Sequential Estimation of States and Parameters in Noisy Nonlinear Dynamical Systems

Abstract

The problem considered is the sequential estimation of states and parameters in noisy nonlinear systems. The class of systems considered is those in which the dynamical behavior is described by an ordinary differential equation. No statistical assumptions are required concerning the nature of the unknown inputs to the system or the measurement errors on the output. For estimation purposes, a least-squares criterion is used. The new feature of the approach presented is that a sequential least-squares estimator is obtained for the class of problems considered. This estimator could be implemented in real time. Experimental results from several examples indicate that the proposed estimation scheme is feasible.