Optimal control of nonlinear differential algebraic equation systems

A novel iterative procedure is described for solving nonlinear optimal control problems subject to differential algebraic equations. The procedure iterates on an integrated modified linear quadratic model based problem with parameter updating in such a manner that the correct solution of the original non-linear problem is achieved. The resulting algorithm has a particular advantage in that the solution is achieved without the need to solve the differential algebraic equations . Convergence aspects are discussed and a simulation example is described which illustrates the performance of the technique. 1. Introduction When modelling industrial processes often the resulting equations consist of coupled differential and algebraic equations (DAEs). In many situations these equations are nonlinear and cannot readily be directly reduced to ordinary differential equations.

Velocity-based moving mesh methods for nonlinear partial differential equations

This article describes a number of velocity-based moving mesh numerical methods formultidimensional nonlinear time-dependent partial differential equations (PDEs). It consists of a short historical review followed by a detailed description of a recently developed multidimensional moving mesh finite element method based on conservation. Finite element algorithms are derived for both mass-conserving and non mass-conserving problems, and results shown for a number of multidimensional nonlinear test problems, including the second order porous medium equation and the fourth order thin film equation as well as a two-phase problem. Further applications and extensions are referenced.

Inverting recurrent neural networks for internal model control of nonlinear systems

In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.

Internal model control of nonlinear systems through the inversion of recurrent neural networks

Recurrent neural networks can be used for both the identification and control of nonlinear systems. This paper takes a previously derived set of theoretical results about recurrent neural networks and applies them to the task of providing internal model control for a nonlinear plant. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant, without the need to train an additional network to perform the inverse control.

Identification of nonlinear systems with a dynamic recurrent neural network

Two approaches are presented to calculate the weights for a Dynamic Recurrent Neural Network (DRNN) in order to identify the input-output dynamics of a class of nonlinear systems. The number of states of the identified network is constrained to be the same as the number of states of the plant.

Electron spin resonance study of puff-resolved free radical formation in mainstream cigarette smoke

Puff-by-puff resolved gas phase free radicals were measured in mainstream smoke from Kentucky 2R4F reference cigarettes using ESR spectroscopy. Three spin-trapping reagents were evaluated: PBN, DMPO and DEPMPO. Two procedures were used to collect gas phase smoke on a puff-resolved basis: i) the accumulative mode, in which all the gas phase smoke up to a particular puff was bubbled into the trap (i.e., the 5th puff corresponded to the total smoke from the 1st to 5th puffs). In this case, after a specified puff, an aliquot of the spin trap was taken and analysed; or, ii) the individual mode, in which the spin trap was analysed and then replaced after each puff. Spin concentrations were determined by double-integration of the first derivative of the ESR signal. This was compared with the integrals of known standards using the TEMPO free radical. The radicals trapped with PBN were mainly carbon-centred, whilst the oxygen-centred radicals were identified with DMPO and DEPMPO. With each spin trap, the puff-resolved radical concentrations showed a characteristic pattern as a function of the puff number. Based on the spin concentrations, the DMPO and DEPMPO spin traps showed better trapping efficiencies than PBN. The implication for gas phase free radical analysis is that a range of different spin traps should be used to probe complex free radical reactions in cigarette smoke.

Electron paramagnetic resonance investigation of the free radicals in the gas and particulate phases of cigarette smoke using spin-trapping techniques

Free radicals in cigarette smoke have been studied using spin trapping EPR techniques. 2R4F reference cigarettes were smoked using 35 ml puff volumes of 2 seconds duration, once every 60 seconds. The particulate phase of the smoke was separated from the gas phase by passing the smoke through a Cambridge filter pad. For both phases, free radicals were measured and identified. A range of spin-traps was employed: PBN, DMPO, DEPMPO, and DPPH-PBN. In the gas-phase, short-lived carbon- and oxygen- centered radicals were identified; the ratios between them changed during the smoking runs. For the first puffs, C-centered radicals predominated while for the later puffs, O-centered radicals were mainly observed. The particulate phase and the ‘tar’ were studied as well.

Turning on the alarm: the neural mechanisms of the transition from innocuous to painful sensation

The experience of pain occurs when the level of a stimulus is sufficient to elicit a marked affective response, putatively to warn the organism of potential danger and motivate appropriate behavioral responses. Understanding the biological mechanisms of the transition from innocuous to painful levels of sensation is essential to understanding pain perception as well as clinical conditions characterized by abnormal relationships between stimulation and pain response. Thus, the primary objective of this study was to characterize the neural response associated with this transition and the correspondence between that response and subjective reports of pain. Towards this goal, this study examined BOLD response profiles across a range of temperatures spanning the pain threshold. 14 healthy adults underwent functional magnetic resonance imaging (fMRI) while a range of thermal stimuli (44-49oC) were applied. BOLD responses showed a sigmoidal profile along the range of temperatures in a network of brain regions including insula and mid- cingulate, as well as a number of regions associated with motor responses including ventral lateral nuclei of the thalamus, globus pallidus and premotor cortex. A sigmoid function fit to the BOLD responses in these regions explained up to 85% of the variance in individual pain ratings, and yielded an estimate of the temperature of steepest transition from non-painful to painful heat that was nearly identical to that generated by subjective ratings. These results demonstrate a precise characterization of the relationship between objective levels of stimulation, resulting neural activation, and subjective experience of pain and provide direct evidence for a neural mechanism supporting the nonlinear transition from innocuous to painful levels along the sensory continuum.

A hydrogen-1 nuclear magnetic resonance, mass spectral and extended Hückel study of some olefin complexes of rhodium(I) and iridium(I) and the crystal and molecular structure of η4-2,4-dimethylpenta-1,4-diene(η5-formylcyclopentadienyl)rhodium(I)

A 1H NMR study of monosubstituted η-cyclopentadienyl-rhodium(I) complexes of type LLRh(C5H4X) and -iridium(I) complexes of type L2Ir(C5H4X) (L = ethene, LL = 1,3- or 1,5-diolefin; X = C(C6H5)3, CHO, or COOCH3) has been carried out. For complexes of both metals in which the neutral ligand is ethene or a non-conjugated diolefin the NMR spectra of the cyclopentadienyl protons are unusual in that H(2), H(5) resonate to high field either at room temperature or below. The corresponding NMR spectra for the cyclopentadienyl ring protons of complexes where the neutral ligand is a conjugated diene are, with one exception, normal. A single crystal X-ray structural analysis of (η4-2,4-dimethylpenta-1,4-diene)(η5-formylcyclopentadienyl)rhodium(I) (which exhibits an abnormal 1H NMR spectrum) reveals substantial localisation of electron density in the C(3)C(4) Cp ring bond (1.283(33) Å) which may be consistent with a contribution from an ‘allyl-ene’ rotamer to the ring—metal bonding scheme. An extended Hückel calculation with self consistent charge iteration was performed on this complex. The results predict a greater Mulliken overlap population for the C(3)C(4) bond in the cyclopentadienyl ring and show that the localisation is dependent on both the Cp ring substituent and the nature of the diolefin. The mass spectral fragmentation patterns of some representative diene complexes of iridium(I) and rhodium(I) are presented.

Nonlinear data assimilation in geosciences: an extremely efficient particle filter

Almost all research fields in geosciences use numerical models and observations and combine these using data-assimilation techniques. With ever-increasing resolution and complexity, the numerical models tend to be highly nonlinear and also observations become more complicated and their relation to the models more nonlinear. Standard data-assimilation techniques like (ensemble) Kalman filters and variational methods like 4D-Var rely on linearizations and are likely to fail in one way or another. Nonlinear data-assimilation techniques are available, but are only efficient for small-dimensional problems, hampered by the so-called ‘curse of dimensionality’. Here we present a fully nonlinear particle filter that can be applied to higher dimensional problems by exploiting the freedom of the proposal density inherent in particle filtering. The method is illustrated for the three-dimensional Lorenz model using three particles and the much more complex 40-dimensional Lorenz model using 20 particles. By also applying the method to the 1000-dimensional Lorenz model, again using only 20 particles, we demonstrate the strong scale-invariance of the method, leading to the optimistic conjecture that the method is applicable to realistic geophysical problems. Copyright c 2010 Royal Meteorological Society