Search results for "Quantum physic"

Observer-based adaptive stabilization of a class of uncertain nonlinear systems

2014

In this paper, an adaptive output feedback stabilization method for a class of uncertain nonlinear systems is presented. Since this approach does not require any information about the bound of uncertainties, this information is not needed a priori and a mechanism for its estimation is exploited. The adaptation law is obtained using the Lyapunov direct method. Since all the states are not measurable, an observer is designed to estimate unmeasurable states for stabilization. Therefore, in the design procedure, first an observer is designed and then the control signal is constructed based on the estimated states and adaptation law with the σ-modification algorithm. The uniformly ultimately bou…

Adaptive Neural Stabilizing Controller for a Class of Mismatched Uncertain Nonlinear Systems by State and Output Feedback

2015

In this paper, first, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is proposed. By using a radial basis function NN (RBFNN), a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. Then, an observer-based adaptive controller based on RBFNN is designed to stabilize uncertain nonlinear systems with immeasurable states. The state-feedback and observer-based controllers are based on Lyapunov and strictly positive real-Lyapunov stability theory, respectively, and it is shown that the asymptotic convergence of the closed-loop system to ze…

2014

This paper deals with the fault detection problem for a class of discrete-time wireless networked control systems described by switching topology with uncertainties and disturbances. System states of each individual node are affected not only by its own measurements, but also by other nodes’ measurements according to a certain network topology. As the topology of system can be switched in a stochastic way, we aim to designH∞fault detection observers for nodes in the dynamic time-delay systems. By using the Lyapunov method and stochastic analysis techniques, sufficient conditions are acquired to guarantee the existence of the filters satisfying theH∞performance constraint, and observer gains…

Saturation of a spin-1/2 particle by generalized local control

2011

We show how to apply a generalization of Local control design to the problem of saturation of a spin 1/2 particle by magnetic fields in Nuclear Magnetic Resonance. The generalization of local or Lyapunov control arises from the fact that the derivative of the Lyapunov function does not depend explicitly on the control field. The second derivative is used to determine the local control field. We compare the efficiency of this approach with respect to the time-optimal solution which has been recently derived using geometric methods.

An LMI Approach to Exponential Stock Level Estimation for Large-Scale Logistics Networks

2013

This article aims to present a convex optimization approach for exponential stock level estimation problem of large-scale logistics networks. The model under consideration presents the dependency and interconnections between the dynamics of each single location. Using a Lyapunov function, new sufficient conditions for exponential estimation of the networks are driven in terms of linear matrix inequalities (LMIs). The explicit expression of the observer gain is parameterized based on the solvability conditions. A numerical example is included to illustrate the applicability of the proposed design method.

Adaptive output feedback neural network control of uncertain non-affine systems with unknown control direction

2014

Abstract This paper deals with the problem of adaptive output feedback neural network controller design for a SISO non-affine nonlinear system. Since in practice all system states are not available in output measurement, an observer is designed to estimate these states. In comparison with the existing approaches, the current method does not require any information about the sign of control gain. In order to handle the unknown sign of the control direction, the Nussbaum-type function is utilized. In order to approximate the unknown nonlinear function, neural network is firstly exploited, and then to compensate the approximation error and external disturbance a robustifying term is employed. …

Faults diagnosis based on proportional integral observer for TS fuzzy model with unmeasurable premise variable

2014

In this work, we focus on the synthesis of a Proportional Integral (PI) observer for the actuators and sensors faults diagnosis based on Takagi-Sugeno (TS) fuzzy model with unmeasurable premise variables. The faults estimation method is based on the assumption that these faults act as unknown inputs under polynomials form whose their kth derivatives are bounded. The convergence conditions of the observer as well as the faults reconstruction are established on the basis of the Lyapunov stability theory and the L 2 optimization technique, expressed as Linear Matrix Inequalities (LMI) constraints. In order to validate the proposed approach, a hydraulic system with two tanks is proposed.

Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer

2013

Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2013/670878 Open Access This paper presents an unknown input Proportional Multiple-Integral Observer (PIO) for synchronization of chaotic systems based on Takagi-Sugeno (TS) fuzzy chaotic models subject to unmeasurable decision variables and unknown input. In a secure communication configuration, this unknown input is regarded as a message encoded in the chaotic system and recovered by the proposed PIO. Both states and outputs of the fuzzy chaotic models are subject to polynomial unknown input with kth derivative zero. Using Lyapunov stability theory…

Solution nuclear magnetic resonance spectroscopy on a nanostructured diamond chip

2017

We demonstrate nuclear magnetic resonance (NMR) spectroscopy of picoliter-volume solutions with a nanostructured diamond chip. Using optical interferometric lithography, diamond surfaces were nanostructured with dense, high-aspect-ratio nanogratings, enhancing the surface area by more than a factor of 15 over mm^2 regions of the chip. The nanograting sidewalls were doped with nitrogen-vacancy (NV) centers so that more than 10 million NV centers in a (25 micrometer)^2 laser spot are located close enough to the diamond surface (5 nm) to detect the NMR spectrum of 1 pL of fluid lying within adjacent nanograting grooves. The platform was used to perform 1H and 19F NMR spectroscopy at room tempe…

Entanglement-Based dc magnetometry with separated ions

2017

We demonstrate sensing of inhomogeneous dc magnetic fields by employing entangled trapped ions, which are shuttled in a segmented Paul trap. As sensor states, we use Bell states of the type j↑↓i þ eiφj↓↑i encoded in two 40Caþ ions stored at different locations. The linear Zeeman effect leads to the accumulation of a relative phase φ, which serves for measuring the magnetic-field difference between the constituent locations. Common-mode magnetic-field fluctuations are rejected by the entangled sensor state, which gives rise to excellent sensitivity without employing dynamical decoupling and therefore enables accurate dc sensing. Consecutive measurements on sensor states encoded in the S1=2 g…