Time Delay Compensation and Stability Analysis of Networked Predictive Control Systems Based on Hammerstein Model

A novel approach is proposed for a networked control system with random delays containing a nonlinear process based on a Hammerstein model. The method uses a time delay two step generalized predictive control (TDTSGPC), which consists of two parts, one is to deal with the input nonlinearity of the Hammerstein model and the other is to compensate the network induced delays in the networked control system. Theoretical results using the Popov theorem are presented for the closed-loop stability of the system in the case of a constant delay. Simulation examples illustrating the validity of the approach are presented

High frame rate multi-perspective cardiac ultrasound imaging using phased array probes

Ultrasound (US) imaging is used to assess cardiac disease by assessing the geometry and function of the heart utilizing its high spatial and temporal resolution. However, because of physical constraints, drawbacks of US include limited field-of-view, refraction, resolution and contrast anisotropy. These issues cannot be resolved when using a single probe. Here, an interleaved multi-perspective 2-D US imaging system was introduced, aiming at improved imaging of the left ventricle (LV) of the heart by acquiring US data from two separate phased array probes simultaneously at a high frame rate. In an ex-vivo experiment of a beating porcine heart, parasternal long-axis and apical views of the left ventricle were acquired using two phased array probes. Interleaved multi-probe US data were acquired at a frame rate of 170 frames per second (FPS) using diverging wave imaging under 11 angles. Image registration and fusion algorithms were developed to align and fuse the US images from two different probes. First- and second-order speckle statistics were computed to characterize the resulting probability distribution function and point spread function of the multi-probe image data. First-order speckle analysis showed less overlap of the histograms (reduction of 34.4%) and higher contrast-to-noise ratio (CNR, increase of 27.3%) between endocardium and myocardium in the fused images. Autocorrelation results showed an improved and more isotropic resolution for the multi-perspective images (single-perspective: 0.59 mm × 0.21 mm, multi-perspective: 0.35 mm × 0.18 mm). Moreover, mean gradient (MG) (increase of 74.4%) and entropy (increase of 23.1%) results indicated that image details of the myocardial tissue can be better observed after fusion. To conclude, interleaved multi-perspective high frame rate US imaging was developed and demonstrated in an ex-vivo experimental setup, revealing enlarged field-of-view, and improved image contrast and resolution of cardiac images.</p

Multiobjective Criteria for Nonlinear Model Selection and Identification with Neural Networks

This paper presents anew approach to model selection and identification of nonlinear systems via neural networks and genetic algorithms, based on multiobjective performance criteria. It considers three performance indices (or cost functions) in the objectives, which are the distance measurement and maximum difference measurement between the real nonlinear system and the nonlinear model, and the complexity measurement of the nonlinear model, instead of single performance index. The Volterra polynomial basis function network and the Gaussian radial basis function network are applied to approximate the nonlinear system. A numerical algorithm for multiobjective nonlinear model selection and identification using neural networks and genetic algorithms is developed

Antiproton-deuteron annihilation at low energies

Recent experimental studies of the antiproton-deuteron system at low energies have shown that the imaginary part of the antiproton-deuteron scattering length is smaller than the antiproton-proton one. Two- and three-body systems with strong annihilation are investigated and a mechanism explaining this unexpected relation between the imaginary parts of the scattering lengths is proposed.Comment: 6 pages, 3 figures, to be published in The European Physical Journal

Coherent phonon modes of crystalline and amorphous Ge2Sb2Te5 thin films: A fingerprint of structure and bonding

Copyright © 2015 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Journal of Applied Physics, Volume 117, article 025306, and may be found at http://dx.doi.org/10.1063/1.4905617Femtosecond optical pump-probe measurements have been made upon epitaxial, polycrystalline, and amorphous thin films of Ge2Sb2Te5 (GST). A dominant coherent optical phonon mode of 3.4 THz frequency is observed in time-resolved anisotropic reflectance (AR) measurements of epitaxial films, and is inferred to have 3-dimensional T2-like character based upon the dependence of its amplitude and phase on pump and probe polarization. In contrast, the polycrystalline and amorphous phases exhibit a comparatively weak mode of about 4.5 THz frequency in both reflectivity (R) and AR measurements. Raman microscope measurements confirm the presence of the modes observed in pump-probe measurements, and reveal additional modes. While the Raman spectra are qualitatively similar for all three phases of GST, the mode frequencies are found to be different within experimental error, ranging from 3.2 to 3.6 THz and 4.3 to 4.7 THz, indicating that the detailed crystallographic structure has a significant effect upon the phonon frequency. While the lower frequency (3.6 THz) mode of amorphous GST is most likely associated with GeTe4 tetrahedra, modes in epitaxial (3.4 THz) and polycrystalline (3.2 THz) GST could be associated with either GeTe6 octahedra or Sb-Te bonds within defective octahedra. The more polarizable Sb-Te bonds are the most likely origin of the higher frequency (4.3-4.7 THz) mode, although the influence of Te-Te bonds cannot be excluded. The effect of high pump fluence, which leads to irreversible structural changes, has been explored. New modes with frequency of 3.5/3.6 THz in polycrystalline/amorphous GST may be associated with Sb2Te3 or GeTe4 tetrahedra, while a 4.2 THz mode observed in epitaxial GST may be related to segregation of Sb.Engineering and Physical Sciences Research Council (EPSRC

Nucleon Axial Form Factor from Lattice QCD

Results for the isovector axial form factors of the proton from a lattice QCD calculation are presented for both point-split and local currents. They are obtained on a quenched $16^{3} \times 24$ lattice at $\beta= 6.0$ with Wilson fermions for a range of quark masses from strange to charm. We determine the finite lattice renormalization for both the local and point-split currents of heavy quarks. Results extrapolated to the chiral limit show that the $q^2$ dependence of the axial form factor agrees reasonably well with experiment. The axial coupling constant $g_A$ calculated for the local and the point-split currents is about 6\% and 12\% smaller than the experimental value respectively.Comment: 8 pages, 5 figures (included in part 2), UK/93-0

Variable Neural Networks for Adaptive Control of Nonlinear Systems

This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, is referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time according to specified design strategies so that the network will not overfit or underfit the the data set. Based on the Gaussian radial basis function variable neural network, an adaptive control scheme is presented. The location of the centres and the determination of the widths of the Gaussian radial basis functions in the variable neural network are analysed to make a compromise between orthogonality and smoothness. The weight of adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modelling error. The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using a simulated example

Stable Sequential Identification of Continuous Nonlinear Dynamical Systems by Growing RBF Networks

This paper presents a sequential identification scheme for continuous nonlinear dynamical systems using neural networks. The nonlinearities of the dynamical systems are assumed to be known. The identification model is a Gaussian radial basis function neural network that grows gradually to span the appropriate state-space and of sufficient complexity to provide an approximation to the dynamical system. The sequential identification algorithm for continuous dynamical nonlinear systems is developed in the continuous-time framework instead of discrete-time. The approach, different from the conventional methods of optimizing a cost function, attempts to ensure stability of the overall system while the neural network learns the system dynamics. The stability and convergence of the overall identification scheme is guaranteed by a parameter adjustment laws developed using the Lyapunov synthesis approach. To ensure the modelling error can be reduced arbitrarily, a one-to-one mapping is proposed so that the states and inputs of the system are transferred into compact sets. The operation of the sequential identification scheme is illustrated using simulated experimental results

Neural Network Based Variable Structure Control for Nonlinear Discrete Systems

Neural network based variable structure control is proposed for the design of nonlinear discrete systems. Sliding mode control is used to provide good stability and robustness performance for nonlinear systems. An affine nonlinear neural predictor is introduced to predict the outputs of the nonlinear process and to make the variable structure control algorithm simple and easy to implement. When the predictor model is inaccurate, variable structure control with sliding modes is used to improve the stability of the system. A recursive weight learning algorithm for the neural networks based affine nonlinear predictor is also developed and the convergence of both the weights and the estimation error is analysed

On-Line Identification of Nonlinear Systems Using Volterra Polynomial Basis Function Neural Networks

An on-line identification scheme using Volterra polynomial basis function (VPBF) neural networks is considered for nonlinear control systems. This comprises of a structure selection procedure and a recursive weight learning algorithm. The orthogonal least squares algorithm is introduced for off-line structure selection and the growing network technique is used for on-line structure selection. An on-line recursive weight learning algorithm is developed to adjust the weights so that the identified model can adapt to variations of the characteristics and operating points in the nonlinear systems. The convergence f both the weights and estimation errors is established using a Lyapunov technique. The identification procedure is illustrated using simulated examples