Multivariable nonparametric learning: A robust iterative inversion-based control approach

Learning control enables significant performance improvement for systems by utilizing past data. Typical design methods aim to achieve fast convergence by using prior system knowledge in the form of a parametric model. To ensure that the learning process converges in the presence of model uncertainties, it is essential that robustness is appropriately introduced, which is particularly challenging for multivariable systems. The aim of the present article is to develop an optimization-based design framework for fast and robust learning control for multivariable systems. This is achieved by connecting robust control and nonparametric frequency response function identification, which results in a design approach that enables the synthesis of learning and robustness parameters on a frequency-by-frequency basis. Application to a multivariable benchmark motion system confirms the potential of the developed framework.