摘要
常规的解析余度容错方法容易受到不确定因素和随机干扰的影响,本文以飞行控制系统为研究对象,提出基于智能解析余度的容错飞行控制系统设计方案,使用径向基神经网络的在线学习和全局逼近的性能,建立飞行控制系统传感器之间的解析余度关系,利用不相同传感器之间的解析关系进行残差分析从而进行传感器的故障隔离与信号重构.这样有效地抑制了测量噪声和模型不确定性.应用某型飞机进行仿真,实现了传感器的在线故障隔离与重构,验证了该方法的有效性.
To restrain modeling uncertainties and disturbance, a method for sensor failure diagnosis and accommodation is proposed, which is based on intelligence analytical redundancy. The analytical models of sensors are set up by using the Radio Basis Function neural network which is designed applying efficient al- gorithm of on-line learning and parameter optimization. According to the relationship between the sensors, residual is produced and analyzed for sensors fault isolation and signal reconfiguration. By the method, model uncertainties, noises and disturbance are restrained. The scheme is illustrated through simulations applying the flight control system of a fighter. Results show sensor online fault isolation and reconfigura tion is achieved.
出处
《传感技术学报》
CAS
CSCD
北大核心
2007年第8期1912-1916,共5页
Chinese Journal of Sensors and Actuators
关键词
解析余度
径向基神经网络(RBFNN)
飞行控制系统
容错
analytical redundancy
Radio Basis Function neural network
flight control systems
fault-tolerance
