On-Line Supervised Adaptive Training Using Radial Basis Function Networks

A new recursive supervised training algorithm is derived for the radial basis neural network architecture. The new algorithm combines the procedures of on-line candidate regressor selection with the conventional Givens QR based recursive parameter estimator to provide efficient adaptive supervised network training. A new concise on-line correlation based performance monitoring scheme is also introduced as an auxiliary device to detect structural changes in temporal data processing applications. Practical and simulated examples are included to demonstrate the effectiveness of the new procedures

Aircraft Attitude Distributed Fault-tolerant Control Based on Dynamic Actuator

Abstract: For attitude control system, based on decentralized fault-tolerant control framework, actuators damage and stuck fault detection and identification unit are designed for the flight control system. And observer-based auxiliary system unit is also designed. The auxiliary system implies control surface damage faults and disturbances information. Firstly, we give the attitude control system under actuator stuck, lose of effectiveness, and control surface damages faults. Secondly, a multi-observer is designed for actuator fault detection and identification using a decision-making mechanism to determine current actuator failure modes. Then, an adaptive sliding mode observer is designed for implicit control surface damages and interference information. The reconfigurable controller can achieve fault tolerant using the information of adaptive sliding mode observer. Finally, the simulation results show the effectiveness of the proposed method