论文部分内容阅读
针对一类存在执行器和传感器故障的非线性系统,提出基于滤波器的故障重构方法.为了使算法同时适用于状态和输出端,通过由系统方程构造新状态方程对系统作扩展变换,将原系统输出端非线性和故障转换到扩展系统的状态方程,在此基础上设计故障诊断滤波器,采用迭代学习调节算法更新虚拟故障使之逼近实际故障.该算法可以检测和估计系统故障,并且对不同类型故障具有一定的适应性.在单关节机器人模型上进行仿真实验,实验结果验证了所提出算法的可行性和有效性.
For a class of nonlinear systems with actuators and sensor faults, a filter-based fault reconstruction method is proposed. In order to make the algorithm suitable for both state and output, the system is extended and transformed by constructing a new state equation from system equations. The original system output nonlinearity and fault transition to the extended system state equation, based on which the fault diagnosis filter is designed and the iterative learning algorithm is used to update the virtual fault to approximate the actual fault.The algorithm can detect and estimate the system fault, and Which has some adaptability to different types of faults.A simulation experiment is carried out on a single-joint robot model.The experimental results verify the feasibility and effectiveness of the proposed algorithm.