Discrete-Time Modal State Reconstruction for Infinite-Dimensional Systems Using Generalized Sampling

Abstract

AbstractIn this contribution a novel approach for discrete-time state reconstruction for infinite-dimensional systems is presented. It is shown that an exact reconstruction of a finite number of modal states of the infinite-dimensional system can be obtained at the sampling time instances. This is achieved by aid of generalized sampling which generates the output samples by weighted averaging over one sampling interval. The reconstruction is done in finite time and is thus non-asymptotic. Since the method makes it possible to implement state feedback controls without involving a Luenberger observer, the spillover problem arising from the early-lumping approach can be avoided.