Adaptive Estimation for Cause-Effect Control Systems with Model Parameters and Noise Perturbations

In this paper, the state estimation problem is studied for a class of discrete-time nonlinear Effect systems(E-systems) in “Cause-effect control theory”. An adaptive unscented Kalman filtering algorithm is designed for the case that the state of the E-systems undergoes sudden changes when there are uncertainties in the model parameters or noise perturbations. A fading factor is introduced into the prediction error covariance of the filter in order to weaken the influence of a priori information on the current estimate in real time, and thus improve the accuracy of the state estimation. Finally, effectiveness of the algorithm is verified by a numerical simulation example.