Recursive Identification of EIV-ARX Models for Time Varying SISO Processes
For control engineers needing real-time adaptation of ARX models under sensor degradation and changing dynamics, but the method is incremental and simulation-only.
The paper proposes a recursive algorithm (rARX-DIPCA) for identifying time-varying EIV-ARX models, enabling real-time tracking of model parameters and noise variances without storing historical data. Simulation studies show effective tracking performance on benchmark systems.
This paper proposes a recursive algorithm, rARX-DIPCA, for identifying errors-in-variables autoregressive models with exogenous input (EIV-ARX), for tracking time-varying SISO processes. Building on a recently developed recursive iterative PCA method, the proposed algorithm recursively updates model parameters and noise variances as new measurements arrive, without storing historical data beyond a specified lag window. The method enables real-time adaptation to sensor degradation, and changes in model coefficients. The algorithm simultaneously identifies process order, time delay, and noise variances while maintaining computational efficiency through online covariance updates. Simulation studies on benchmark systems demonstrate effective tracking performance and practical applicability.