Alternative Formulation of the Discrete Linear Quadratic Regulator (DLQR)

In this paper, an alternative apriori and aposteriori formulation has been derived for the discrete linear quadratic regulator (DLQR) in a manner analogous to that used in the discrete Kalman filter. It has been shown that the formulation seamlessly fits into the available formulation of the DLQR and the equivalent terms in the existing formulation and the proposed formulation have been identified. Thereafter, the significance of this alternative formulation has been interpreted in terms of the sensitivity of the controller performances to any changes in the states or to changes in the control inputs. The implications of this alternative formulation to adaptive controller tuning have also been discussed.

Nonlinear control of an air-breathing engine including its validation with vehicle guidance

An implementable nonlinear control design approach is presented for a supersonic air-breathing ramjet engine. The primary objective is to ensure that the thrust generated by the engine tracks the commanded thrust without violating the operational constraints. An important constraint is to manage the shock wave location in the intake so that it neither gets detached nor gets too much inside the intake. Both the objectives are achieved by regulating the fuel flow to the combustion chamber and by varying the throat area of the nozzle simultaneously. The design approach accounts for the nonlinear cross-coupling effects and nullifies those. Also, an extended Kalman filter has been used to filter out the sensor and process noises as well as to make the states available for feedback. Furthermore, independent control design has been carried out for the actuators. To test the performance of the engine for a realistic flight trajectory, a representative trajectory is generated through a trajectory optimization process, which is augmented with a newly-developed finite-time state dependent Riccati equation technique for nullifying the perturbations online. Satisfactory overall performance has been obtained during both climb and cruise phases. (C) 2015 Elsevier Masson SAS. All rights reserved.

System reliability analysis of granular filter for protection against piping in dams

Granular filters are provided for the safety of water retaining structure for protection against piping failure. The phenomenon of piping triggers when the base soil to be protected starts migrating in the direction of seepage flow under the influence of seepage force. To protect base soil from migration, the voids in the filter media should be small enough but it should not also be too small to block smooth passage of seeping water. Fulfilling these two contradictory design requirements at the same time is a major concern for the successful performance of granular filter media. Since Terzaghi era, conventionally, particle size distribution (PSD) of granular filters is designed based on particle size distribution characteristics of the base soil to be protected. The design approach provides a range of D15f value in which the PSD of granular filter media should fall and there exist infinite possibilities. Further, safety against the two critical design requirements cannot be ensured. Although used successfully for many decades, the existing filter design guidelines are purely empirical in nature accompanied with experience and good engineering judgment. In the present study, analytical solutions for obtaining the factor of safety with respect to base soil particle migration and soil permeability consideration as proposed by the authors are first discussed. The solution takes into consideration the basic geotechnical properties of base soil and filter media as well as existing hydraulic conditions and provides a comprehensive solution to the granular filter design with ability to assess the stability in terms of factor of safety. Considering the fact that geotechnical properties are variable in nature, probabilistic analysis is further suggested to evaluate the system reliability of the filter media that may help in risk assessment and risk management for decision making.

Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

A Voltage Space Vector Diagram Formed by Nineteen Concentric Dodecagons for Medium-Voltage Induction Motor Drive

In this paper, a multilevel dodecagonal voltage space vector structure with nineteen concentric dodecagons is proposed for the first time. This space vector structure is achieved by cascading two sets of asymmetric three-level inverters with isolated H-bridges on either side of an open-end winding induction motor. The dodecagonal structure is made possible by proper selection of dc link voltages and switching states of the inverters. The proposed scheme retains all the advantages of multilevel topologies as well as the advantages of dodecagonal voltage space vector structure. In addition to that, a generic and simple method for calculation of pulsewidth modulation timings using only sampled reference values (v(alpha) and v(beta)) is proposed. This enables the scheme to be used for any closed-loop application such as vector control. In addition, a new method of switching technique is proposed, which ensures minimum switching while eliminating the fifth-and seventh-order harmonics and suppressing the eleventh and thirteenth harmonics, eliminating the need for bulky filters. The motor phase voltage is a 24-stepped wave-form for the entire modulation range thereby reducing the number of switchings of the individual inverter modules. Experimental results for steady-state operation, transient operation, including start-up have been presented and the results of fast Fourier transform analysis is also presented for validating the proposed concept.

Micro-satellite angular rate estimation using second-order sliding mode observer

Development of computationally efficient and accurate attitude rate estimation algorithm using low-cost commercially available star sensor arrays and processing unit for micro-satellite mission is presented. Our design reduces the computational load of least square (LS)-based rate estimation method while maintaining the same accuracy compared to other rate estimation approaches. Furthermore, rate estimation accuracy is improved by using recently developed fast and accurate second-order sliding mode observer (SOSMO) scheme. It also gives robust estimation in the presence of modeling uncertainties, unknown disturbances, and measurement noise. Simulation study shows that rate estimation accuracy achieved by our LS-based method is comparable with other methods for a typical commercially available star sensor array. The robustness analysis of SOSMO with respect to measurement noise is also presented in this paper. Simulation test bench for a practical scenario of satellite rate estimation uses moment-of-inertia variation and environmental disturbances affecting a typical micro-satellite at 500km circular orbit. Comparison studies of SOSMO with 1-SMO and pseudo-linear Kalman filter show that satisfactory estimation accuracy is achieved by SOSMO.

An Ensemble Kushner-Stratonovich-Poisson Filter for Recursive Estimation in Nonlinear Dynamical Systems

We propose a Monte Carlo filter for recursive estimation of diffusive processes that modulate the instantaneous rates of Poisson measurements. A key aspect is the additive update, through a gain-like correction term, empirically approximated from the innovation integral in the time-discretized Kushner-Stratonovich equation. The additive filter-update scheme eliminates the problem of particle collapse encountered in many conventional particle filters. Through a few numerical demonstrations, the versatility of the proposed filter is brought forth.

Perceptual thresholds for detecting modifications applied to the acoustical properties of a violin

This study is the first step in the psychoacoustic exploration of perceptual differences between the sounds of different violins. A method was used which enabled the same performance to be replayed on different "virtual violins," so that the relationships between acoustical characteristics of violins and perceived qualities could be explored. Recordings of real performances were made using a bridge-mounted force transducer, giving an accurate representation of the signal from the violin string. These were then played through filters corresponding to the admittance curves of different violins. Initially, limits of listener performance in detecting changes in acoustical characteristics were characterized. These consisted of shifts in frequency or increases in amplitude of single modes or frequency bands that have been proposed previously to be significant in the perception of violin sound quality. Thresholds were significantly lower for musically trained than for nontrained subjects but were not significantly affected by the violin used as a baseline. Thresholds for the musicians typically ranged from 3 to 6 dB for amplitude changes and 1.5%-20% for frequency changes. interpretation of the results using excitation patterns showed that thresholds for the best subjects were quite well predicted by a multichannel model based on optimal processing. (c) 2007 Acoustical Society of America.

Stainless steel sandwich

The Cambridge University's Gordon Laboratory, in collaboration with Fibertech and the Defence Science and Technology Laboratory in the UK, has developed a novel melt spun fiber bore called 'Fibrecore', fabricated entirely from stainless steel with thin faceplates. Fibrecore is typically manufactured by 5mm-long and 70μm thick stainless steel fibers, produced by a melt overflow process. Its entirely metallic construction allows spot welding and tungsten inert gas welding without difficulty. Fibrecore exhibits different energy absorption mechanisms such as core cushioning, core-faceplate delamination, and plastic faceplate deformation, often in a concertina-like fashion. Its low-cost, high structural efficiency and good energy absorption characteristics make it attractive for a range of commercial and military applications. Such applications being evaluated include vehicle body panels, exhaust system noise reduction, low cost filters, and lightweight physical protection. In addition to these characteristics, Fibrecore exhibits properties such as corrosion protection, vibrational damping, and thermal insulation, which also extend its applications.

Bayesian separation and recovery of convolutively mixed autoregressive sources

In this paper we address the problem of the separation and recovery of convolutively mixed autoregressive processes in a Bayesian framework. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) methods. We present three algorithms. The first one is a classical Gibbs sampler that generates samples from the posterior distribution. The two other algorithms are stochastic optimization algorithms that allow to optimize either the marginal distribution of the sources, or the marginal distribution of the parameters of the sources and mixing filters, conditional upon the observation. Simulations are presented.

Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.

Long Term Dynamics in CO2 Allowance Prices and Carbon Capture Investments

In this paper we analyse the behaviour of the EU market for CO2 emission allowances; specifically, we focus on the contracts maturing in the Kyoto Protocol's second period of application (2008 to 2012). We calibrate the underlying parameters for the allowance price in the long run and we also calibrate those from the Spanish wholesale electricity market. This information is then used to assess the option to install a carbon capture and storage (CCS) unit in a coal-fired power plant. We use a two-dimensional binomial lattice where costs and profits are valued and the optimal investment time is determined. In other words, we study the trigger allowance prices above which it is optimal to install the capture unit immediately. We further analyse the impact of several variables on the critical prices, among them allowance price volatility and a hypothetical government subsidy. We conclude that, at current permit prices, from a financial point of view, immediate installation does not seem justified. This need not be the case, though, if carbon market parameters change dramatically and/or a specific policy to promote these units is adopted.