Representing objects in space is difficult because sensorimotor events are anchored in different reference frames, which can be either eye-, arm-, or target-centered. In the brain, Gain-Field (GF) neurons in the parietal cortex are involved in computing the necessary spatial transformations for aligning the tactile, visual and proprioceptive signals. In reaching tasks, these GF neurons exploit a mechanism based on multiplicative interaction for binding simultaneously touched events from the hand with visual and proprioception information.By doing so, they can infer new reference frames to represent dynamically the location of the body parts in the visual space (i.e., the body schema) and nearby targets (i.e., its peripersonal space). In this line, we propose a neural model based on GF neurons for integrating tactile events with arm postures and visual locations for constructing hand- and target-centered receptive fields in the visual space. In robotic experiments using an artificial skin, we show how our neural architecture reproduces the behaviors of parietal neurons (1) for encoding dynamically the body schema of our robotic arm without any visual tags on it and (2) for estimating the relative orientation and distance of targets to it. We demonstrate how tactile information facilitates the integration of visual and proprioceptive signals in order to construct the body space.
Touch perception is an important sense to model in humanoid robots to interact physically and socially with humans. We present a neural controller that can adapt the compliance of the robot arm in four directions using as input the tactile information from an artificial skin and as output the estimated torque for admittance control-loop reference. This adaption is done in a self-organized fashion with a neural system that learns first the topology of the tactile map when we touch it and associates a torque vector to move the arm in the corresponding direction. The artificial skin is based on a large area piezoresistive tactile device (ungridded) that changes its electrical properties in the presence of the contact. Our results show the self-calibration of a robotic arm (2 degrees of freedom) controlled in the four directions and derived combination vectors, by the soft touch on all the tactile surface, even when the torque is not detectable (force applied near the joint). The neural system associates each tactile receptive field with one direction and the correct force. We show that the tactile-motor learning gives better interactive experiments than the admittance control of the robotic arm only. Our method can be used in the future for humanoid adaptive interaction with a human partner.
This paper is concerned with the analysis of losses in induction motors. The most significant have been chose for minimization. These are in particular the losses in the windings and in the magnetic circuit due to eddy currents and hysteresis. Equations for the rotor flux linkage and orthogonal components of the stator current in the rotor reference frame dq in the induction motor’s vector control system based on the condition of minimizing the total losses in copper and motor steel in the steady state. Here, effects of steel saturation are not taken into account. The limit values of the torque and speed are determined, where the rotor flux linkage con-trol can improve the energy characteristics of the drive outside the magnetic saturation. It is shown that the main difficulty in imple-menting energy-optimal control is that the rotor flux linkage operates not only energy parameters, but also speed regulation in the field-weakening region.A block diagram of the implementation of energy-optimal control with field weakening mode is proposed. The idea is to switch the control algorithms of the magnetic field of the motor in such a way that in the start-brake modes the rotor flux linkage changes in the speed reference function, and when operating at a steady speed, in the function of the torque. A co mpara-tive analysis for a typical and developed drive systems in field-weakening mode by the simulation is carried out.It is shown that with the same transients of the torque and speed in a typical system, the efficiency in steady-state decreases with a decrease of torque load torque, whereas the proposed system it remains unchanged. The change in efficiency in dynamic conditions occurs when the rotor flux linkage changes. With energy-optimal control, there is a slight increase in the stator current peaks when the torque load changes abruptly, but at low torque load an additional field-weakening leads to a decrease in the stator voltage, which carry on a decrease in electricity consumption.
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