Model car-exhaust catalyst studied by TPD and TP-RAIRS: Surface reactions of NO on clean and O-covered Ir{100}

The adsorption of NO on Ir{100} has been studied as a function of NO coverage and temperature using temperature programmed reflection absorption infrared spectroscopy (TP-RAIRS), low energy electron diffraction (LEED) and temperature programmed desorption (TPD). After saturating the clean (1 x 5)-reconstructed surface with NO at 95 K. two N-2, desorption peaks are observed upon heating. The first N-2 peak at 346 K results from the decomposition of bridge-bonded NO, and the second at 475 K from the decomposition of atop-bonded NO molecules. NO decomposition is proposed to be the rate limiting step for both N-2 desorption states. For high NO coverages on the (1 x 5) surface, the narrow width of the first N-2 desorption peak is indicative of an autocatalytic process for which the parallel formation of N2O appears to be the crucial step. When NO is adsorbed on the metastable unreconstructed (1 x 1) phase of clean Ir{100} N-2 desorption starts at lower temperatures, indicating that this surface modification is more reactive. When a high coverage of oxygen, near 0.5 ML, is pre-adsorbed on the surface, the decomposition of NO is inhibited and mainly desorption of intact NO is observed.

Spectroscopy of ultrathin epitaxial rutile TiO[sub 2](110) films grown on W(100)

Epitaxial ultrathin titanium dioxide films of 0.3 to similar to 7 nm thickness on a metal single crystal substrate have been investigated by high resolution vibrational and electron spectroscopies. The data complement previous morphological data provided by scanned probe microscopy and low energy electron diffraction to provide very complete characterization of this system. The thicker films display electronic structure consistent with a stoichiometric TiO2 phase. The thinner films appear nonstoichiometric due to band bending and charge transfer from the metal substrate, while work function measurements also show a marked thickness dependence. The vibrational spectroscopy shows three clear phonon bands at 368, 438, and 829 cm(-1) (at 273 K), which confirms a rutile structure. The phonon band intensity scales linearly with film thickness and shift slightly to lower frequencies with increasing temperature, in accord with results for single crystals. (c) 2007 American Institute of Physics.

Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

Filtering techniques in self-tuning

In the last few years a state-space formulation has been introduced into self-tuning control. This has not only allowed for a wider choice of possible control actions, but has also provided an insight into the theory underlying—and hidden by—that used in the polynomial description. This paper considers many of the self-tuning algorithms, both state-space and polynomial, presently in use, and by starting from first principles develops the observers which are, effectively, used in each case. At any specific time instant the state estimator can be regarded as taking one of two forms. In the first case the most recently available output measurement is excluded, and here an optimal and conditionally stable observer is obtained. In the second case the present output signal is included, and here it is shown that although the observer is once again conditionally stable, it is no longer optimal. This result is of significance, as many of the popular self-tuning controllers lie in the second, rather than first, category.

Self-tuning regulators—a state space approach

This paper employs a state space system description to provide a pole placement scheme via state feedback. It is shown that when a recursive least squares estimation scheme is used, the feedback employed can be expressed simply in terms of the estimated system parameters. To complement the state feedback approach, a method employing both state feedback and linear output feedback is discussed. Both methods arc then compared with the previous output polynomial type feedback schemes.

An implicit pole-assignment self-tuning regulator

A new self-tuning implicit pole-assignment algorithm is presented which, through the use of a pole compression factor and different RLS model and control structures, overcomes stability and convergence problems encountered in previously available algorithms. Computational requirements of the technique are much reduced when compared to explicit pole-assignment schemes, whereas the inherent robustness of the strategy is retained.

Auto-weighted minimum-variance self-tuning controller

A self-tuning controller which automatically assigns weightings to control and set-point following is introduced. This discrete-time single-input single-output controller is based on a generalized minimum-variance control strategy. The automatic on-line selection of weightings is very convenient, especially when the system parameters are unknown or slowly varying with respect to time, which is generally considered to be the type of systems for which self-tuning control is useful. This feature also enables the controller to overcome difficulties with non-minimum phase systems.

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.

Automatic tuning of PID controllers using model reference adaptive control techniques

This paper discusses the application of model reference adaptive control concepts to the automatic tuning of PID controllers. The effectiveness of the proposed method is shown through simulated applications. The gradient approach and simulated examples are provided.

Tuning the self-assembly of the bioactive dipeptide L-carnosine by incorporation of a bulky aromatic substituent

The dipeptide L-carnosine has a number of important biological properties. Here, we explore the effect of attachment of a bulky hydrophobic aromatic unit, Fmoc [N-(fluorenyl-9-methoxycarbonyl)] on the self-assembly of Fmoc-L-carnosine, i.e., Fmoc-Beta-alanine-histidine (Fmoc-BetaAH). It is shown that Fmoc-BetaAH forms well-defined amyloid fibril containing Beta sheets above a critical aggregation concentration, which is determined from pyrene and ThT fluorescence experiments. Twisted fibrils were imaged by cryogenic transmission electron microscopy. The zinc-binding properties of Fmoc-BetaAH were investigated by FTIR and Raman spectroscopy since the formation of metal ion complexes with the histidine residue in carnosine is well-known, and important to its biological roles. Observed changes in the spectra may reflect differences in the packing of the Fmoc-dipeptides due to electrostatic interactions. Cryo-TEM shows that this leads to changes in the fibril morphology. Hydrogelation is also induced by addition of an appropriate concentration of zinc ions. Our work shows that the Fmoc motif can be employed to drive the self-assembly of carnosine into amyloid fibrils.