摘要：AbstractIn this work, a recursive system identification algorithm is extended to improve reliability and better handle stochastic disturbances, measurement noise, and other adverse phenomena. The proposed approach involves the modification of the recursive predictor-based subspace identification (PBSID) algorithm to incorporate constraints on the fidelity and accuracy of the identified models, correctness of the sign of the input-to-output gains, and the integration of heuristics to ensure stability of the recursively identified models. The efficacy of the proposed approach is demonstrated using case studies involving the modeling of time-varying glucose–insulin dynamics.
关键词：Keywordsrecursive system identificationsubspace identificationstate-space modelssystem stabilityartificial pancreas
