Discussion on: "Kalman Filter Constraint Switching for Turbofan Engine Health Estimation"

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. Recently published work has shown a new method for incorporating state variable inequality constraints in the Kalman filter. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The incorporation of state variable constraints has been shown to generally improve the filter’s estimation accuracy. However, the incorporation of inequality constraints poses some risk to the estimation accuracy. After all, the Kalman filter is theoretically optimal, so the incorporation of heuristic constraints may degrade the optimality of the filter. This paper proposes a way to switch the filter constraints so that the state estimates follow the unconstrained (theoretically optimal) filter when the confidence in the unconstrained filter is high. ∗Corresponding author. Email address [email protected]. This work was supported by the NASA Aviation Safety and Security Program at NASA Glenn Research Center.