Design of a multi-processor control systems

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Comparison of a preferred and actual method of selecting and implementing computerised control systems in a manufacturing environment

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An investigation of management accounting control systems in the palm oil industry:a sociomaterial approach to practice change

The primary aim of this research is to understand what constitutes management accounting and control (MACs) practice and how these control processes are implicated in the day to day work practices and operations of the organisation. It also examines the changes that happen in MACs practices over time as multiple actors within organisational settings interact with each other. I adopt a distinctive practice theory approach (i.e. sociomateriality) and the concept of imbrication in this research to show that MACs practices emerge from the entanglement between human/social agency and material/technological agency within an organisation. Changes in the pattern of MACs practices happens in imbrication processes which are produced as the two agencies entangle. The theoretical approach employed in this research offers an interesting and valuable lens which seeks to reveal the depth of these interactions and uncover the way in which the social and material imbricate. The theoretical framework helps to reveal how these constructions impact on and produce modifications of MACs practices. The exploration of the control practices at different hierarchical levels (i.e. from the operational to middle management and senior level management) using the concept of imbrication process also maps the dynamic flow of controls from operational to top management and vice versa in the organisation. The empirical data which is the focus of this research has been gathered from a case study of an organisation involved in a large vertically integrated palm oil industry company in Malaysia specifically the refinery sector. The palm oil industry is a significant industry in Malaysia as it contributed an average of 4.5% of Malaysian Gross Domestic Product, over the period 1990 -2010. The Malaysian palm oil industry also has a significant presence in global food oil supply where it contributed 26% of the total oils and fats global trade in 2010. The case organisation is a significant contributor to the Malaysian palm oil industry. The research access has provided an interesting opportunity to explore the interactions between different groups of people and material/technology in a relatively heavy process food industry setting. My research examines how these interactions shape and are shaped by control practices in a dynamic cycle of imbrications over both short and medium time periods.

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise

Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained.

Probabilistic DHP adaptive critic for nonlinear stochastic control systems

Following the recently developed algorithms for fully probabilistic control design for general dynamic stochastic systems (Herzallah & Káarnáy, 2011; Kárný, 1996), this paper presents the solution to the probabilistic dual heuristic programming (DHP) adaptive critic method (Herzallah & Káarnáy, 2011) and randomized control algorithm for stochastic nonlinear dynamical systems. The purpose of the randomized control input design is to make the joint probability density function of the closed loop system as close as possible to a predetermined ideal joint probability density function. This paper completes the previous work (Herzallah & Kárnáy, 2011; Kárný, 1996) by formulating and solving the fully probabilistic control design problem on the more general case of nonlinear stochastic discrete time systems. A simulated example is used to demonstrate the use of the algorithm and encouraging results have been obtained.

Enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty

Modern advances in technology have led to more complex manufacturing processes whose success centres on the ability to control these processes with a very high level of accuracy. Plant complexity inevitably leads to poor models that exhibit a high degree of parametric or functional uncertainty. The situation becomes even more complex if the plant to be controlled is characterised by a multivalued function or even if it exhibits a number of modes of behaviour during its operation. Since an intelligent controller is expected to operate and guarantee the best performance where complexity and uncertainty coexist and interact, control engineers and theorists have recently developed new control techniques under the framework of intelligent control to enhance the performance of the controller for more complex and uncertain plants. These techniques are based on incorporating model uncertainty. The newly developed control algorithms for incorporating model uncertainty are proven to give more accurate control results under uncertain conditions. In this paper, we survey some approaches that appear to be promising for enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty.

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.

A formal methodology for the verification of concurrent systems

There is an increasing emphasis on the use of software to control safety critical plants for a wide area of applications. The importance of ensuring the correct operation of such potentially hazardous systems points to an emphasis on the verification of the system relative to a suitably secure specification. However, the process of verification is often made more complex by the concurrency and real-time considerations which are inherent in many applications. A response to this is the use of formal methods for the specification and verification of safety critical control systems. These provide a mathematical representation of a system which permits reasoning about its properties. This thesis investigates the use of the formal method Communicating Sequential Processes (CSP) for the verification of a safety critical control application. CSP is a discrete event based process algebra which has a compositional axiomatic semantics that supports verification by formal proof. The application is an industrial case study which concerns the concurrent control of a real-time high speed mechanism. It is seen from the case study that the axiomatic verification method employed is complex. It requires the user to have a relatively comprehensive understanding of the nature of the proof system and the application. By making a series of observations the thesis notes that CSP possesses the scope to support a more procedural approach to verification in the form of testing. This thesis investigates the technique of testing and proposes the method of Ideal Test Sets. By exploiting the underlying structure of the CSP semantic model it is shown that for certain processes and specifications the obligation of verification can be reduced to that of testing the specification over a finite subset of the behaviours of the process.

Computer control of machines utilising independent drive mechanisms

The thesis describes an investigation into methods for the specification, design and implementation of computer control systems for flexible manufacturing machines comprising multiple, independent, electromechanically-driven mechanisms. An analysis is made of the elements of conventional mechanically-coupled machines in order that the operational functions of these elements may be identified. This analysis is used to define the scope of requirements necessary to specify the format, function and operation of a flexible, independently driven mechanism machine. A discussion of how this type of machine can accommodate modern manufacturing needs of high-speed and flexibility is presented. A sequential method of capturing requirements for such machines is detailed based on a hierarchical partitioning of machine requirements from product to independent drive mechanism. A classification of mechanisms using notations, including Data flow diagrams and Petri-nets, is described which supports capture and allows validation of requirements. A generic design for a modular, IDM machine controller is derived based upon hierarchy of control identified in these machines. A two mechanism experimental machine is detailed which is used to demonstrate the application of the specification, design and implementation techniques. A computer controller prototype and a fully flexible implementation for the IDM machine, based on Petri-net models described using the concurrent programming language Occam, is detailed. The ability of this modular computer controller to support flexible, safe and fault-tolerant operation of the two intermittent motion, discrete-synchronisation independent drive mechanisms is presented. The application of the machine development methodology to industrial projects is established.

The human factors aspects of alarms in human supervisory control tasks.

This research thesis is concerned with the human factors aspects of industrial alarm systems within human supervisory control tasks. Typically such systems are located in central control rooms, and the information may be presented via visual display units. The thesis develops a human, rather than engineering, centred approach to the assessment, measurement and analysis of the situation. A human factors methodology was employed to investigate the human requirements through: interviews, questionnaires, observation and controlled experiments. Based on the analysis of current industrial alarm systems in a variety of domains (power generation, manufacturing and coronary care), it is suggested that often designers do not pay due considerations to the human requirements. It is suggested that most alarm systems have severe shortcomings in human factors terms. The interviews, questionnaire and observations led to the proposal of 'alarm initiated activities' as a framework for the research to proceed. The framework comprises of six main stages: observe, accept, analyse, investigate, correct and monitor. This framework served as a basis for laboratory research into alarm media. Under consideration were speech-based alarm displays and visual alarm displays. Non-speech auditory displays were the subject of a literature review. The findings suggest that care needs to be taken when selecting the alarm media. Ideally it should be chosen to support the task requirements of the operator, rather than being arbitrarily assigned. It was also indicated that there may be some interference between the alarm initiated activities and the alarm media, i.e. information that supports one particular stage of alarm handling may interfere with another.

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.