Design of a minimum variance multiple input-multiple output neuro self-tuning proportional-integral-derivative controller for non-linear dynamic systems

In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.

Electronic energy transfer in a dinuclear Ru/Os complex containing a photoresponsive dithienylethene derivative as bridging ligand

In order to build up a multicomponent system able to perform useful light-induced functions, a dithienylethene-bridged heterodinuclear metal complex (Ru/Os) has been prepared. The compound was characterized and its photophysical properties studied in detail.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

Eliminativism about derivative prima facie duties

Ross divides prima facie duties into derivative and foundational ones, but seems to understand the notion of a derivative prima facie duty in two very different ways. Sometimes he understands them in a non-eliminativist way. According to this understanding, basic prima facie duties ground distinct derivative ones. According to the eliminativist understanding, basic duties do not ground distinct derivative duties, but replace (eliminate) them. On the eliminativist view, discovering that a prima facie duty is derivative is discovering that it is not genuine. The genuine one is the basic one. I argue that Ross is best understood as an eliminativist.

The synthesis of dicobalt and dinickel complexes of trimethylsilylethynyl-1, 2-dihydrofullerene; characterisation by n.m.r. and structure of the first acyclic metal fullerene derivative: molecular structure of [η2-{2-H-1-(Me3SiC ≡ C)-C60}Ni2(η-C5H5)2]

The synthesis and characterisation of the complexes [η2-{2-H-1-(Me3SiC ≡ C)-C60}Co2(CO)6] (2)} and [η-2-{2-H-1-(Me3SiC ≡ C)-C60}Ni2η-C5H5)2] (3)} is reported, together with a single-crystal molecular structure for (3). This provides the first structural data for an acyclic metal derivative of [60]-fullerene.

Regularization of Descriptor Systems by Derivative and Proportional State Feedback

For linear multivariable time-invariant continuous or discrete-time singular systems it is customary to use a proportional feedback control in order to achieve a desired closed loop behaviour. Derivative feedback is rarely considered. This paper examines how derivative feedback in descriptor systems can be used to alter the structure of the system pencil under various controllability conditions. It is shown that derivative and proportional feedback controls can be constructed such that the closed loop system has a given form and is also regular and has index at most 1. This property ensures the solvability of the resulting system of dynamic-algebraic equations. The construction procedures used to establish the theory are based only on orthogonal matrix decompositions and can therefore be implemented in a numerically stable way. The problem of pole placement with derivative feedback alone and in combination with proportional state feedback is also investigated. A computational algorithm for improving the “conditioning” of the regularized closed loop system is derived.

Synthesis, characterization, crystal structure, and DNA-binding of ruthenium(II) complexes of heterocyclic nitrogen ligands resulting from a benzimidazole-based quinazoline derivative

The reaction of cis-[RuCl2(dmso)(4)] with [6-(2-pyridinyl)-5,6-dihydrobenzimidazo[1,2-c] quinazoline] (L) afforded in pure form a blue ruthenium(II) complex, [Ru(L-1)(2)] (1), where the original L changed to [2-(1H-benzoimidazol-2-yl)-phenyl]-pyridin-2-ylmethylene-amine (HL1). Treatment of RuCl3 center dot 3H(2)O with L in dry tetrahydrofuran in inert atmosphere led to a green ruthenium(II) complex, trans-[RuCl2(L-2)(2)] (2), where L was oxidized in situ to the neutral species 6-pyridin-yl-benzo[4,5]imidazo[1,2-c] quinazoline (L-2). Complex 2 was also obtained from the reaction of RuCl3 center dot 3H(2)O with L-2 in dry ethanol. Complexes 1 and 2 have been characterized by physico-chemical and spectroscopic tools, and 1 has been structurally characterized by single-crystal X-ray crystallography. The electrochemical behavior of the complexes shows the Ru(III)/Ru(II) couple at different potentials with quasi-reversible voltammograms. The interaction of these complexes with calf thymus DNA by using absorption and emission spectral studies allowed determination of the binding constant K-b and the linear Stern-Volmer quenching constant K-SV

The domain derivative in rough-surface scattering and rigorous estimates for first-order perturbation theory

We investigate Fréchet differentiability of the scattered field with respect to variation in the boundary in the case of time–harmonic acoustic scattering by an unbounded, sound–soft, one–dimensional rough surface. We rigorously prove the differentiability of the scattered field and derive a characterization of the Fréchet derivative as the solution to a Dirichlet boundary value problem. As an application of these results we give rigorous error estimates for first–order perturbation theory, justifying small perturbation methods that have a long history in the engineering literature. As an application of our rigorous estimates we show that a plane acoustic wave incident on a sound–soft rough surface can produce an unbounded scattered field.

Futures trading, spot price volatility and market efficiency: evidence from European real estate securities futures

In 2007 futures contracts were introduced based upon the listed real estate market in Europe. Following their launch they have received increasing attention from property investors, however, few studies have considered the impact their introduction has had. This study considers two key elements. Firstly, a traditional Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, the approach of Bessembinder & Seguin (1992) and the Gray’s (1996) Markov-switching-GARCH model are used to examine the impact of futures trading on the European real estate securities market. The results show that futures trading did not destabilize the underlying listed market. Importantly, the results also reveal that the introduction of a futures market has improved the speed and quality of information flowing to the spot market. Secondly, we assess the hedging effectiveness of the contracts using two alternative strategies (naïve and Ordinary Least Squares models). The empirical results also show that the contracts are effective hedging instruments, leading to a reduction in risk of 64 %.

Low-frequency volatility of real estate securities in relation to macroeconomic risk

Real estate securities have a number of distinct characteristics that differentiate them from stocks generally. Key amongst them is that under-pinning the firms are both real as well as investment assets. The connections between the underlying macro-economy and listed real estate firms is therefore clearly demonstrated and of heightened importance. To consider the linkages with the underlying macro-economic fundamentals we extract the ‘low-frequency’ volatility component from aggregate volatility shocks in 11 international markets over the 1990-2014 period. This is achieved using Engle and Rangel’s (2008) Spline-Generalized Autoregressive Conditional Heteroskedasticity (Spline-GARCH) model. The estimated low-frequency volatility is then examined together with low-frequency macro data in a fixed-effect pooled regression framework. The analysis reveals that the low-frequency volatility of real estate securities has strong and positive association with most of the macroeconomic risk proxies examined. These include interest rates, inflation, GDP and foreign exchange rates.