Learning supported Model Predictive Control for Tracking of Periodic References

Increased autonomy of controllers in tasks with uncertainties stemming from the interaction with the environment can be achieved by incorporation of learning. Examples are control tasks where the system should follow a reference which depends on measurement data from surrounding systems as e.g. humans or other control systems. We propose a learning strategy for Gaussian processes to model, filter and predict references for control systems under model predictive control. Hereby constraints in the learning are included to achieve safety guarantees as trackability and recursive feasibility. An illustrative simulation example for motion compensation is given which shows performance improvements of combined constrained learning and predictive control besides the provided guarantees.