In-depth mesocrystal formation analysis of microwave-assisted synthesis of LiMnPO4nanostructures in organic solution

In the present work, we report on the preparation of LiMnPO4 (lithiophilite) nanorods and mesocrystals composed of self-assembled rod subunits employing microwave-assisted precipitation with processing times on the time scale of minutes. Starting from metal salt precursors and H3PO4 as phosphate source, single-phase LiMnPO4 powders with grain sizes of approx. 35 and 65 nm with varying morphologies were obtained by tailoring the synthesis conditions using rac-1-phenylethanol as solvent. The mesocrystal formation, microstructure and phase composition were determined by electron microscopy, nitrogen physisorption, X-ray diffraction (including Rietveld refinement), dynamic light scattering, X-ray absorption and X-ray photoelectron spectroscopy, and other techniques. In addition, we investigated the formed organic matter by gas chromatography coupled with mass spectrometry in order to gain a deeper understanding of the dissolution\u2013precipitation process. Also, we demonstrate that the obtained LiMnPO4 nanocrystals can be redispersed in polar solvents such as ethanol and dimethylformamide and are suitable as building blocks for the fabrication of nanofibers via electrospinning

Numerically robust synthesis of discrete-time H[infinity] estimators based on dual J-lossless factorisations

An approach to the numerically reliable synthesis of the H[infinity] suboptimal state estimators for discretised continuous-time processes is presented. The approach is based on suitable dual J-lossless factorisations of chain-scattering representations of estimated processes. It is demonstrated that for a sufficiently small sampling period the standard forward shift operator techniques may become ill-conditioned and numerical robustness of the design procedures can be significantly improved by employing the so-called delta operator models of the process. State-space models of all H[infinity] sub-optimal estimators are obtained by considering the suitable delta-domain algebraic Riccati equation and the corresponding generalised eigenproblem formulation. A relative condition number of this equation is used as a measure of its numerical conditioning. Both regular problems concerning models having no zeros on the boundary of the delta-domain stability region and irregular (non-standard) problems of models with such zeros are examined. For the first case, an approach based on a dual J-lossless factorisation is proposed while in the second case an extended dual J-lossless factorisation based on a zero compensator technique s required. Two numerical examples are given to illustrate some properties of the considered delta-domain approach

A reliable synthesis of discrete-time Η∞ control. Part I: basic theorems and J-lossless conjugators

The paper gives a basis for solving many problems of numerically reliable synthesis of sub-optimal discrete-time control in Η∞. The approach is based on J-lossless factorisations of the delta-domain chain-scattering descriptions of continuous-time plants being controlled. Relevant properties of poles and zeros of chain-scattering models are given. Necessary and sufficient conditions for the existence of stabilising J-lossless conjugators are presented and discussed. Some aspects of numerical conditioning of synthesis of such conjugators are considered. A numerical example illustrating synthesis of stabilising right J-lossless conjugators is also included

Control of delay plants via continuous-time GPC principle

The continuous-time generalised predictive control (CGPC) is considered in the context of control of continuous-time systems having a transportation delay. It is shown that the basic CGPC design strategy can be given in a form which facilitates a clear discussion of relevant design consequences concerning stability issues. The main results that follow incorporate several solutions to the delay-plant control design problem and a verification of the proposed algorithms in terms of the closed-loop stability

Simple stable discrete-time generalised predictive control with anticipated filtration of control error

It is shown that under some specific conditions, the solution of the generalised predictive control (GPC) design using the concept of anticipated filtering (AF) of the control error always exists, and that such a design leads to stable control systems with definite closed-loop characteristics. The plant cancellation issue is taken into account, and it is demonstrated that certain bounds on GPC design parameters have to be considered. An iterative procedure for simultaneous determination of the three basic design-tuning parameters: the control horizon, the controller gain, and the order of plant cancellation, is also supplied. An important feature of this approach is that the anticipated filtering makes it possible to reduce a disagreeable control effort associated with GPC and to make the [lambda]-tuning mechanism practicable. The bounds on the GPC design parameters are discussed, and certain optimal tuning rules are proposed and validated via simulated experiments

Analytical Design of Stable Continuous-Time Generalised Predictive Control

With a recently renewed interest in the continuous-time approach to control system design the continuous-time generalised predictive control (CGPC) is also worth considering. The main objective of this presentation is the development of an analytical perspective that results in explicit design procedures for stable control of both minimum-phase and non-minimum-phase SISO systems. The basic project idea is founded on a set of closed-loop prototype characteristics with definite time-domain specifications

Discrete-time Predictive Control with Overparameterized Delay-plant Models and an Identified Cancellation Order

The paper presents several solutions to the discrete-time generalized predictive (GPC) controller problem, including an anticipative filtration mechanism, which are suitable for plants with nonzero transportation delays. Necessary modifications of the GPC design procedure required for controlling plants based on their non-minimal models are discussed in detail. Although inevitably invoking the troublesome pole-zero cancellation problem, such models can be used in adaptive systems as a remedy for the uncertainty or variability of the model order. The purpose of this paper is to present a complete GPC controller design for delay plants that is robust to the overparameterization of the plant model. Refined conditions for the existence and stability of GPC control solutions in terms of pertinent design parameters are given, and explicit forms of closed-loop characteristic polynomials are provided. The issue of identifying the model cancellation order is also considered, and practical solutions are proposed. The presented ideas are illustrated numerically

Loudness Scaling Test Based on Categorical Perception

The main goal of this research study is focused on creating a method for loudness scaling based on categorical perception. Its main features, such as: way of testing, calibration procedure for securing reliable results, employing natural test stimuli, etc., are described in the paper and assessed against a procedurę that uses 1/2-octave bands of noise (LGOB) for the loudness growth estimation. The Mann-Whitney U-test is employed to check whether the proposed method is statistically equivalent to LGOB. It is shown that loudness functions obtained in both methods are similar in the statistical context. Moreover, the band-filtered musical instrument signals are experienced as more pleasant than the narrow-band noise stimuli and the proposed test is performed in a shorter time. The method proposed may be incorporated into fitting hearing strategies or used for checking individual loudness growth functions and adapting them to the comfort level settings while listening to music

Evolutionary Multi-Objective Pareto Optimisation of Diagnostic State Observers

A multi-objective Pareto-optimisation procedure for the design of residual generators which constitute a primary instrument for model-based fault detection and isolation (FDI) in systems of plant monitoring and control is considered. An evolutionary approach to the underlying multi-objective optimisation problem is utilised. The resulting robust observer detector allows for FDI, taking into account the issue of false alarms