic contractions that reversed at the target. In Experiment 1, eight young adults (YA; 27.1 ± 4.2) and eight OA (73.3 ± 4.5) aimed to perform an ankle dorsiflexion–plantarflexion movement that reversed at 9° in 180 ms (target). We found that the coordination pattern (motor plan) differed for the two groups. OA used significantly greater soleus (SOL) activity to reverse the ankle movement than YA and exhibited greater tibialis anterior (TA) muscle activity variability (p < 0.05). OA exhibited worse endpoint control than YA, which associated with the exacerbated TA variability (R2 > 0.2; p < 0.01). Experiment 2 aimed to confirm that the OA motor plan was detrimental to endpoint control. Fifteen YA (20.5 ± 1.4) performed an ankle dorsiflexion–plantarflexion contraction that reversed at 30% MVC in 160 ms by using either a pattern that mimicked OA (High SOL) or YA (Low SOL). With the High SOL coordination pattern, YA exhibited impaired endpoint control and greater TA activation variability. These findings provide strong evidence that OA select a unique motor plan that is detrimental to endpoint control.

A velocity plan with internal feedback control best explains modulation of saccade kinematics during eye-hand coordination

ABSTRACTFast movements like saccadic eye movements that occur in the absence of sensory feedback are often thought to be under internal feedback control. In this framework, a desired input in the form of desired displacement signal is widely believed to be encoded in a spatial map of the superior colliculus (SC). This is then converted into a dynamic velocity signal that drives the oculomotor neurons. However, recent evidence has shown the presence of a dynamic signal within SC neurons, which correlates with saccade velocity. Hence, we used models based on optimal control theory to test whether saccadic execution could be achieved by a velocity based internal feedback controller. We compared the ability of a trajectory control model based on velocity to that of an endpoint control model based on final displacement to capture saccade behavior of modulation of peak saccade velocity by the hand movement, independent of the saccade amplitude. The trajectory control model tracking the desired velocity in optimal feedback control framework predicted this saccade velocity modulation better than an endpoint control model. These results suggest that the saccadic system has the flexibility to incorporate a velocity plan based internal feedback control that is imposed by task context.NEW & NOTEWORTHYWe show that the saccade generation system may use an explicit velocity tracking controller when demand arises. Modulation of peak saccade velocity due to modulation of the velocity of the accompanying hand movement was better captured using a velocity tracking stochastic optimal control model compared to an endpoint model of saccade control. This is the first evidence of trajectory planning and control for the saccadic system based on optimal control theory.

Biological Plausibility of Arm Postures Influences the Controllability of Robotic Arm Teleoperation

Objective We investigated how participants controlling a humanoid robotic arm’s 3D endpoint position by moving their own hand are influenced by the robot’s postures. We hypothesized that control would be facilitated (impeded) by biologically plausible (implausible) postures of the robot. Background Kinematic redundancy, whereby different arm postures achieve the same goal, is such that a robotic arm or prosthesis could theoretically be controlled with less signals than constitutive joints. However, congruency between a robot’s motion and our own is known to interfere with movement production. Hence, we expect the human-likeness of a robotic arm’s postures during endpoint teleoperation to influence controllability. Method Twenty-two able-bodied participants performed a target-reaching task with a robotic arm whose endpoint’s 3D position was controlled by moving their own hand. They completed a two-condition experiment corresponding to the robot displaying either biologically plausible or implausible postures. Results Upon initial practice in the experiment’s first part, endpoint trajectories were faster and shorter when the robot displayed human-like postures. However, these effects did not persist in the second part, where performance with implausible postures appeared to have benefited from initial practice with plausible ones. Conclusion Humanoid robotic arm endpoint control is impaired by biologically implausible joint coordinations during initial familiarization but not afterwards, suggesting that the human-likeness of a robot’s postures is more critical for control in this initial period. Application These findings provide insight for the design of robotic arm teleoperation and prosthesis control schemes, in order to favor better familiarization and control from their users.

A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices

Teleception is defined as sensing that occurs remotely, with no physical contact with the object being sensed. To emulate innate control systems of the human body, a control system for a semi- or fully autonomous assistive device not only requires feedforward models of desired movement, but also the environmental or contextual awareness that could be provided by teleception. Several recent publications present teleception modalities integrated into control systems and provide preliminary results, for example, for performing hand grasp prediction or endpoint control of an arm assistive device; and gait segmentation, forward prediction of desired locomotion mode, and activity-specific control of a prosthetic leg or exoskeleton. Collectively, several different approaches to incorporating teleception have been used, including sensor fusion, geometric segmentation, and machine learning. In this paper, we summarize the recent and ongoing published work in this promising new area of research.