Recursive Identification of Reachset-Conformant Models using Constraint Underapproximation

Ensuring the safety of real systems necessitates the identification of non-deterministic models that capture all system behaviors. This process is often referred to as reachset-conformant identification. The state of the art, however, relies on batch processing of data, resulting in a) large linear programs to be solved when dealing with large datasets and b) in overly conservative solutions when identifying time-variant systems. We address these problems by proposing recursive reachset-conformant identification methods, which iteratively refine the identification results. Amongst others, we present a novel algorithm to reduce the number of linear constraints in general optimization problems by underapproximating the feasible set of solutions. Additionally, we enable the models to adapt to changing system dynamics by incorporating forgetting factors in our methods. The proposed methods are evaluated by numerical experiments that show significant improvements in computational efficiency with only slight increases in conservatism.