Adaptive Control Strategies based on the Unscented Kalman Filter and Interacting Multiple Models

Abstract

In this paper, the interacting multiple model was used to design two adaptive controllers for a dynamic spacecraft system. Using knowledge of the system mode, the gains of a proportional-derivative and sliding mode controller corresponding to a nominal and a fault mode of the system were mixed to create a control input that more accurately represented the current system state. By implementing the interacting multiple model with an unscented Kalman filter, this technique was extended to a nonlinear dynamic system. The developed strategies are validated on a simulated spacecraft system and evaluated using Monte Carlo simulations by means of root mean squared errors. Results emphasize the preservation and increase in tracking performance permitted by the adaptive strategies in the presence of faults.