State and input simultaneous estimation for a class of time-delay systems with uncertainties

This brief addresses the problem of estimation of both the states and the unknown inputs of a class of systems that are subject to a time-varying delay in their state variables, to an unknown input, and also to an additive uncertain, nonlinear disturbance. Conditions are derived for the solvability of the design matrices of a reduced-order observer for state and input estimation, and for the stability of its dynamics. To improve computational efficiency, a delay-dependent asymptotic stability condition is then developed using the linear matrix inequality formulation. A design procedure is proposed and illustrated by a numerical example.<br /

Actuator fault diagnosis of singular delayed LPV systems with inexact measured parameters via PI unknown input observer

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this study, actuator fault diagnosis of singular delayed linear parameter varying (SDLPV) systems is considered. The considered system has a time-varying state delay and its matrices are dependent on some parameters that are measurable online. It is assumed that the measured parameters are inexact due to the existence of noise in real situations. The system with inexact measured parameters is converted to an uncertain system. Actuator fault diagnosis is carried out based on fault size estimation. For this purpose, the system is transformed to a polytopic representation and then a polytopic proportional integral unknown input observer (PI-UIO) is designed. The proposed observer provides simultaneous state and actuator fault estimation while attenuating, in the H8H8 sense, the effects of input disturbance, output noise and the uncertainty caused by inexact measured parameters. The design procedure of PI-UIO is formulated as a convex optimisation problem with a set of Linear Matrix Inequality (LMI) constraints in the vertices of the parameter domain, guaranteeing robust exponential convergence of the PI-UIO. The efficiency of the proposed method is illustrated with an electrical circuit example modelled as an SDLPV system.Peer ReviewedPostprint (author's final draft

Adaptive Nonlinear RF Cancellation for Improved Isolation in Simultaneous Transmit-Receive Systems

This paper proposes an active radio frequency (RF) cancellation solution to suppress the transmitter (TX) passband leakage signal in radio transceivers supporting simultaneous transmission and reception. The proposed technique is based on creating an opposite-phase baseband equivalent replica of the TX leakage signal in the transceiver digital front-end through adaptive nonlinear filtering of the known transmit data, to facilitate highly accurate cancellation under a nonlinear TX power amplifier (PA). The active RF cancellation is then accomplished by employing an auxiliary transmitter chain, to generate the actual RF cancellation signal, and combining it with the received signal at the receiver (RX) low noise amplifier (LNA) input. A closed-loop parameter learning approach, based on the decorrelation principle, is also developed to efficiently estimate the coefficients of the nonlinear cancellation filter in the presence of a nonlinear TX PA with memory, finite passive isolation, and a nonlinear RX LNA. The performance of the proposed cancellation technique is evaluated through comprehensive RF measurements adopting commercial LTE-Advanced transceiver hardware components. The results show that the proposed technique can provide an additional suppression of up to 54 dB for the TX passband leakage signal at the RX LNA input, even at considerably high transmit power levels and with wide transmission bandwidths. Such novel cancellation solution can therefore substantially improve the TX-RX isolation, hence reducing the requirements on passive isolation and RF component linearity, as well as increasing the efficiency and flexibility of the RF spectrum use in the emerging 5G radio networks.Comment: accepted to IEE

Entanglement-Enhanced Lidars for Simultaneous Range and Velocity Measurements

Lidar is a well known optical technology for measuring a target's range and radial velocity. We describe two lidar systems that use entanglement between transmitted signals and retained idlers to obtain significant quantum enhancements in simultaneous measurement of these parameters. The first entanglement-enhanced lidar circumvents the Arthurs-Kelly uncertainty relation for simultaneous measurement of range and radial velocity from detection of a single photon returned from the target. This performance presumes there is no extraneous (background) light, but is robust to the roundtrip loss incurred by the signal photons. The second entanglement-enhanced lidar---which requires a lossless, noiseless environment---realizes Heisenberg-limited accuracies for both its range and radial-velocity measurements, i.e., their root-mean-square estimation errors are both proportional to $1/M$ when $M$ signal photons are transmitted. These two lidars derive their entanglement-based enhancements from use of a unitary transformation that takes a signal-idler photon pair with frequencies $\omega_S$ and $\omega_I$ and converts it to a signal-idler photon pair whose frequencies are $(\omega_S + \omega_I)/2$ and $\omega_S-\omega_I$. Insight into how this transformation provides its benefits is provided through an analogy to superdense coding.Comment: 7 pages, 3 figure

Parametric Macromodels of Drivers for SSN Simulations

This paper addresses the modeling of output and power supply ports of digital drivers for accurate and efficient SSN simulations. The proposed macromodels are defined by parametric relations, whose parameters are estimated from measured or simulated port transient responses, and are implemented as SPICE subcircuits. The modeling technique is applied to commercial high-speed devices and a realistic simulation example is shown

Experimental multiphase estimation on a chip

Multiparameter estimation is a general problem that aims at measuring unknown physical quantities, obtaining high precision in the process. In this context, the adoption of quantum resources promises a substantial boost in the achievable performances with respect to the classical case. However, several open problems remain to be addressed in the multiparameter scenario. A crucial requirement is the identification of suitable platforms to develop and experimentally test novel efficient methodologies that can be employed in this general framework. We report the experimental implementation of a reconfigurable integrated multimode interferometer designed for the simultaneous estimation of two optical phases. We verify the high-fidelity operation of the implemented device, and demonstrate quantum-enhanced performances in two-phase estimation with respect to the best classical case, post-selected to the number of detected coincidences. This device can be employed to test general adaptive multiphase protocols due to its high reconfigurability level, and represents a powerful platform to investigate the multiparameter estimation scenario.Comment: 10+7 pages, 7+4 figure

A Model for the Genesis of Arterial Pressure Mayer Waves from Heart Rate and Sympathetic Activity

Both theoretic models and cross-spectral analyses suggest that an oscillating sympathetic nervous outflow generates the low frequency arterial pressure fluctuations termed Mayer waves. Fluctuations in heart rate also have been suggested to relate closely to Mayer waves, but empiric models have not assessed the joint causative influences of hemt rate and sympathetic activity. Therefore, we constructed a model based simply upon the hemodynamic equation deriving from Ohm's Law. With this model, we determined time relations and relative contributions of heart rate and sympathetic activity to the genesis of arterial pressure Mayer waves. We assessed data from eight healthy young volunteers in the basal state and in a high sympathetic state known to produce concurrent increases in sympathetic nervous outflow and Mayer wave amplitude. We fit the Mayer waves (0.05-0.20 Hz) in mean arterial pressure by the weighted sum ofleading oscillations in heart rate and sympathetic nerve activity. This model of our data showed heart rate oscillations leading by 2-3.75 seconds were responsible for almost half of the variance in arterial pressure (basal R^2=0.435±0.140, high sympathetic R^2=0.438±0.180). Surprisingly, sympathetic activity (lead 0-5 seconds) contributed only modestly to the explained variance in Mayer waves during either sympathetic state (basal: ∆R^2=0.046±0.026; heightened: ∆R^2=0.085±0.036). Thus, it appears that heart rate oscillations contribute to Mayer waves in a simple linear fashion, whereas sympathetic fluctuations contribute little to Mayer waves in this way. Although these results do not exclude an important vascular sympathetic role, they do suggest that additional Ji1ctors, such as sympathetic transduction into vascular resistance, modulate its influence.Binda and Fred Shuman Foundation; National Institute on Aging (AG14376)

A Model for the Genesis of Arterial Pressure Mayer Waves from Heart Rate and Sympathetic Activity

Both theoretic models and cross-spectral analyses suggest that an oscillating sympathetic nervous outflow generates the low frequency arterial pressure fluctuations termed Mayer waves. Fluctuations in heart rate also have been suggested to relate closely to Mayer waves, but empiric models have not assessed the joint causative influences of hemt rate and sympathetic activity. Therefore, we constructed a model based simply upon the hemodynamic equation deriving from Ohm's Law. With this model, we determined time relations and relative contributions of heart rate and sympathetic activity to the genesis of arterial pressure Mayer waves. We assessed data from eight healthy young volunteers in the basal state and in a high sympathetic state known to produce concurrent increases in sympathetic nervous outflow and Mayer wave amplitude. We fit the Mayer waves (0.05-0.20 Hz) in mean arterial pressure by the weighted sum ofleading oscillations in heart rate and sympathetic nerve activity. This model of our data showed heart rate oscillations leading by 2-3.75 seconds were responsible for almost half of the variance in arterial pressure (basal R^2=0.435±0.140, high sympathetic R^2=0.438±0.180). Surprisingly, sympathetic activity (lead 0-5 seconds) contributed only modestly to the explained variance in Mayer waves during either sympathetic state (basal: ∆R^2=0.046±0.026; heightened: ∆R^2=0.085±0.036). Thus, it appears that heart rate oscillations contribute to Mayer waves in a simple linear fashion, whereas sympathetic fluctuations contribute little to Mayer waves in this way. Although these results do not exclude an important vascular sympathetic role, they do suggest that additional Ji1ctors, such as sympathetic transduction into vascular resistance, modulate its influence.Binda and Fred Shuman Foundation; National Institute on Aging (AG14376)