A Neural Network Approach to Failure Decision of Adaptively Controlled Systems

作     者：K. Kumamaru K. Inoue S. Nonaka H. Ono T. Söderström 

作者机构：Dept of Control Eng. and Sci. Faculty of Computer Science and Systems Eng. Kyushu Institute of Technology Iizuka 820 Fukuoka Japan Dept. of Automatic Control and Systems Analysis Institute of Technology. Uppsala Unit. P. O. Box 27 Uppsala. Sweden 

出 版 物：《IFAC Proceedings Volumes》 

年 卷 期：1994年第27卷第8期

页      面：605-610页

主　　题：Adaptive control failure diagnosis Fullback discrimination information neural network parameter estimation 

摘      要：In this paper, a combined method of change detection and failure decision is proposed for the system under the adaptive control based on the self-tuning regulator. The controlled system is assumed encounter unexpected parameter changes, which may be caused by a failure or a normal operation. Such a system change can effectively be detected by using Fullback Discrimination Information (KDI) as an index for model discrimination. In order to decide whether the detected system change is caused by a failure or not, a neural network approach to failure decision is introduced. Based on the knowledge about failure modes and system operations, the regulator parameter variations after the change detection are used as training data for the network learning. In this way an on-line monitoring scheme of adaptively controlled systems can be established. Simulation studies of a second-order damped oscillator have been earned out to demonstrate the effectiveness of the method.