Linear quadratic game and non-cooperative predictive methods for potential application to modelling driver-AFS interactive steering control

This paper is concerned with the modelling of strategic interactions between the human driver and the vehicle active front steering (AFS) controller in a path-following task where the two controllers hold different target paths. The work is aimed at extending the use of mathematical models in representing driver steering behaviour in complicated driving situations. Two game theoretic approaches, namely linear quadratic game and non-cooperative model predictive control (non-cooperative MPC), are used for developing the driver-AFS interactive steering control model. For each approach, the open-loop Nash steering control solution is derived; the influences of the path-following weights, preview and control horizons, driver time delay and arm neuromuscular system (NMS) dynamics are investigated, and the CPU time consumed is recorded. It is found that the two approaches give identical time histories as well as control gains, while the non-cooperative MPC method uses much less CPU time. Specifically, it is observed that the introduction of weight on the integral of vehicle lateral displacement error helps to eliminate the steady-state path-following error; the increase in preview horizon and NMS natural frequency and the decline in time delay and NMS damping ratio improve the path-following accuracy. © 2013 Copyright Taylor and Francis Group, LLC.

Absence of branches from xylan in Arabidopsis gux mutants reveals potential for simplification of lignocellulosic biomass.

As one of the most abundant polysaccharides on Earth, xylan will provide more than a third of the sugars for lignocellulosic biofuel production when using grass or hardwood feedstocks. Xylan is characterized by a linear β(1,4)-linked backbone of xylosyl residues substituted by glucuronic acid, 4-O-methylglucuronic acid or arabinose, depending on plant species and cell types. The biological role of these decorations is unclear, but they have a major influence on the properties of the polysaccharide. Despite the recent isolation of several mutants with reduced backbone, the mechanisms of xylan synthesis and substitution are unclear. We identified two Golgi-localized putative glycosyltransferases, GlucUronic acid substitution of Xylan (GUX)-1 and GUX2 that are required for the addition of both glucuronic acid and 4-O-methylglucuronic acid branches to xylan in Arabidopsis stem cell walls. The gux1 gux2 double mutants show loss of xylan glucuronyltransferase activity and lack almost all detectable xylan substitution. Unexpectedly, they show no change in xylan backbone quantity, indicating that backbone synthesis and substitution can be uncoupled. Although the stems are weakened, the xylem vessels are not collapsed, and the plants grow to normal size. The xylan in these plants shows improved extractability from the cell wall, is composed of a single monosaccharide, and requires fewer enzymes for complete hydrolysis. These findings have implications for our understanding of the synthesis and function of xylan in plants. The results also demonstrate the potential for manipulating and simplifying the structure of xylan to improve the properties of lignocellulose for bioenergy and other uses.

Increasing the open-circuit voltage of photoprotein-based photoelectrochemical cells by manipulation of the vacuum potential of the electrolytes.

The innately highly efficient light-powered separation of charge that underpins natural photosynthesis can be exploited for applications in photoelectrochemistry by coupling nanoscale protein photoreaction centers to man-made electrodes. Planar photoelectrochemical cells employing purple bacterial reaction centers have been constructed that produce a direct current under continuous illumination and an alternating current in response to discontinuous illumination. The present work explored the basis of the open-circuit voltage (V(OC)) produced by such cells with reaction center/antenna (RC-LH1) proteins as the photovoltaic component. It was established that an up to ~30-fold increase in V(OC) could be achieved by simple manipulation of the electrolyte connecting the protein to the counter electrode, with an approximately linear relationship being observed between the vacuum potential of the electrolyte and the resulting V(OC). We conclude that the V(OC) of such a cell is dependent on the potential difference between the electrolyte and the photo-oxidized bacteriochlorophylls in the reaction center. The steady-state short-circuit current (J(SC)) obtained under continuous illumination also varied with different electrolytes by a factor of ~6-fold. The findings demonstrate a simple way to boost the voltage output of such protein-based cells into the hundreds of millivolts range typical of dye-sensitized and polymer-blend solar cells, while maintaining or improving the J(SC). Possible strategies for further increasing the V(OC) of such protein-based photoelectrochemical cells through protein engineering are discussed.

Gaussian Approximation Potential: an interatomic potential derived from first principles Quantum Mechanics

Simulation of materials at the atomistic level is an important tool in studying microscopic structure and processes. The atomic interactions necessary for the simulation are correctly described by Quantum Mechanics. However, the computational resources required to solve the quantum mechanical equations limits the use of Quantum Mechanics at most to a few hundreds of atoms and only to a small fraction of the available configurational space. This thesis presents the results of my research on the development of a new interatomic potential generation scheme, which we refer to as Gaussian Approximation Potentials. In our framework, the quantum mechanical potential energy surface is interpolated between a set of predetermined values at different points in atomic configurational space by a non-linear, non-parametric regression method, the Gaussian Process. To perform the fitting, we represent the atomic environments by the bispectrum, which is invariant to permutations of the atoms in the neighbourhood and to global rotations. The result is a general scheme, that allows one to generate interatomic potentials based on arbitrary quantum mechanical data. We built a series of Gaussian Approximation Potentials using data obtained from Density Functional Theory and tested the capabilities of the method. We showed that our models reproduce the quantum mechanical potential energy surface remarkably well for the group IV semiconductors, iron and gallium nitride. Our potentials, while maintaining quantum mechanical accuracy, are several orders of magnitude faster than Quantum Mechanical methods.

First principles interatomic potential for tungsten based on Gaussian process regression

An accurate description of atomic interactions, such as that provided by first principles quantum mechanics, is fundamental to realistic prediction of the properties that govern plasticity, fracture or crack propagation in metals. However, the computational complexity associated with modern schemes explicitly based on quantum mechanics limits their applications to systems of a few hundreds of atoms at most. This thesis investigates the application of the Gaussian Approximation Potential (GAP) scheme to atomistic modelling of tungsten - a bcc transition metal which exhibits a brittle-to-ductile transition and whose plasticity behaviour is controlled by the properties of $\frac{1}{2} \langle 111 \rangle$ screw dislocations. We apply Gaussian process regression to interpolate the quantum-mechanical (QM) potential energy surface from a set of points in atomic configuration space. Our training data is based on QM information that is computed directly using density functional theory (DFT). To perform the fitting, we represent atomic environments using a set of rotationally, permutationally and reflection invariant parameters which act as the independent variables in our equations of non-parametric, non-linear regression. We develop a protocol for generating GAP models capable of describing lattice defects in metals by building a series of interatomic potentials for tungsten. We then demonstrate that a GAP potential based on a Smooth Overlap of Atomic Positions (SOAP) covariance function provides a description of the $\frac{1}{2} \langle 111 \rangle$ screw dislocation that is in agreement with the DFT model. We use this potential to simulate the mobility of $\frac{1}{2} \langle 111 \rangle$ screw dislocations by computing the Peierls barrier and model dislocation-vacancy interactions to QM accuracy in a system containing more than 100,000 atoms.