MIMO Adaptive Bank-To-Steer Control Algorithms for Guided Re-entry Vehicles

This paper proposes new Bank-to-Steer (BTS) control algorithms for controlling the attitude of an Apollo style capsule during guided re-entry phase. The fundamental design challenge is dynamic coupling between roll and yaw axes as a function of trim Angle-ofAttack (or pitch angle) which varies widely during the guided re-entry phase. To solve this problem, two novel approaches are proposed. The trim AOA is estimated in real-time using only the polarity of the commanded torque generated by the BTS control algorithms, without using air data from direct measurement systems as in Space Shuttle or estimation using onboard sensors and stored knowledge about flight mechanics as in Kistler. The coupled roll and yaw axes are controlled by an MIMO gain-scheduled state-feedback control algorithm. This algorithm is based on mature linear parameter varying (LPV) synthesis and proven pulse-width-pulse-frequency (PWPF) modulator. These algorithms have potential advantages over Apollo and Kistler BTS control algorithms: estimation of trim AOA is relatively simple and effective even in the presence of off-nominal flight conditions; the BTS control algorithm does not need the table of optimal gain sets and associated time-consuming procedure of gain design at different trim conditions because it automatically tunes the nominal gains as a function of trim AOA; the BTS control algorithm is so flexible that it can be re-used for other phases such as skip re-entry phase and attitude-to-velocity phase.