Partial-state estimation using an adaptive disturbance rejection algorithm

This paper develops an adaptive disturbance rejection framework to achieve partial state estimation. The cost of covariance propagation in the Kalman filter and the spatially local Kalman filter is prohibitive if the order of the system is large. Alternatively, the order of the adaptive controller can be chosen manually and the adaptive disturbance rejection algorithm yields better estimates in the presence of large uncertainty in the plant inputs. The state estimation using the adaptive disturbance rejection technique was demonstrated on a serially interconnected mass spring damper simulation example and its performance compared with the Kalman filter. The adaptive disturbance rejection estimator that uses the state dependent Markov parameters was then used for data assimilation in a one dimensional hydrodynamic flow example.