State estimation and fault detection of fractional order nonlinear systems
Midwest Symposium on Circuits and Systems
Disturbance decoupling, Extended unknown input observer, Fault detection, Fractional Kalman filter, Fractional order systems, State estimation
This study is devoted to robust state estimation and fault detection for nonlinear discrete fractional order systems using a novel fractional order filter algorithm. While noise and disturbance effects can suppress or even disturb the state estimation, the proposed filter can preciously estimate the states of nonlinear fractional order systems. Disturbance decoupling approach is the fundamental basis for the proposed filter to make it robust against unknown inputs. Simulation results illustrate the advantages of the proposed filter for state estimation and fault detection of nonlinear fractional order systems in the presence of both noise and disturbance.
Tabatabaei, Mahmood; Zarei, Jafar; Razavi-Far, Roozbeh; and Saif, Mehrdad. (2019). State estimation and fault detection of fractional order nonlinear systems. Midwest Symposium on Circuits and Systems, 2018-August, 1086-1089.