This paper presents a switched observer design methodology for distributed parameter systems governed by partial differential equations. The approach combines Proper Orthogonal Decomposition (POD) model reduction with adaptive sensor configuration switching to address computational challenges in infinite-dimensional state estimation. A finite-dimensional reduced-order model is constructed through POD basis extraction, enabling design of a robust switched Luenberger observer that dynamically selects among multiple measurement configurations. Sufficient conditions for exponential stability and prescribed H ∞ performance are formulated as Linear Matrix Inequalities (LMIs), ensuring computational tractability. The methodology accommodates practical considerations including sensor failures and operational flexibility. Validation through a counter-current tubular heat exchanger demonstrates accurate temperature distribution estimation under distributed uncertainties, confirming effectiveness for industrial distributed parameter system monitoring.
Tello et al. (Wed,) studied this question.