Sensor Fault Estimation via Iterative Learning Scheme for Linear Repetitive System

In this study, a sensor fault estimation framework is proposed for linear repetitive system. Firstly, the problem of sensor fault estimation is converted to state estimation via state redefinition. Then, state estimation is realized by conventional state observer. The uniformly convergence of error extended system is guaranteed by asymptotic stability. Afterwards, iterative learning law is presented for fault estimation. And the optimal function is designed for the iterative convergence. Finally, Linear matrix inequalities (LMIs) is utilized to obtain the specific feasible solution, thus to improve the performance of proposed method. Further, a numerical example is provided to demonstrate the effectiveness of the developed method.