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Current practice in automation includes a trend whereby control systems in industrial plants are increasingly being distributed, especially in process plants where geographically distributed processes require controllers to be placed close to the process being controlled. At the same time, however, it is also becoming increasingly evident that the use of DCCS (Distributed Computer Control Schemes) techniques alone will not be as effective as the technology could be. This is largely due to the lack of emphasis on the human role during the design of DCCS, and the consequent lack of appropriate support for operators. The design of operator support systems calls for the use of the techniques to facilitate both human-computer interaction (HCI) and human-human (HHI) interaction. The reason is that operators in process plants work in teams or groups: operators must not only work closely together (with common data and applications), but must also be able to see other team members' decisions and actions, as reflected in changes to shared data. In such situations, a concentration on HCI design is not enough; HHI must be considered so as to increase the effectiveness of cooperative work. This has led to the emergence of a special class of systems - computer-supported cooperative work (CSCW) - which support groups working together on a common application, in a way that makes any one user's action visible to all the others. Multimedia technology can improve the efficiency and effectiveness of such CSCW systems if it is used appropriately. To design such a CSCW system, a key is the development of an appropriate architecture upon which to base the system. In this thesis, a distributed whiteboard architecture, a network of sharable common workspace (whiteboard), is suggested.

Fuzzy controllers (FCs) are supposed to work in situations where there exists a large uncertainty or unknown variation in plant parameters and structures. Thus, it is necessary to introduce adaptive mechanisms in such control systems to tune the controllers to match current process characteristics. Of the various tunable parameters in a FC, the input and output scaling factors have the highest priority due to their global effect on the control performance. However, the working mechanisms of these scaling factors to the performance of fuzzy control system are yet to be founded due to the lack of an analytical structure for the general fuzzy control systems. In this thesis, for a general fuzzy control system, a systematic investigation on the importance of the scaling factors is carried out. Some quantitative and qualitative results about the relationships between the scaling factors and the system steady-state and dynamic properties are obtained. By applying the Lyapunov stability theory, this thesis analyzes the relationship between the scaling factors and the system stability for a class of uncertain nonlinear processes, where a simple and realistic sufficient condition is established in the sense that the system trajectory is bounded. The methodology and results established open up many opportunities for further research and encourage wide applications. Based on the obtained analysis results, a knowledge-based self-tuning fuzzy control scheme is proposed, where the scaling factors are tuned on-line based on the system performance indices and the current process states, and a stability monitor is introduced to guarantee that the states of the controlled process are bounded. The relevant design issues are also discussed.

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