PREDICTIVE FILTERING FOR NONLINEAR-SYSTEMS

A real-time predictive filter is derived for nonlinear systems. The ma jor advantage of this new filter over conventional filters is that it provides a method of determining optimal state estimates In the presen ce of significant error in the assumed (nominal) model. The new real-t ime nonlinear filter determines (predicts) the optimal model error tra jectory so that the measurement-minus-estimate covariance statisticall y matches the known measurement-minus-truth covariance. The optimal mo del error is found by using a one-time step ahead control approach. Al so, because the continuous model is used to determine state estimates, the filter avoids discrete state jumps. The predictive filter is used to estimate the position and velocity of nonlinear mass-damper-spring system. Results using this new algorithm indicate that the real-time predictive filter provides accurate estimates in the presence of highl y nonlinear dynamics and significant errors in the model parameters.