Operation of brushless doubly-fed machine for drive applications

This paper presents dynamic and steady-state performance of the Brushless Doubly-Fed Machine (BDFM) operating as a variable speed drive. A simple closed-loop control system is used which only requires a speed feedback. The controller is capable of stabilising the machine when changes in speed and torque are applied. The machine starts in cascade mode and then makes a transition to the synchronous mode to reach the desired speed. This will allow a uni-directional converter to be used. The experiments included in this paper were carried out on a 180 frame size BDFM.

Stable operation of the Brushless Doubly-Fed Machine (BDFM)

A simple method of controlling the Brushless Doubly-Fed Machine (BDFM) is presented. The controller comprises two Proportional-Integral (PI) modules and requires only the rotor speed feedback. The machine model and the control system are developed in MATLAB. Both simulation and experimental results are presented. The performance of the system is presented in the motoring and generating operations. The experimental tests included in this paper were carried out on a 180 frame size BDFM with a nested-loop rotor. © 2007 IEEE.

Performance of thin film carbon materials and carbon nanotubes as cold cathodes

The field emissions from three different types of carbon films are studied using a Kiethly voltage-current source-measure unit under computer control. The three types of carbon films are : 1) a-C:H:N deposited using an inductively coupled rf PECVD process, where the N content in the films can be as high as 30 at %; 2) cathodic arc deposited tetrahedral amorphous carbon with embedded regions of carbon nanotube and anion structures and 3) unoriented carbon nanotube films on a porous substrate. The films are formed by filtering a solution of nanotubes dispersed in alcohol through the pores and drying.

Visuomotor feedback gains upregulate during the learning of novel dynamics.

At an early stage of learning novel dynamics, changes in muscle activity are mainly due to corrective feedback responses. These feedback contributions to the overall motor command are gradually reduced as feedforward control is learned. The temporary increased use of feedback could arise simply from the large errors in early learning with either unaltered gains or even slightly downregulated gains, or from an upregulation of the feedback gains when feedforward prediction is insufficient. We therefore investigated whether the sensorimotor control system alters feedback gains during adaptation to a novel force field generated by a robotic manipulandum. To probe the feedback gains throughout learning, we measured the magnitude of involuntary rapid visuomotor responses to rapid shifts in the visual location of the hand during reaching movements. We found large increases in the magnitude of the rapid visuomotor response whenever the dynamics changed: both when the force field was first presented, and when it was removed. We confirmed that these changes in feedback gain are not simply a byproduct of the change in background load, by demonstrating that this rapid visuomotor response is not load sensitive. Our results suggest that when the sensorimotor control system experiences errors, it increases the gain of the visuomotor feedback pathways to deal with the unexpected disturbances until the feedforward controller learns the appropriate dynamics. We suggest that these feedback gains are upregulated with increased uncertainty in the knowledge of the dynamics to counteract any errors or disturbances and ensure accurate and skillful movements.

The performance of closed reactor grade plutonium-thorium fuel cycles in reduced-moderation pressurised water reactors

The production of long-lived transuranic (TRU) waste is a major disadvantage of fission-based nuclear power. Previous work has indicated that TRU waste can be virtually eliminated in a pressurised water reactor (PWR) fuelled with a mixture of thorium and TRU waste, when all actinides are returned to the reactor after reprocessing. However, the optimal configuration for a fuel assembly operating this fuel cycle is likely to differ from the current configuration. In this paper, the differences in performance obtained in a reduced-moderation PWR operating this fuel cycle were investigated using WIMS. The chosen configuration allowed an increase of at least 20% in attainable burn-up for a given TRU enrichment. This will be especially important if the practical limit on TRU enrichment is low. The moderator reactivity coefficients limit the enrichment possible in the reactor, and this limit is particularly severe if a negative void coefficient is required for a fully voided core. Several strategies have been identified to mitigate this. Specifically, the control system should be designed to avoid a detrimental effect on moderator reactivity coefficients. The economic viability of this concept is likely to be dependent on the achievable thermal-hydraulic operating conditions. © 2012 Elsevier Ltd. All rights reserved.

Generalization in Adaptation to Stable and Unstable Dynamics

Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization. © 2012 Kadiallah et al.

Stabilization of periodic orbits in a wedge billiard

This paper introduces a stabilization problem for an elementary impact control system in the plane. The rich dynamical properties of the wedge billiard, combined to the relevance of the associated stabilization problem for feedback control issues in legged robotics make it a valuable benchmark for energy-based stabilization of impact control systems.