Mathematical modelling and optimal multivariable control of chemical processes

This work reports the developnent of a mathenatical model and distributed, multi variable computer-control for a pilot plant double-effect climbing-film evaporator. A distributed-parameter model of the plant has been developed and the time-domain model transformed into the Laplace domain. The model has been further transformed into an integral domain conforming to an algebraic ring of polynomials, to eliminate the transcendental terms which arise in the Laplace domain due to the distributed nature of the plant model. This has made possible the application of linear control theories to a set of linear-partial differential equations. The models obtained have well tracked the experimental results of the plant. A distributed-computer network has been interfaced with the plant to implement digital controllers in a hierarchical structure. A modern rnultivariable Wiener-Hopf controller has been applled to the plant model. The application has revealed a limitation condition that the plant matrix should be positive-definite along the infinite frequency axis. A new multi variable control theory has emerged fram this study, which avoids the above limitation. The controller has the structure of the modern Wiener-Hopf controller, but with a unique feature enabling a designer to specify the closed-loop poles in advance and to shape the sensitivity matrix as required. In this way, the method treats directly the interaction problems found in the chemical processes with good tracking and regulation performances. Though the ability of the analytical design methods to determine once and for all whether a given set of specifications can be met is one of its chief advantages over the conventional trial-and-error design procedures. However, one disadvantage that offsets to some degree the enormous advantages is the relatively complicated algebra that must be employed in working out all but the simplest problem. Mathematical algorithms and computer software have been developed to treat some of the mathematical operations defined over the integral domain, such as matrix fraction description, spectral factorization, the Bezout identity, and the general manipulation of polynomial matrices. Hence, the design problems of Wiener-Hopf type of controllers and other similar algebraic design methods can be easily solved.

Neural network enhanced self tuning adaptive control application for non-linear control of dynamic systems

The main theme of research of this project concerns the study of neutral networks to control uncertain and non-linear control systems. This involves the control of continuous time, discrete time, hybrid and stochastic systems with input, state or output constraints by ensuring good performances. A great part of this project is devoted to the opening of frontiers between several mathematical and engineering approaches in order to tackle complex but very common non-linear control problems. The objectives are: 1. Design and develop procedures for neutral network enhanced self-tuning adaptive non-linear control systems; 2. To design, as a general procedure, neural network generalised minimum variance self-tuning controller for non-linear dynamic plants (Integration of neural network mapping with generalised minimum variance self-tuning controller strategies); 3. To develop a software package to evaluate control system performances using Matlab, Simulink and Neural Network toolbox. An adaptive control algorithm utilising a recurrent network as a model of a partial unknown non-linear plant with unmeasurable state is proposed. Appropriately, it appears that structured recurrent neural networks can provide conveniently parameterised dynamic models for many non-linear systems for use in adaptive control. Properties of static neural networks, which enabled successful design of stable adaptive control in the state feedback case, are also identified. A survey of the existing results is presented which puts them in a systematic framework showing their relation to classical self-tuning adaptive control application of neural control to a SISO/MIMO control. Simulation results demonstrate that the self-tuning design methods may be practically applicable to a reasonably large class of unknown linear and non-linear dynamic control systems.

Diabetic patient education and motivation. Available in 2 volumes.

Diabetes mellitus is a condition which requires a high degree of patient cooperation in self-management to achieve optimal glycaemic control. The concept of patient education, to enhance the treatment and management of diabetes, is well recognised. Several diabetes education programmes have already been described, but increased knowledge of diabetes did not necessarily result in improved self-mangement or glycaemic control. Other factors, such as attitudes and motivations, may therefore be particuarly important. The aims of the present study were to investigate the influence of patients' attitudes to diabetes, and to develop motivational aspects which enable the application of knowledge to enhance self-management and compliance with treatment. Thirty-one insulin-dependent diabetic (IDD) patients entered into a 12 month educational programme, particularly designed to increase motivation. Patients' attitudes to diabetes, their knowledge and self-management skills were assessed using questionnaires and practical tests, and parameters of glycaemic control were measured. The progress of these patients was compared at intervals with a close matched group of 25 control IFF patients who continued to receive routine clinic care. Patients completing the educational programme achieved better glycaemic control (p< 0.05), greater knowledge (p< 0.001), more favourable attitudes (p< 0.03) and increased competence in management skills (p< 0.02) compared with the control group. Evaluation procedures indicated that the programme was acceptable to the patients, and was successful in terms of increasing patient motivation. Six months after completion of the programme, glycaemic control deteriorated, although knowledge, attitudes and management skills were unchanged. This might reflect the withdrawal of extrinsic motivation, attention and supervision provided during the programme. It is recommended that consideration be given to the development of patients' intrinsic motivation to achieve maximum benefit from diabetes education programmes.

Wireless power charger for light electric vehicles

Wireless power transmission technology is gaining more and more attentions in city transportation applications due to its commensurate power level and efficiency with conductive power transfer means. In this paper, an inductively coupled wireless charging system for 48V light electric vehicle is proposed. The power stages of the system is evaluated and designed, including the high frequency inverter, the resonant network, full bridge rectifier, and the load matching converter. Small signal modeling and linear control technology is applied to the load matching converter for input voltage control, which effectively controls the wireless power flow. The prototype is built with a dsPIC digital signal controller; the experiments are carried out, and the results reveal nature performances of a series-series resonant inductive power charger in terms of frequency, air-gap length, power flow control, and efficiency issues.

The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods

Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering. PCA calculates the variance between variables and groups them into a few uncorrelated groups or principal components (PCs) that are orthogonal to each other. Hierarchical clustering is carried out by separating data into many clusters and merging similar clusters together. Here, we use an example of human leukocyte antigen (HLA) supertype classification to demonstrate the usage of the two methods. Two programs, Generating Optimal Linear Partial Least Square Estimations (GOLPE) and Sybyl, are used for PCA and hierarchical clustering, respectively. However, the reader should bear in mind that the methods have been incorporated into other software as well, such as SIMCA, statistiXL, and R.

Gaussian regression and optimal finite dimensional linear models

The problem of regression under Gaussian assumptions is treated generally. The relationship between Bayesian prediction, regularization and smoothing is elucidated. The ideal regression is the posterior mean and its computation scales as O(n³), where n is the sample size. We show that the optimal m-dimensional linear model under a given prior is spanned by the first m eigenfunctions of a covariance operator, which is a trace-class operator. This is an infinite dimensional analogue of principal component analysis. The importance of Hilbert space methods to practical statistics is also discussed.

Theory of optimal power budget in quasi-linear dispersion-managed fibre links

A theory of an optimal distribution of the gain of in-line amplifiers in dispersion-managed transmission systems is developed. As an example of the application of the general method, a design of the line with periodically imbalanced in-line amplification is proposed.

Optimal power budget in quasi-linear dispersion-managed fiber links

We develop a theory of an optimal distribution of the gain of in-line amplifiers in dispersion-managed transmission systems. As an example of the application of the general method we propose a design of the line with periodically imbalanced in-line amplification.

Generalised Riccati solution and pinning control of complex stochastic networks

This paper considers the global synchronisation of a stochastic version of coupled map lattices networks through an innovative stochastic adaptive linear quadratic pinning control methodology. In a stochastic network, each state receives only noisy measurement of its neighbours' states. For such networks we derive a generalised Riccati solution that quantifies and incorporates uncertainty of the forward dynamics and inverse controller in the derivation of the stochastic optimal control law. The generalised Riccati solution is derived using the Lyapunov approach. A probabilistic approximation type algorithm is employed to estimate the conditional distributions of the state and inverse controller from historical data and quantifying model uncertainties. The theoretical derivation is complemented by its validation on a set of representative examples.