Cyber security and protection of ICS systems: An australian example

Many aspects of our modern society now have either a direct or implicit dependence upon information technology. As such, a compromise of the availability or integrity in relation to these systems (which may encompass such diverse domains as banking, government, health care, and law enforcement) could have dramatic consequences from a societal perspective. These key systems are often referred to as critical infrastructure. Critical infrastructure can consist of corporate information systems or systems that control key industrial processes; these specific systems are referred to as ICS (Industry Control Systems) systems. ICS systems have devolved since the 1960s from standalone systems to networked architectures that communicate across large distances, utilise wireless network and can be controlled via the Internet. ICS systems form part of many countries’ key critical infrastructure, including Australia. They are used to remotely monitor and control the delivery of essential services and products, such as electricity, gas, water, waste treatment and transport systems. The need for security measures within these systems was not anticipated in the early development stages as they were designed to be closed systems and not open systems to be accessible via the Internet. We are also seeing these ICS and their supporting systems being integrated into organisational corporate systems.

Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

Enabling industrial scale simulation / emulation models

OLE Process Control (OPC) is an industry standard that facilitates the communication between PCs and Programmable Logic Controllers (PLC). This communication allows for the testing of control systems with an emulation model. When models require faster and higher volume communications, limitations within OPC prevent this. In this paper an interface is developed to allow high speed and high volume communications between a PC and PLC enabling the emulation of larger and more complex control systems and their models. By switching control of elements within the model between the model engine and the control system it is possible to use the model to validate the system design, test the real world control systems and visualise real world operation.

Robust functional estimation and fault diagnosis for complex uncertain systems

System monitoring and fault diagnosis capabilities are the most important aspects in improving safety and reliability of automatic control systems. This research proposed new methodologies on fault diagnosis and estimation for complex uncertain systems. As a result of this research, complex industrial plants can now be more effectively controlled.

Adaptive fuzzy multi-surface sliding control of multiple-input and multiple-output autonomous flight systems

In this study, we proposed an adaptive fuzzy multi-surface sliding control (AFMSSC) for trajectory tracking of 6 degrees of freedom inertia coupled aerial vehicles with multiple inputs and multiple outputs (MIMO). It is shown that an adaptive fuzzy logic-based function approximator can be used to estimate the system uncertainties and an iterative multi-surface sliding control design can be carried out to control flight. Using AFMSSC on MIMO autonomous flight systems creates confluent control that can account for both matched and mismatched uncertainties, system disturbances and excitation in internal dynamics. It is proved that the AFMSSC system guarantees asymptotic output tracking and ultimate uniform boundedness of the tracking error. Simulation results are presented to validate the analysis.

Uses of management control and performance measurement systems by Deans and Heads in Australian universities: their effects on research, teaching and networking capabilities

Invoking a resource-based view (RBV), this study investigates relationships between management control systems (MCSs) use, including information use from performance measurement systems (PMSs), and organisational capabilities in the context of academic units of Australian universities. Increased competition and attention to distinctive capabilities amongst universities, particularly at their strategic operating unit level of a Faculty1 or School2, provides the setting for application of this theoretic perspective. Based on a questionnaire survey of all Faculty Deans and Heads of Schools in all 39 universities in Australia, evidence is provided on relationships between diagnostic and interactive use of MCSs, attention given to imposed and discretionary types of PMS information, the strength of capabilities of the academic unit and, in turn, overall performance of the academic unit. Highlights of findings are that Heads/Deans conceived capabilities of their unit in functional dimensions, not in generic dimensions as found in prior literature; interactive MCS use and imposed performance measures, respectively, direct relate to several types of capabilities and indirectly to performance of the academic unit, but diagnostic MCS use does not. The findings have practical implications for styles of control systems use and performance information use by management in universities.

An observer design procedure for a class of nonlinear time-delay systems

This paper considers a class of uncertain, nonlinear differential state delayed control systems and presents a reduced-order observer design procedure to asymptotically estimate any vector state functionals. The method proposed involves decomposition of the delayed portion of the system into two parts: a matched and mismatched part. Provided that the rank of the mismatched part is less than the number of the outputs, a reduced-order linear functional observer, with any prescribed stability margin, can be constructed by using a simple procedure. A numerical example is given to illustrate the new design procedure and its features.

Using information flow model to specify the Kanban-controlled shopfloor control system

The implementation of Kanban-based production control systems may be difficult in make-to-order environments such as job shops. The flexible manufacturing approach constitutes a promising solution to adapt the Kanban method to such environments. This paper presents an information flow modelling approach for specifying the operational planning and control functions of the Kanban-controlled shopfloor control system (KSCS) in a flexible manufacturing environment. By decomposing the KSCS control functionalities, we have created the system information flow model through the data flow diagrams of Structured Systems Analysis Methodology. The data flow diagrams serve effective system specifications for communicating the system operations to participants of different disciplines as well as the system model for the design and development of KSCS.

Interpersonal trust and control behaviour of Chinese managers

Interpersonal trust is believed to influence the management and control of organisations in China. China's importance as a host country for foreign direct investments (FDIs) through multinational company subsidiaries (MNCs) and international joint ventures (IJVs) is growing rapidly. MNCs and INs located in China often employ local Chinese managers to control their subsidiaries or ventures. This makes it essential for designers of management control systems to have an understanding of the interpersonal trust-sensitive control behaviour of Chinese managers. One of the important aspects of control behaviour is how managers control their subordinates.

This paper examines the relationship between Chinese managers' trust in subordinates and their (Chinese managers') control behaviour towards the subordinates. On the basis of a questionnaire survey of a cohort of managers from Beijing, the study explores the effects of trust on the use of social controls, formal controls, and monitoring.

The findings of this study indicate that a manager's high (low) trust in a subordinate is associated with a low (high) level of monitoring, a high (high) level of social control and a high (low) level of perceived performance. The hypothesis that a superior's high (low) level of trust is associated with a low (high) level of reliance on formal controls was not supported. These findings, while indicative of control behaviour of Chinese managers in particular, also add to the growing academic literature on trust and control in general. In a practical sense, an
understanding of the trust-sensitive control behaviour of Chinese managers is particularly useful in designing and implementing effective control systems for international organisations operating in China.

Robust strictly positive real synthesis of polynomial segments for discrete time systems

By using the result of robust strictly positive real synthesis of polynomial segments for continuous time systems, it is proved that, for any two n-th order polynomials a(z) and b(z), the Schur stability of their convex combination is necessary and sufficient for the existence of an n-th order polynomial c(z) such that c(z)/a(z) and c(z)/b(z) are both strictly positive real. We also provide the construction method of c(z). Illustrative examples are provided to show the effectiveness of this method.

Knowledge sharing in professional accountancy firms : the effects of task complexity and management control system

Numerous empirical studies on knowledge management have focused on effectiveness of enablers such as organizational structure (Bennett and Gabriel, 1999; Gold et al., 2001), technology (Gold et al., 2001; O’Dell and Grayson, 1998), culture (DeLong and Fahey, 2000; Gupta and Govindarajan, 2000), managerial system (Nonaka, 1994; Sveiby, 1997) and strategy (Bierly and Chakrabarti, 1996; Holsapple and Joshi, 2001) on knowledge sharing. These enablers are organizational infrastructure or mechanism for facilitating the sharing of knowledge in a firm. In knowledge-intensive firms, task complexity and management control systems (MCS) can potentially affect the mode and effectiveness of knowledge sharing. However, these two factors have not been distinctly and explicitly investigated and discussed in literature relevant to the domain of knowledge sharing and management. This study proposes to examine how task complexity and the design of MCS could be the key determinants of the mode and effectiveness of knowledge sharing in professional accounting firms or practices.

Haptic control methodologies for rover teleoperation in non-deterministic remote environments

Haptic teleoperation allows human operators to interact with a remote mobile robot using their haptic sensory modality. This research introduces new haptic control methodologies allowing the teleoperator to overcome the limitations of existing techniques, ultimately facilitating improved mobile robotic control for the exploration of hazardous and remote environments.

Performance of first and second-order sliding mode observers for nonlinear systems

This paper presents a brief study on the design and performance comparison of conventional first-order and super-twisting second-order sliding mode observers for some nonlinear control systems. Estimation accuracy, fast response, chattering effect, peaking phenomenon and robustness are considered for nonlinear ystems under observer-based output feedback control and state feedback control.

Adaptive intelligent systems for energy management of vehicles

The thesis demonstrated the architecture of adaptive intelligent systems for energy management that is capable of interacting with complex systems including the vehicle, environment, and driver components, as well as the interrelationships between these variables, to deliver fuel consumption improvements.

Estimator-based sliding mode control strategy design

This study addresses the design and properties of serial sliding mode control (SMC) systems for an induction servo motor drive to track periodic commands. It contains a SMC, an adaptive SMC (ASMC) and an estimator-based SMC (ESMC). The effectiveness of the proposed control systems is verifi ed by numerical simulations, and the superiority of the ESMC system is indicated in comparison with the SMC and ASMC systems.