Adaptive fast finite time control of a class of uncertain nonlinear systems

This paper concerns the adaptive fast finite time control of a class of nonlinear uncertain systems of which the upper bounds of the system uncertainties are unknown. By using the fast non-smooth control Lyapunov function and the method of so-called adding a power integrator merging with adaptive technique, a recursive design procedure is provided, which guarantees the fast finite time stability of the closed-loop system. It is proved that the control input is bounded, and a simulation example is given to illustrate the effectiveness of the theoretical results.

Identification and characterisation of novel genes involved in skeletal muscle hypertrophy

There is mounting evidence in support of the view that skeletal muscle hypertrophy results from the complex and coordinated interaction of numerous signalling pathways. Well characterised components integral to skeletal muscle adaptation include the transcriptional activity of the members of the myogenic regulatory factors, numerous secreted peptide growth factors, and the regenerative potential of satellite cells. Whilst studies investigating isolated components or pathways have enhanced our current understanding of skeletal muscle hypertrophy, our knowledge of how all of these components react in concert to a common stimulus remains limited. The broad aim of this thesis was to identify and characterise novel genes involved in skeletal muscle hypertrophy. We have created a customised human skeletal muscle specific microarray which contains ∼11,000 cDNA clones derived from a normalised human skeletal muscle cDNA library as well as 270 genes with known functional roles in human skeletal muscle. The first aspect of this thesis describes the production of the microarray and evaluates the robustness and reproducibility of this analytical technique. Study one aimed to use this microarray in the identification of genes that are differentially expressed during the forced differentiation of human rhabdomyosarcoma cells, an in vitro model of skeletal muscle development. Firstly using this unique model of aberrant myogenic differentiation we aimed to identify genes with previously unidentified roles in myogenesis. Secondly, the data from this study permitted the examination of the performance of the microarray in detecting differential gene expression in a biological system. We identified several new genes with potential roles in the myogenic arrest of rhabdomyosarcoma and further characterised the expression of muscle specific genes in rhabdomyosarcoma differentiation. In study two, the molecular responses of cell cycle regulators, muscle regulatory factors, and atrophy related genes were mapped in response to a single bout of resistance exercise in human skeletal muscle. We demonstrated an increased expression of MyoD, myogenin and p21, whilst the expression of myostatin was decreased. The results of this study contribute to the existing body of knowledge on the molecular regulation skeletal muscle to a hypertrophic stimulus. In study three, the muscle samples collected in study two were analysed using the human skeletal muscle specific microarray for the identification of novel genes with potential roles in the hypertrophic process. The analysis uncovered four interesting genes (TXNIP, MLP, ASB5, FLJ 38973) that have not previously been examined in human skeletal muscle in response to resistance exercise. The functions of these genes and their potential roles in skeletal muscle are discussed. In study four, the four genes identified in study three were examined in human primary skeletal muscle cell cultures during myogenic differentiation. Human primary skeletal muscle cells were derived from the vastus lateralis muscle of 8 healthy volunteers (6 males and 2 females). Cell cultures were differentiated using serum withdrawal and serum withdrawal combined with IGF-1 supplementation. Markers of the cell proliferation, cell cycle arrest and myogenic differentiation were examined to assess the effectiveness of the differentiation stimulus. Additionally, the expressions of TXNIP, MLP, ASB5 and FLJ 38973 measured in an attempt to characterise further their roles in skeletal muscle. The expression of TXNIP changed markedly in response to both differentiation stimuli, whilst the expression of the remaining genes were not altered. Therefore it was suggested that expression of these genes might be responsive to the mechanical strain or contraction induced by the resistance exercise. In order to examine whether these novel genes responded specifically to resistance type exercise, their expression was examined following a single bout of endurance exercise. The expression of TXNIP, MLP, and FLJ 38973 remained unchanged whilst ASB5 increased 30 min following the cessation of the exercise.

Identification of nonlinear systems using hybrid functions

Most real systems have nonlinear behavior and thus model linearization may not produce an accurate representation of them. This paper presents a method based on hybrid functions to identify the parameters of nonlinear real systems. A hybrid function is a combination of two groups of orthogonal functions: piecewise orthogonal functions (e.g. Block-Pulse) and continuous orthogonal functions (e.g. Legendre polynomials). These functions are completed with an operational matrix of integration and a product matrix. Therefore, it is possible to convert nonlinear differential and integration equations into algebraic equations. After mathematical manipulation, the unknown linear and nonlinear parameters are identified. As an example, a mechanical system with single degree of freedom is simulated using the proposed method and the results are compared against those of an existing approach.

Optimal actuator fault tolerance for static nonlinear systems based on minimum output velocity jump

Fault tolerance for a class of non linear systems is addressed based on the velocity of their output variables. This paper presents a mapping to minimize the possible jump of the velocity of the output, due to the actuator failure. The failure of the actuator is assumed as actuator lock. The mapping is derived and it provides the proper input commands for the healthy actuators of the system to tolerate the effect of the faulty actuator on the output of the system. The introduced mapping works as an optimal input reconfiguration for fault recovery, which provides a minimum velocity jump suitable for static nonlinear systems. The proposed mapping is validated through different case studies and a complementary simulation. In the case studies and the simulation, the mapping provides the commands to compensate the effect of different faults within the joints of a robotic manipulator. The new commands and the compare between the velocity of the output variables for the health and faulty system are presented.

The last piece in the cellulase puzzle : the characterisation of β-glucosidase from the herbivorous gecarcinid land crab Gecarcoidea natalis

A 160 kDa enzyme with β-glucosidase activity was purified from the midgut Gland of the land crab Gecarcoidea natalis. The enzyme was capable of releasing glucose progressively from cellobiose, cellotriose or cellotetraose. Although β-glucosidases (EC 3.2.1.21) have some activity towards substrates longer than cellobiose, the enzyme was classified as a glucohydrolase (EC 3.2.1.74) as it had a preference for larger substrates (cellobiose<cellotriose=cellotetraose). It was able to synthesise some cellotetraose by the transglycosylation of smaller substrates – another common feature of glucohydrolases. The interaction between the glucohydrolase described here and the endo-β-1,4-glucanases described previously for G. natalis provides a complete model for cellulose hydrolysis in crustaceans and possibly in other invertebrates. After mechanical fragmentation by the gastric mill, multiple endo-β-1,4-glucanases would initially cleave β-1,4-glycosidic bonds within native cellulose, releasing small oligomers, including cellobiose, cellotriose and cellotetraose. The glucohydrolase would then attach to these oligomers, progressively releasing glucose. The glucohydrolase might also attach directly to crystalline cellulose to release glucose from free chain ends. This two-enzyme system differs from the traditional model, which suggests that total cellulose hydrolysis requires the presence an endo-β-1,4-glucanse, a cellobiohydrolase and a β-glucosidase

Gold-nanorod-facilitated nonlinear optical microscopy under radially polarised beam illumination

Reports on the use of radially polarised beam in gold-nanorod-facilitated nonlinear microscopy and therapy. It has been found that the use of radially polarised beam can greatly reduce the energy fluence threshold for treating cancer cells labelled with gold nanorods. The slight distortion in the polarisation properties of the radially polarised beam after propagating through double-clad photonic crystal fibres makes it promising in the application of fibre-optic based endoscopic system.

Microtexture characterisation of electrodeposited nanocrystalline nickel

This data includes transmission electron microscopy orientation maps of the received electrodeposited nanocrystalline nickel. The data was obtained using the Nanomegas Digitstar System which is currently the only equipment of this type available in Australia. The data has been acquired with steps in nanometre scale and enables the determination of local microtexture of the specimens.

DSP-based sliding-mode control for electromagnetic-levitation precise position system

This paper investigates the robust tracking control problem for a bipolar electromagnetic-levitation precise-position system. The dynamic model of the precise-position device is derived by conducting a thorough analysis on the nonlinear electromagnetic forces. Conventional sliding-mode control and terminal sliding-mode control strategies are developed to guarantee asymptotic and finite-time tracking capabilities of the closed-loop system. A lumped uncertainty estimator is proposed to estimate the system uncertainties. The estimated information is then used to construct a smooth uniformly ultimately bounded sliding-mode control. An exact estimator is also proposed to exactly estimate the unknown uncertainties in finite time. The output of the exact estimator is used to design a continuous chattering free terminal sliding-mode control. The time taken for the closed-loop system to reach zero tracking error is proven to be finite. Experiment results are presented, using a real time digital-signal-processor (DSP) based electromagnetic-levitation system to validate the analysis.

Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method

Hemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem.

A robust learning control for SISO Nonlinear systems with T-S Fuzzy Model : C02-robust control

In this paper, a robust learning control is developed for a class of single input single output (SISO) nonlinear systems with T-S fuzzy model. It is seen that the proposed sliding mode learning control with the powerful Lipshitz-like condition can guarantee the stability, convergence and robustness of the closed-loop system without involving any assumptions on uncertain system dynamics. In addition, theconcept that the local system with the maximum membership function dominates the system dynamic behaviours helps to greatly simplify the control system design. It will be further seen that the continuous learning control ensures the advantage of chattering-free that may occur in conventional sliding mode systems. Simulation examples are presented to demonstrate the effectiveness of the proposed learning control through the comparison with the H-infinity control.

Observer-based control for time-varying delay neural networks with nonlinear observation

This paper studies the problem of designing observer-based controllers for a class of delayed neural networks with nonlinear observation. The system under consideration is subject to nonlinear observation and an interval time-varying delay. The nonlinear observation output is any nonlinear Lipschitzian function and the time-varying delay is not required to be differentiable nor its lower bound be zero. By constructing a set of appropriate Lyapunov-Krasovskii functionals and utilizing the Newton-Leibniz formula, some delay-dependent stabilizability conditions which are expressed in terms of Linear Matrix Inequalities (LMIs) are derived. The derived conditions allow simultaneous computation of two bounds that characterize the exponential stability rate of the closed-loop system. The unknown observer gain and the state feedback observer-based controller are directly obtained upon the feasibility of the derived LMIs stabilizability conditions. A simulation example is presented to verify the effectiveness of the proposed result.

An illumination invariant face recognition system for access control using video

Illumination and pose invariance are the most challenging aspects of face recognition. In this paper we describe a fully automatic face recognition system that uses video information to achieve illumination and pose robustness. In the proposed method, highly nonlinear manifolds of face motion are approximated using three Gaussian pose clusters. Pose robustness is achieved by comparing the corresponding pose clusters and probabilistically combining the results to derive a measure of similarity between two manifolds. Illumination is normalized on a per-pose basis. Region-based gamma intensity correction is used to correct for coarse illumination changes, while further refinement is achieved by combining a learnt linear manifold of illumination variation with constraints on face pattern distribution, derived from video. Comparative experimental evaluation is presented and the proposed method is shown to greatly outperform state-of-the-art algorithms. Consistent recognition rates of 94-100% are achieved across dramatic changes in illumination.

Characterisation of an ancient S100 protein with multiple functions

 This project focused on the novel S100A19 protein, expressed exclusively in marsupials and monotremes, identifying it as an important component of the innate immune system. Data showed that S100A19 is differentially regulated in the pouch and mammary gland of the wallaby to protect the infant when most susceptible to infection.

Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV-Environment-Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems. © 2014 Elsevier Ltd.

Nonlinear dynamic modeling of ionic polymer conductive network composite actuators using rigid finite element method

Ionic polymer conductive network composite (IPCNC) actuators are a class of electroactive polymer composites that exhibit some interesting electromechanical characteristics such as low voltage actuation, large displacements, and benefit from low density and elastic modulus. Thus, these emerging materials have potential applications in biomimetic and biomedical devices. Whereas significant efforts have been directed toward the development of IPMC actuators, the establishment of a proper mathematical model that could effectively predict the actuators' dynamic behavior is still a key challenge. This paper presents development of an effective modeling strategy for dynamic analysis of IPCNC actuators undergoing large bending deformations. The proposed model is composed of two parts, namely electrical and mechanical dynamic models. The electrical model describes the actuator as a resistive-capacitive (RC) transmission line, whereas the mechanical model describes the actuator as a system of rigid links connected by spring-damping elements. The proposed modeling approach is validated by experimental data, and the results are discussed. © 2014 Elsevier B.V. All rights reserved.