Moving Horizon Estimation for Linear Cascade Systems

Abstract

A structured approach to the problem of state estimation for cascaded linear sub-systems is studied in terms of minimizing a measure of the error relative to a model over a moving horizon of past system input and output observations. A quadratic programming formulation of this optimization problem is considered and two approaches are explored. One approach involves solving the Karush-Kuhn-Tucker conditions directly, and the other is based on the alternating direction method of multipliers. In both cases, the problem structure can be exploited to yield distributed computations in the following sense: Construction of the estimate for each sub-system component of the state involves information pertaining to the two immediate neighbours only. Numerical simulations based on model data from an automated irrigation channel are used to investigate and compare the computational burden of the two approaches.