An application on intelligent control using neural network and fuzzy logic

Abstract

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 artificial neural network approaches are the most common methodologies developed actively during recent years. In this paper, we describe a completed feasibility study demonstrating the merit of employing pattern recognition and an artificial 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 levitation vehicle (MLV) system is studied. Analytical fault symptoms are obtained by system dynamics measurements and the classification is carried out through a multilayer feed-forward network. The neural network is first taught the different fault situations through training patterns. After the network is trained, it achieves an overall classification 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.