LAUSR

This work aims to quickly identify an FIR inverse dynamical model for linear time-invariant (LTI) systems. Various applications are enabled using the constructed inverse filter, as illustrated by an inversion-based iterative learning control (ILC) algorithm. With the help of interleaving inversion-based ILC and ILC-based inverse dynamics identification, accelerated convergence is obtained. The proposed method removes the numerical instability issues in the calculation of an inverse model. Hence, it is shown more robust against measurement noises. Both simulation comparison and experimental results demonstrate the efficacy and advantages of the proposed strategy.