Supervisory system identification for bilinear systems with application to thermal dynamics in buildings
|G. Chasparis. Supervisory system identification for bilinear systems with application to thermal dynamics in buildings. pages 832-837, 10, 2014.|
|Buch||Proceedings of the 2014 IEEE Multi-Conference on Systems and Control - MSC 2014|
When system identification is performed online and predictions of the system response are requested often (as in model predictive control formulations), the identification model with the best performance may not be fixed with time. Besides, more accurate models may require larger training times compared to low-order linear models. This is particularly evident in thermal dynamics in buildings where operating conditions may change throughout the year. To this end, this paper introduces a supervisory identification process, tailored specifically for inputoutput stable bilinear systems, where two parallel decision processes run periodically. The first one is concerned with the selection of the appropriate partition of the input(s) domain, while the second one is concerned with the selection of the identification model for each one of the resulting partition sets. The overall identification model constitutes a switched system. We show analytically that the proposed scheme is adaptive and robust to changes in the performance of the identification models, while convergence is attained (in probability) to the best model. We finally demonstrate these properties through simulations of the proposed supervisory process in Matlab/Simulink where identification of thermal dynamics in buildings is performed.