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Fault detection and estimation based on adaptive iterative learning algorithm for nonlinear systems. (Chinese. English summary) Zbl 1474.93073

Summary: 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 \({H_\infty}\) performance index is introduced to improve the convergence rate of the fault estimation observer. Using the algorithm we first design the fault detection observer to detect the fault, then design the fault estimation observer. 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.

MSC:

93B47 Iterative learning control
93C40 Adaptive control/observation systems
93C10 Nonlinear systems in control theory
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