Singularity-free indirect iterative learning control

We investigate trajectory tracking for a general multi-input nonlinear system, where there is no priori knowledge of the system parameters and the form of the nonlinear function. An identification-based indirect iterative learning control (ILC) scheme to repetitively estimate the linearity in a neighborhood of a desired trajectory is presented. How to avoid singularity for the system with unknown control direction during the learning process is discussed. A parameter modification procedure for the ILC is presented such that the determinant of the estimate of the input coupling matrix is uniformly bounded.