An application on intelligent control using neural network and fuzzy logic

Intelligent control has become an issue of primary importance in modern process automation as it provides the prerequisites for the task of fault detection. The ability to detect the faults is essential to improve reliability and security of a complex control system. Parameter estimation methods, state observation schemes, statistical likelihood ratio tests, rule-based expert system reasoning, pattern recognition techniques, and artiicial neural network approaches are the most common method-ologies developed actively during recent years. In this paper, we describe a completed feasibility study demonstrating the merit of employing pattern recognition and an artiicial neural network for fault diagnosis through back propagation learning algorithm and making the use of fuzzy approximate reasoning for fault control via parameter changes in a dynamic system. As a test case, a complex magnetic levi-tation vehicle (MLV) system is studied. Analytical fault symptoms are obtained by system dynamics measurements and the classiication is carried out through a mul-tilayer feed-forward network. The neural network is rst taught the diierent fault situations through training patterns. After the network is trained, it achieves an overall classiication accuracy of 99.78% for a disturbance-free MLV model, 91.4% for a model with track disturbance irregularities, and 93.85% for a model with measurement noise. Proper actions are performed based on fuzzy reasoning of knowledge base results in a normal process operation recovered.