Predictive optimisation of high-power IGBT switching under Active Voltage Control

Active Voltage Control (AVC) is an implementation of classic Proportional-Derivative (PD) control and multi-loop feedback control to force IGBT to follow a pre-set switching trajectory. The initial objective of AVC was mainly to synchronise the switching of IGBTs connected in series so as to realise voltage balancing between devices. For a single IGBT switching, the AVC reference needs further optimisation. Thus, a predictive manner of AVC reference generation is required to cope with the nonlinear IGBT switching parameters while performing low loss switching. In this paper, an improved AVC structure is adopted along with a revised reference which accommodates the IGBT nonlinearity during switching and is predictive based on current being switched. Experimental and simulation results show that close control of a single IGBT switching is realised. It is concluded that good performance can be obtained, but the proposed method needs careful stability analysis for parameter choice. © 2013 IEEE.

Electrical control of nanoscale functionalization in graphene by the scanning probe technique

Functionalized graphene is a versatile material that has well-known physical and chemical properties depending on functional groups and their coverage. However, selective control of functional groups on the nanoscale is hardly achievable by conventional methods utilizing chemical modifications. We demonstrate electrical control of nanoscale functionalization of graphene with the desired chemical coverage of a selective functional group by atomic force microscopy (AFM) lithography and their full recovery through moderate thermal treatments. Surprisingly, our controlled coverage of functional groups can reach 94.9% for oxygen and 49.0% for hydrogen, respectively, well beyond those achieved by conventional methods. This coverage is almost at the theoretical maximum, which is verified through scanning photoelectron microscope measurements as well as first-principles calculations. We believe that the present method is now ready to realize 'chemical pencil drawing' of atomically defined circuit devices on top of a monolayer of graphene. © 2014 Nature Publishing Group All rights reserved.

Application of Ru(bpy)(3)(2+) in control and formation of gold surface nanostructure through oxidation-reduction cycling

It was studied that the nanostructure formed on a gold surface via a simple oxidation-reduction cycles (ORC) in 0.1 M KCl containing Ru(bpy)(3)(2+) with different concentrations. Atomic force microscopy (AFM) and energy-dispersed spectroscopy (EDS) were used to characterize the nanostructure formed on the gold surface. Sweep-step voltammetry and corresponding electroluminescence (ECL) response, in situ electrochemical quartz crystal microbalance (EQCM) measurement were used to monitor the ORC. procedure. It was found that the surface structure became more uniform in the presence of Ru(bpy)(3)(2+), and the surface roughness was decreasing with the increasing of Ru(bpY)(3)(2+) concentration, suggesting a simple and effective method to control the formation of nanostructure on the gold surface.

Toward high-performance polymer solar cells: The importance of morphology control

Polymer solar cells have the potential to become a major electrical power generating tool in the 21st century. R&D endeavors are focusing on continuous roll-to-roll printing of polymeric or organic compounds from solution-like newspapers-to produce flexible and lightweight devices at low cost. It is recognized, though, that besides the functional properties of the compounds the organization of structures on the nanometer level-forced and controlled mainly by the processing conditions applied-determines the performance of state-of-the-art polymer solar cells. In such devices the photoactive layer is composed of at least two functional materials that form nanoscale interpenetrating phases with specific functionalities, a so-called bulk heterojunction. In this perspective article, our current knowledge on the main factors determining the morphology formation and evolution is introduced, and gaps of our understanding on nanoscale structure-property relations in the field of high-performance polymer solar cells are addressed. Finally, promising routes toward formation of tailored morphologies are presented.

Control of self-organized low-dimensional morphology in Poly(styrene-b-4vinylpyridine)/polystyrene blend thin films

The surface morphologies of poly(styrene-b-4vinylpyridine) (PS-b-P4VP) diblock copolymer and homopolystyrene (hPS) binary blend thin films were investigated by atomic force microscopy as a function of total volume fraction of PS (phi(PS)) in the mixture. It was found that when hPS was added into symmetric PS-b-P4VP diblock copolymers, the surface morphology of this diblock copolymer was changed to a certain degree. With phi(PS) increasing at first, hPS was solubilized into the corresponding domains of block copolymer and formed cylinders. Moreover, the more solubilized the hPS, the more cylinders exist. However, when the limit was reached, excessive hPS tended to separate from the domains independently instead of solubilizing into the corresponding domains any longer, that is, a macrophase separation occurred. A model describing transitions of these morphologies with an increase in phi(PS) is proposed. The effect of composition on the phase morphology of blend films when graphite is used as a substrate is also investigated.

Surface morphology control of immiscible polymer-blend thin films

The effects of the molecular weights (molecular weight of polystyrene, M-w,M-PS, varying from 2.9 to 129 k) on the surface morphologies of spin-coated and annealed polystyrene/poly (methyl methacrylate) (PS/PMMA = 50/50, w/w) blend films were investigated by atomic force microscopy and X-ray photoelectron spectroscopy. For the spin-coated films, when the M-w,M-PS varied from 2.9 to 129 k, three different kinds of surface morphologies (a nanophase-separated morphology, a PMMA cellular or network-like morphology whose meshes filled with PS, a sea-island like morphology) were observed and their formation mechanisms are discussed, respectively. Upon annealing, two different morphology-evolution processes were observed. It is found that a upper PS-rich phase layer is formed when M-w,M-PS < 4 k, and this behavior is mainly attributed to the low interfacial tension between PS and PMMA component. When M-w,M-PS > 4 k, the PS-rich phase forms droplets on top of the PMMA-rich phase layer which wets the SiOx substrate. These results indicate that the surface morphology of the polymer blend films can be controlled by the polymer molecular weight and annealing conditions.

Construction and control of plasmid DNA network

The influences of different cations on plasmid DNA network structures on a mica substrate were investigated by atomic force microscopy (AFM). Interactions between the DNA strands and mica substrate, and between the DNA strands themselves were more strongly influenced by the complex cations (Fe(phen)(3)(2+), Ni(phen)(3)(2+), and Co(phen)(3)(3+)) than by the simple cations (Mg2+, Mn2+, Ni2+, Ca2+, Co3+). The mesh height of the plasmid DNA network was higher when the complex cations were added to DNA samples. The mesh size decreased with increasing DNA concentration and increased with decreasing DNA concentration in the same cation solution sample. Hence, plasmid DNA network height can be controlled by selecting different cations, and the mesh size can be controlled by adjusting plasmid DNA concentration.

Control of Vibration in Mechanical Systems Using Shaped Reference Inputs

Dynamic systems which undergo rapid motion can excite natural frequencies that lead to residual vibration at the end of motion. This work presents a method to shape force profiles that reduce excitation energy at the natural frequencies in order to reduce residual vibration for fast moves. Such profiles are developed using a ramped sinusoid function and its harmonics, choosing coefficients to reduce spectral energy at the natural frequencies of the system. To improve robustness with respect to parameter uncertainty, spectral energy is reduced for a range of frequencies surrounding the nominal natural frequency. An additional set of versine profiles are also constructed to permit motion at constant speed for velocity-limited systems. These shaped force profiles are incorporated into a simple closed-loop system with position and velocity feedback. The force input is doubly integrated to generate a shaped position reference for the controller to follow. This control scheme is evaluated on the MIT Cartesian Robot. The shaped inputs generate motions with minimum residual vibration when actuator saturation is avoided. Feedback control compensates for the effect of friction Using only a knowledge of the natural frequencies of the system to shape the force inputs, vibration can also be attenuated in modes which vibrate in directions other than the motion direction. When moving several axes, the use of shaped inputs allows minimum residual vibration even when the natural frequencies are dynamically changing by a limited amount.

The Synthesis of Stable Force-Closure Grasps

This thesis addresses the problem of synthesizing grasps that are force-closure and stable. The synthesis of force-closure grasps constructs independent regions of contact for the fingertips, such that the motion of the grasped object is totally constrained. The synthesis of stable grasps constructs virtual springs at the contacts, such that the grasped object is stable, and has a desired stiffness matrix about its stable equilibrium. A grasp on an object is force-closure if and only if we can exert, through the set of contacts, arbitrary forces and moments on the object. So force-closure implies equilibrium exists because zero forces and moment is spanned. In the reverse direction, we prove that a non-marginal equilibrium grasp is also a force-closure grasp, if it has at least two point contacts with friction in 2D, or two soft-finger contacts or three hard-finger contacts in 3D. Next, we prove that all force-closure grasps can be made stable, by using either active or passive springs at the contacts. The thesis develops a simple relation between the stability and stiffness of the grasp and the spatial configuration of the virtual springs at the contacts. The stiffness of the grasp depends also on whether the points of contact stick, or slide without friction on straight or curved surfaces of the object. The thesis presents fast and simple algorithms for directly constructing stable fore-closure grasps based on the shape of the grasped object. The formal framework of force-closure and stable grasps provides a partial explanation to why we stably grasp objects to easily, and to why our fingers are better soft than hard.

Neural Control of Interlimb Coordination and Gait Timing in Bipeds and Quadrupeds

1) A large body of behavioral data conceming animal and human gaits and gait transitions is simulated as emergent properties of a central pattern generator (CPG) model. The CPG model incorporates neurons obeying Hodgkin-Huxley type dynamics that interact via an on-center off-surround anatomy whose excitatory signals operate on a faster time scale than their inhibitory signals. A descending cornmand or arousal signal called a GO signal activates the gaits and controL their transitions. The GO signal and the CPG model are compared with neural data from globus pallidus and spinal cord, among other brain structures. 2) Data from human bimanual finger coordination tasks are simulated in which anti-phase oscillations at low frequencies spontaneously switch to in-phase oscillations at high frequencies, in-phase oscillations can be performed both at low and high frequencies, phase fluctuations occur at the anti-phase in-phase transition, and a "seagull effect" of larger errors occurs at intermediate phases. When driven by environmental patterns with intermediate phase relationships, the model's output exhibits a tendency to slip toward purely in-phase and anti-phase relationships as observed in humans subjects. 3) Quadruped vertebrate gaits, including the amble, the walk, all three pairwise gaits (trot, pace, and gallop) and the pronk are simulated. Rapid gait transitions are simulated in the order--walk, trot, pace, and gallop--that occurs in the cat, along with the observed increase in oscillation frequency. 4) Precise control of quadruped gait switching is achieved in the model by using GO-dependent modulation of the model's inhibitory interactions. This generates a different functional connectivity in a single CPG at different arousal levels. Such task-specific modulation of functional connectivity in neural pattern generators has been experimentally reported in invertebrates. Phase-dependent modulation of reflex gain has been observed in cats. A role for state-dependent modulation is herein predicted to occur in vertebrates for precise control of phase transitions from one gait to another. 5) The primary human gaits (the walk and the run) and elephant gaits (the amble and the walk) are sirnulated. Although these two gaits are qualitatively different, they both have the same limb order and may exhibit oscillation frequencies that overlap. The CPG model simulates the walk and the run by generating oscillations which exhibit the same phase relationships. but qualitatively different waveform shapes, at different GO signal levels. The fraction of each cycle that activity is above threshold quantitatively distinguishes the two gaits, much as the duty cycles of the feet are longer in the walk than in the run. 6) A key model properly concerns the ability of a single model CPG, that obeys a fixed set of opponent processing equations to generate both in-phase and anti-phase oscillations at different arousal levels. Phase transitions from either in-phase to anti-phase oscillations, or from anti-phase to in-phase oscillations, can occur in different parameter ranges, as the GO signal increases.

Neural Representations for Sensory-Motor Control, III: Learning a Body-Centered Representation of 3-D Target Position

A neural model is described of how the brain may autonomously learn a body-centered representation of 3-D target position by combining information about retinal target position, eye position, and head position in real time. Such a body-centered spatial representation enables accurate movement commands to the limbs to be generated despite changes in the spatial relationships between the eyes, head, body, and limbs through time. The model learns a vector representation--otherwise known as a parcellated distributed representation--of target vergence with respect to the two eyes, and of the horizontal and vertical spherical angles of the target with respect to a cyclopean egocenter. Such a vergence-spherical representation has been reported in the caudal midbrain and medulla of the frog, as well as in psychophysical movement studies in humans. A head-centered vergence-spherical representation of foveated target position can be generated by two stages of opponent processing that combine corollary discharges of outflow movement signals to the two eyes. Sums and differences of opponent signals define angular and vergence coordinates, respectively. The head-centered representation interacts with a binocular visual representation of non-foveated target position to learn a visuomotor representation of both foveated and non-foveated target position that is capable of commanding yoked eye movementes. This head-centered vector representation also interacts with representations of neck movement commands to learn a body-centered estimate of target position that is capable of commanding coordinated arm movements. Learning occurs during head movements made while gaze remains fixed on a foveated target. An initial estimate is stored and a VOR-mediated gating signal prevents the stored estimate from being reset during a gaze-maintaining head movement. As the head moves, new estimates arc compared with the stored estimate to compute difference vectors which act as error signals that drive the learning process, as well as control the on-line merging of multimodal information.

Unsupervised Neural Network for the Control of a Mobile Robot

This article introduces an unsupervised neural architecture for the control of a mobile robot. The system allows incremental learning of the plant during robot operation, with robust performance despite unexpected changes of robot parameters such as wheel radius and inter-wheel distance. The model combines Vector associative Map (VAM) learning and associate learning, enabling the robot to reach targets at arbitrary distances without knowledge of the robot kinematics and without trajectory recording, but relating wheel velocities with robot movements.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.

Equilibria and Dynamics of a Neural Network Model for Opponent Muscle Control

One of the advantages of biological skeleto-motor systems is the opponent muscle design, which in principle makes it possible to achieve facile independent control of joint angle and joint stiffness. Prior analysis of equilibrium states of a biologically-based neural network for opponent muscle control, the FLETE model, revealed that such independent control requires specialized interneuronal circuitry to efficiently coordinate the opponent force generators. In this chapter, we refine the FLETE circuit variables specification and update the equilibrium analysis. We also incorporate additional neuronal circuitry that ensures efficient opponent force generation and velocity regulation during movement.

Neural Representations for Sensory-Motor Control I: Head-Centered 3-D Target Positions from Opponent Eye Commands

This article describes how corollary discharges from outflow eye movement commands can be transformed by two stages of opponent neural processing into a head-centered representation of 3-D target position. This representation implicitly defines a cyclopean coordinate system whose variables approximate the binocular vergence and spherical horizontal and vertical angles with respect to the observer's head. Various psychophysical data concerning binocular distance perception and reaching behavior are clarified by this representation. The representation provides a foundation for learning head-centered and body-centered invariant representations of both foveated and non-foveated 3-D target positions. It also enables a solution to be developed of the classical motor equivalence problem, whereby many different joint configurations of a redundant manipulator can all be used to realize a desired trajectory in 3-D space.