Decision mechanism, knowledge representation, and software architecture for an intelligent control system

Anoop Malaviya

Research output: ThesisDoctoral Thesis

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[Truncated abstract] This thesis analyses the problem of Intelligent Control for large industrial plants and suggests a hierarchical, distributed, object-oriented architecture for Intelligent Control. The architecture is called MLIAC (Multi Level Intelligent Adaptive Control) Architecture. The MLIAC architecture is inspired by biological control systems (which are flexible, and are capable of adapting to unstructured environments with ease) and the success of the distributed architecture SCADA (Supervisory Control and Data Acquisition) Systems. The MLIAC Architecture structures the decision and control mechanism for the real-time properties namely safety, liveliness, and timeliness . . . In addition, three case studies have been reported. The case studies cover the control of a Flexible Manufacturing System and the Mine Products Quality Control. The results show that MLIAC Knowledge Representation model meets the requirements of the Roth-Hayes benchmark regarding Knowledge Representation. The decisions taken are logically tractable. The software architecture is effective and easily implemented. The actual performance has been found to depend upon a number of factors discussed in this thesis. For the specification and design of Potline MLIAC software, a CASE package ("Software Through Pictures") has been used. The Potline MLIAC software has been developed using C⁄C++, SQL, 4 GL and RDBMS based on a Client-Server model. For computer simulation the Potline MLIAC software has been integrated with the MATLAB⁄SIMULINK package.
Original languageEnglish
QualificationDoctor of Philosophy
Publication statusUnpublished - 1997


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