Supervisory output prediction for bilinear systems by reinforcement learning

• Georgios Chasparis
• Thomas Natschläger

Online output prediction is an indispensable part of any model predictive control implementation. For several application scenarios, operating conditions may change quite often, while designing the data collection process may not be an option. To this end, this paper introduces a supervisory output prediction scheme, tailored specifically for input-output stable bilinear systems, that intends on automating the process of selecting the most appropriate prediction model during runtime. The selection process is based upon a reinforcement-learning scheme, where prediction models are selected according to their prior prediction performance. An additional selection process is concerned with appropriately partitioning the control-inputsaAZ domain in order to also allow for switched-system approximations of the original bilinear dynamics. We show analytically that the proposed scheme converges (in probability) to the best model and partition. We also demonstrate these properties through simulations of temperature prediction in residential buildings.