Stochastic Control and Identification Enhancement for the Flutter Suppression Problem

Abstract

The topic of this paper is the application of some recent results in stochastic control to an aerospace problem where there are large uncertainties in the dynamics of the plant to be controlled. An approximation to the stochastic Dynamic Programming is considered that results in an adaptive control of the “closed-loop” type: it utilizes feedback (latest state and parameter estimates and their uncertainties) as well as their anticipated future uncertainties - it anticipates (subject to causality) subsequent feedback. This algorithm has the feature that allows the control to enhance the parameter identification in real time. This is done using the control'invarianza dual effect: the control can affect the state as well as the (augmented) state uncertainty and thus can reduce the uncertainty about some parameters. A flight control application in which stochastic adaptive control appears to offer significant payoff is the active control of aircraft wing-store flutter. Improved flutter suppression can be accomplished with an adaptive controller that has the capability to learn and identify the flutter modes during the flight.