A Continuous Finite-Memory Deadbeat Observer

A significant class of model-based control approaches uses Kalman filters or Luenberger observers to estimate the plant state vector. Infinite dynamic observer memory inflicts a phenomenon generally referred to as divergence. To overcome this drawback, a structure that intrinsically has finite process memory and does not need state vector integration is proposed in this paper. The observer uses a finite number of delayed input/output measurements to reconstruct the state vector with zero error being provided with a process history over its largest time-delay. This property resembles the deadbeat observer performance for discrete time systems. Moreover, the largest time-delay value puts a natural limit to the observer memory. The Luenberger observer analogy proved to be a useful tool for the observer analysis and paves the way toward observer optimization.