A human-centric framework for robotic task learning and optimization

Many chemical processes exhibit diverse timescale dynamics with a strong coupling between timescale sensitive variables. Model predictive control with a non-uniformly spaced optimisation horizon is an effective approach to multi-timescale control and offers opportunities for reduced computational complexity. In such an approach, dynamics with different timescales can be included in an optimisation problem by implementing faster timescale models (with shorter time intervals) earlier in the prediction horizon and increasingly slower timescale models (with longer time intervals) towards the end. In this paper, a reference-flexible condition is developed based on contraction theory to provide a stability guarantee for cost minimising control of multiple timescale nonlinear systems using multiple predictive models.