Iteration‐Varying Fault Estimation for Nonlinear Distributed Parameter Systems via Open‐closed‐Loop Iterative Learning

The issue discussed in this article is iteration‐varying fault estimation for nonlinear distributed parameter systems (DPSs), where faults exhibit iteration‐varying characteristics across iterative processes. Firstly, an iterative learning observer is designed for nonlinear DPSs. Secondly, a fault estimator is proposed under open‐closed‐loop iterative learning framework. The iterative learning mechanism and current feedback are integrated to tackle the challenges posed by iteration‐varying faults. Theoretical analysis using ‐norm and norm techniques prove the ultimate boundedness of fault estimation error. Lastly, the efficacy of proposed method is demonstrated via comprehensive simulations.