Neural control of a batch distillation

The purpose of the work reported here was to investigate the application of neural control to a common industrial process. The chosen problem was the control of a batch distillation. In the first phase towards deployment, a complex software simulation of the process was controlled. Initially, the plant was modelled with a neural emulator. The neural emulator was used to train a neural controller using the backpropagation through time algorithm. A high accuracy was achieved with the emulator after a large number of training epochs. The controller converged more rapidly, but its performance varied more widely over its operating range. However, the controlled system was relatively robust to changes in ambient conditions.

Improved robust control of nonlinear stochastic systems using uncertain models

We introduce a technique for quantifying and then exploiting uncertainty in nonlinear stochastic control systems. The approach is suboptimal though robust and relies upon the approximation of the forward and inverse plant models by neural networks, which also estimate the intrinsic uncertainty. Sampling from the resulting Gaussian distributions of the inversion based neurocontroller allows us to introduce a control law which is demonstrably more robust than traditional adaptive controllers.

Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals

We introduce a novel inversion-based neuro-controller for solving control problems involving uncertain nonlinear systems that could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. In this work a novel robust inverse control approach is obtained based on importance sampling from these distributions. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The performance of the new algorithm is illustrated through simulations with example systems.

Blockade of interleukin-6 signaling inhibits the classic pathway and promotes an alternative pathway of macrophage activation after spinal cord injury in mice

Background Recent in vivo and in vitro studies in non-neuronal and neuronal tissues have shown that different pathways of macrophage activation result in cells with different properties. Interleukin (IL)-6 triggers the classically activated inflammatory macrophages (M1 phenotype), whereas the alternatively activated macrophages (M2 phenotype) are anti-inflammatory. The objective of this study was to clarify the effects of a temporal blockade of IL-6/IL-6 receptor (IL-6R) engagement, using an anti-mouse IL-6R monoclonal antibody (MR16-1), on macrophage activation and the inflammatory response in the acute phase after spinal cord injury (SCI) in mice. Methods MR16-1 antibodies versus isotype control antibodies or saline alone were administered immediately after thoracic SCI in mice. SC tissue repair was compared between the two groups by Luxol fast blue (LFB) staining for myelination and immunoreactivity for the neuronal markers growth-associated protein (GAP)-43 and neurofilament heavy 200 kDa (NF-H) and for locomotor function. The expression of T helper (Th)1 cytokines (interferon (IFN)-? and tumor necrosis factor-a) and Th2 cytokines (IL-4, IL-13) was determined by immunoblot analysis. The presence of M1 (inducible nitric oxide synthase (iNOS)-positive, CD16/32-positive) and M2 (arginase 1-positive, CD206-positive) macrophages was determined by immunohistology. Using flow cytometry, we also quantified IFN-? and IL-4 levels in neutrophils, microglia, and macrophages, and Mac-2 (macrophage antigen-2) and Mac-3 in M2 macrophages and microglia. Results LFB-positive spared myelin was increased in the MR16-1-treated group compared with the controls, and this increase correlated with enhanced positivity for GAP-43 or NF-H, and improved locomotor Basso Mouse Scale scores. Immunoblot analysis of the MR16-1-treated samples identified downregulation of Th1 and upregulation of Th2 cytokines. Whereas iNOS-positive, CD16/32-positive M1 macrophages were the predominant phenotype in the injured SC of non-treated control mice, MR16-1 treatment promoted arginase 1-positive, CD206-positive M2 macrophages, with preferential localization of these cells at the injury site. MR16-1 treatment suppressed the number of IFN-?-positive neutrophils, and increased the number of microglia present and their positivity for IL-4. Among the arginase 1-positive M2 macrophages, MR16-1 treatment increased positivity for Mac-2 and Mac-3, suggestive of increased phagocytic behavior. Conclusion The results suggest that temporal blockade of IL-6 signaling after SCI abrogates damaging inflammatory activity and promotes functional recovery by promoting the formation of alternatively activated M2 macrophages.

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.

A methodology for analysis and control of discrete event dynamic systems

The rapid developments in computer technology have resulted in a widespread use of discrete event dynamic systems (DEDSs). This type of system is complex because it exhibits properties such as concurrency, conflict and non-determinism. It is therefore important to model and analyse such systems before implementation to ensure safe, deadlock free and optimal operation. This thesis investigates current modelling techniques and describes Petri net theory in more detail. It reviews top down, bottom up and hybrid Petri net synthesis techniques that are used to model large systems and introduces on object oriented methodology to enable modelling of larger and more complex systems. Designs obtained by this methodology are modular, easy to understand and allow re-use of designs. Control is the next logical step in the design process. This thesis reviews recent developments in control DEDSs and investigates the use of Petri nets in the design of supervisory controllers. The scheduling of exclusive use of resources is investigated and an efficient Petri net based scheduling algorithm is designed and a re-configurable controller is proposed. To enable the analysis and control of large and complex DEDSs, an object oriented C++ software tool kit was developed and used to implement a Petri net analysis tool, Petri net scheduling and control algorithms. Finally, the methodology was applied to two industrial DEDSs: a prototype can sorting machine developed by Eurotherm Controls Ltd., and a semiconductor testing plant belonging to SGS Thomson Microelectronics Ltd.

Control of antibiotic prescribing in UK NHS hosptials. Available in 2 volumes.

This thesis is an evaluation of practices to control antibiotic prescribing in UK NHS hospitals. Within the past ten years there has been increasing international concern about escalating antibiotic resistance, and the UK has issued several policy documents for pmdent antibiotic prescribing. Chief Pharmacists in 253 UK NHS hospitals were surveyed about the availability and nature of documents to control antibiotic prescribing (formularies, policies and guidelines), and the role of pharmacists and medical microbiologists in monitoring prescribers' compliance with the recommendations of such documents. Although 235 hospitals had at least one document, only 60% had both an antibiotic formulary and guidelines, and only about one-half planned an annual revision of document(s). Pharmacists were reported as mostly checking antibiotic prescribing on every ward whilst medical microbiologists mostly visited selected units only. Response to a similar questionnaire was obtained from the Chief Medical Microbiologists in 131 UK NHS hospitals. Comparisons of the questionnaires indicated areas of apparent disagreement about the roles of pharmacists and medical microbiologists. Eighty three paired-responses received from pharmacists and medical microbiologists in the same hospital revealed poor agreement and awareness about controls. A total of 205 institutional prescribing guidelines were analysed for recommendations for the empirical antibiotic prescribing of Community-Acquired Pneumonia (CAP). Variation was observed in recommendations and agreement with national guidance from the British Thoracic Society (BTS). A questionnaire was subsequently sent to 235 Chief Pharmacists to investigate their awareness of this new guidance from the BTS, and subsequent revision of institutional guidelines. Documents had been revised in only about one-half of hospitals where pharmacists were aware of the new guidance. An audit of empirical antibiotic prescribing practices for CAP was performed at one hospital. Although problems were experienced with retrieval of medical records, diagnostic criteria were poorly recorded, and only 57% of prescribing for non-severe CAP was compliant with institutional guidelines. A survey of clinicians at the same hospital identified that almost one-half used the institutional guidelines and most found them useful. However, areas for improvement concernmg awareness of the guidelines and ease of access were identified. It is important that hospitals are equipped to react to changes in the hospital environment including frequent movement of junior doctors between institutions, the employment of specialist "infectious diseases pharmacists" and the increasing benefits offered by information technology. Recommendations for policy have been suggested.

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.