基于自适应迭代学习算法的一类非线性系统故障检测与估计

针对迭代学习算法在非线性系统故障检测与估计过程中存在估计误差较大和收敛速度较慢等不足的问题,提出了一种基于龙格–库塔故障估计观测器模型的自适应迭代学习算法,有效降低了故障估计误差;并引入H∞性能指标,提高了故障估计观测器的收敛速度.该算法首先设计故障检测观测器对故障进行检测,然后设计故障估计观测器,并将自适应算法与迭代学习策略相结合,使得估计故障逐渐逼近真实故障,从而实现对非线性系统中多种常见故障的精确检测与估计.最后,通过机械臂旋转关节驱动电机的执行器故障仿真验证了所提算法的有效性.

Fault detection and estimation based on adaptive iterative learning algorithm for nonlinear systems

Aiming at the problem that the iterative learning algorithm has a large estimation error and slow convergence speed in the process of nonlinear system fault detection and estimation. An adaptive iterative learning algorithm based on Runge–Kutta fault estimation observer model is proposed, which can effectively reduce the error of fault estimation;and the H∞ performance index is introduced to improve the convergence rate of the fault estimation observer. The algorithm first designs the fault detection observer to detect the fault, then designs the fault estimation observer, and the adaptive algorithm is combined with the iterative learning strategy, so that the estimated fault gradually approaches the real fault,thus achieving accurate detection and estimation of many common faults in the nonlinear system. Finally, the effectiveness of the proposed algorithm is verified by the actuator fault simulation of the mechanically driven motor.