Kalman filter-based SMC for systems with noise and disturbances: applications to magnetic levitation system

The industrial systems usually not only suffer from unknown disturbances but also measurement noise. In sliding mode control society, to counteract the disturbances, especially mismatched disturbances, the extended state observer is usually utilised for disturbance estimation. However, fast convergence speed for the extended state observer requires high gains, which will amplify the measurement noise and cause serious chattering for the sliding mode controller. To this end, this paper proposes a novel sliding mode control scheme by utilizing the Kalman filter and an extended state observer, in which the Kalman filter serves as measurement noise filtration, and the extended state observer can on-line estimate unknown disturbances. The above two parts are interconnected since the Kalman filter needs disturbance estimation from the extended state observer, and the real measurement signal to be injected into the conventional extended state observer is replaced by its estimation value derived from the Kalman filter. Disturbance and state estimations from the extended state observer based on Kalman filter are introduced into the dynamic sliding manifold as well as the composite sliding mode controller. The proposed approach is applied in a magnetic levitation system subject to external disturbance and measurement noise for solving the position tracking problem, and simulation results verify the effectiveness and superiority of the approach.