Dead-time minimisation using active voltage control gate drive for high-power IGBT converters

High-power converters usually need longer dead-times than their lower-power counterparts and a lower switching frequency. Also due to the complicated assembly layout and severe variations in parasitics, in practice the conventional dead-time specific adjustment or compensation for high-power converters is less effective, and usually this process is time-consuming and bespoke. For general applications, minimising or eliminating dead-time in the gate drive technology is a desirable solution. With the growing acceptance of power electronics building blocks (PEBB) and intelligent power modules (IPM), gate drives with intelligent functions are in demand. Smart functions including dead time elimination/minimisation can improve modularity, flexibility and reliability. In this paper, a dead-time minimisation using Active Voltage Control (AVC) gate drive is presented. © 2012 IEEE.

Diesel smoke transient control using a real-time smoke sensor

A novel real time smoke sensor is described, which is mounted in the exhaust manifold and detects the smoke by virtue of the natural electrical charge which is carried on the smoke. The somewhat obscure origin of the charge on the smoke is briefly considered, as well as the operation of the sensor itself. The use of the sensor as part of a feedback control shows that it can be very effective in reducing smoke puffs. Copyright © 1987 Society of Automotive Engineers, Inc.

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Control of bimanual rhythmic movements: Trading efficiency for robustness depending on the context

This paper investigates how the efficiency and robustness of a skilled rhythmic task compete against each other in the control of a bimanual movement. Human subjects juggled a puck in 2D through impacts with two metallic arms, requiring rhythmic bimanual actuation. The arms kinematics were only constrained by the position, velocity and time of impacts while the rest of the trajectory did not influence the movement of the puck. In order to expose the task robustness, we manipulated the task context in two distinct manners: the task tempo was assigned at four different values (hence manipulating the time available to plan and execute each impact movement individually); and vision was withdrawn during half of the trials (hence reducing the sensory inflows). We show that when the tempo was fast, the actuation was rhythmic (no pause in the trajectory) while at slow tempo, the actuation was discrete (with pause intervals between individual movements). Moreover, the withdrawal of visual information encouraged the rhythmic behavior at the four tested tempi. The discrete versus rhythmic behavior give different answers to the efficiency/robustness trade-off: discrete movements result in energy efficient movements, while rhythmic movements impact the puck with negative acceleration, a property preserving robustness. Moreover, we report that in all conditions the impact velocity of the arms was negatively correlated with the energy of the puck. This correlation tended to stabilize the task and was influenced by vision, revealing again different control strategies. In conclusion, this task involves different modes of control that balance efficiency and robustness, depending on the context. © 2008 Springer-Verlag.

Generating Options for Active Risk Control (GO-ARC): introducing a novel technique.

BACKGROUND: After investing significant amounts of time and money in conducting formal risk assessments, such as root cause analysis (RCA) or failure mode and effects analysis (FMEA), healthcare workers are left to their own devices in generating high-quality risk control options. They often experience difficulty in doing so, and tend toward an overreliance on administrative controls (the weakest category in the hierarchy of risk controls). This has important implications for patient safety and the cost effectiveness of risk management operations. This paper describes a before and after pilot study of the Generating Options for Active Risk Control (GO-ARC) technique, a novel tool to improve the quality of the risk control options generation process. OUTCOME MEASURES: The quantity, quality (using the three-tiered hierarchy of risk controls), variety, and novelty of risk controls generated. RESULTS: Use of the GO-ARC technique was associated with improvement on all measures. CONCLUSIONS: While this pilot study has some notable limitations, it appears that the GO-ARC technique improved the risk control options generation process. Further research is needed to confirm this finding. It is also important to note that improved risk control options are a necessary, but not sufficient, step toward the implementation of more robust risk controls.

Precise modal excitation in multimode fibre for control of modal dispersion and mode-group division multiplexing

Each mode of a multimode fibre is excited using binary phase patterns on a Spatial Light Modulator and verified by observation of the near-field leaving the fibre and analysis of the step response. © 2011 OSA.

Linear multimodal actuation through discrete coupling

Due to technological limitations, robot actuators are often designed for specific tasks with narrow performance goals, whereas a wide range of behaviors is necessary for autonomous robots in uncertain complex environments. In an effort to increase the versatility of actuators, we introduce a new concept of multimodal actuation (MMA) that employs dynamic coupling in the form of clutches and brakes to change its mode of operation. The dynamic coupling allows motors and passive elements such as springs to be engaged and disengaged within a single actuator. We apply the concept to a linear series elastic actuator which uses friction brakes controlled online for the dynamic coupling. With this prototype, we are able to demonstrate several modes of operation including stiff position control, series elastic actuation as well as the possibility to store and release energy in a controlled manner for explosive tasks such as jumping. In this paper, we model the proposed concept of actuation and show a systematic performance analysis of the physical prototype that we developed in our laboratory. © 1996-2012 IEEE.

Trajectory control based on discrete full-range dynamics

There has been an increasing interest in the use of mechanical dynamics, (e.g., assive, Elastic, And viscous dynamics) for energy efficient and agile control of robotic systems. Despite the impressive demonstrations of behavioural performance, The mechanical dynamics of this class of robotic systems is still very limited as compared to those of biological systems. For example, Passive dynamic walkers are not capable of generating joint torques to compensate for disturbances from complex environments. In order to tackle such a discrepancy between biological and artificial systems, We present the concept and design of an adaptive clutch mechanism that discretely covers the full-range of dynamics. As a result, The system is capable of a large variety of joint operations, including dynamic switching among passive, actuated and rigid modes. The main innovation of this paper is the framework and algorithm developed for controlling the trajectory of such joint. We present different control strategies that exploit passive dynamics. Simulation results demonstrate a significant improvement in motion control with respect to the speed of motion and energy efficiency. The actuator is implemented in a simple pendulum platform to quantitatively evaluate this novel approach.