Research of iterative learning control based on total least squares

The problem of iterative learning control with measured noise in both the input and output data of a linear time invariant system is concerned. Total least squares method is applied to deal with the ill-posedness in the solving process of adaptive dynamic programming. The error just contained in output-data have been considered in other algorithms of many papers. However, if input-data also have disturbance, the tradition approaches may be not meaningful. By using the presented algorithm, one can get the desired controller only through the input and output data of the system, i.e., the controller design process based on the presented algorithm has data-driven property. Furthermore, because the well-posedness is considered when the algorithm is designed, algorithm is robustness. Illustrative example indicates that the presented algorithm can give a good controller for a system, even when the measured input-data are polluted by noise. Compared with exiting results, the presented algorithm has more effectiveness.