On the domain decomposition and transmission line modelling finite element method for time-domain induction motor analysis

This paper demonstrates how a finite element model which exploits domain decomposition is applied to the analysis of three-phase induction motors. It is shown that a significant gain in cpu time results when compared with standard finite element analysis. Aspects of the application of the method which are particular to induction motors are considered: the means of improving the convergence of the nonlinear finite element equations; the choice of symmetrical sub-domains; the modelling of relative movement; and the inclusion of periodic boundary conditions. © 1999 IEEE.

A comparison of power losses for small (< 1 kW) induction motor drives adopting squarewave and sinusoidal PWM excitation

In this paper the influence of the form of motor excitation on the performance of a small (< 1 kW) induction motor drive is studied. Two forms of excitation, namely sine waves generated by pulse width modulation and simple square wave are explored. Sine wave excitation gives lower motor losses but increases inverter losses. Conversely, square wave excitation increases motor losses but decreases inverter losses. Losses have been measured directly by calorimetric means or, in the case of the inverter, predicted by a Pspice model that has been verified by calorimetric methods. The work shows that overall, the use of square wave excitation leads to a more efficient drive. © 2004 The Institution of Electrical Engineers.

Duty cycle and modelling considerations in three-phase induction motor drives for washing machines

Three-phase induction motors offer significant advantages over commutator motors in some domestic appliances. Models for wide speed range three-phase induction motors for use in a horizontal axis washing machine have been developed using the MEGA finite element package with an external formulation for calculating iron losses. Motor loss predictions have been verified using a novel high accuracy calorimeter. Good agreement has been observed over a wide speed range at different loadings. The model is used to predict motor temperature rise under typical washing machine loading conditions to ensure its limiting temperature is not exceeded and enables alternative designs to be investigated without recourse to physical prototypes. © 2005 IEEE.

Impact of PWM schemes on induction motor losses

This paper presents the results of an investigation into the impact of pulse width modulation (PWM) switching schemes on power losses in induction motors and their inverter drives. The PWM schemes considered include sinusoidal PWM, spacevector PWM and discontinuous PWM. Both experimental results and simulated predictions are presented for fractional horsepower and small integral horsepower motors. Direct loss measurements have been carried out using a calorimetric test rig; detailed simulations of the skewed motors have been carried out using multi-slice time-stepped 2D FEA. The simulated and measured losses under the different modulation schemes are compared and discussed. © 2006 IEEE.

Loss comparison between an SPWM and harmonic elimination excited small, (<1kW) induction motor drive using Pspice simulation and calorimetry

Adopting square wave excitation to drive induction motors (IMs) can substantially reduce inverter switching losses. However, the low-order time harmonics inherent in the output voltage generates parasitic torques that degrade motor performance and reduce efficiency. In this paper, a novel harmonic elimination modulation technique with full voltage control is studied as an interesting and alternative means of operating small (<1kW) IM drives efficiently. A fully verified harmonic elimination scheme, which removes the 5th, 7th, 11th, 13th and 17 th time harmonics, was implemented and applied to an IGBT driven IM. The power losses incurred in the inverter and the IM as a result of the switching scheme have been determined. © 2008 Crown copyright.

Pulsed field magnetization of a high temperature superconducting motor

As we known, the high temperature (77 K) superconducting (HTS) motor is considered as a competitive electrical machine by more and more people. There have been various of designs for HTS motor in the world. However, most of them focus on HTS tapes rather than bulks. Therefore, in order to investigate possibility of HTS bulks on motor application, a HTS magnet synchronous motor which has 75 pieces of YBCO bulks surface mounted on the rotor has been designed and developed in Cambridge University. After pulsed field magnetization (PFM) process, the rotor can trap a 4 poles magnetic field of 375 mT. The magnetized rotor can provide a maximum torque of 49.5 Nm and a maximum power of 7.8 kW at 1500 rpm. © 2010 IEEE.

Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG.

In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.

Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model.

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.

Single trial learning of external dynamics: What can the brain teach us about learning mechanisms?

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has developed learning mechanisms to deal with and compensate for the constantly changing dynamics of the world. If this mechanism or mechanisms can be understood from a computational point of view, then they can also be used to drive the adaptability and learning of robots. In this paper, we will present a new technique for examining changes in the feedforward motor command due to adaptation. This technique can then be utilized for examining motor adaptation in humans and determining a computational algorithm which explains motor learning. © 2007.

Principles of sensorimotor learning.

The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities - whether it is snowboarding or ballroom dancing - but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved.